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https://paperswithcode.com/paper/dynamic-network-model-from-partial | 1805.10616 | null | null | Dynamic Network Model from Partial Observations | Can evolving networks be inferred and modeled without directly observing
their nodes and edges? In many applications, the edges of a dynamic network
might not be observed, but one can observe the dynamics of stochastic cascading
processes (e.g., information diffusion, virus propagation) occurring over the
unobserved ne... | null | http://arxiv.org/abs/1805.10616v4 | http://arxiv.org/pdf/1805.10616v4.pdf | NeurIPS 2018 12 | [
"Elahe Ghalebi",
"Baharan Mirzasoleiman",
"Radu Grosu",
"Jure Leskovec"
] | [
"model",
"Open-Ended Question Answering"
] | 2018-05-27T00:00:00 | http://papers.nips.cc/paper/8192-dynamic-network-model-from-partial-observations | http://papers.nips.cc/paper/8192-dynamic-network-model-from-partial-observations.pdf | dynamic-network-model-from-partial-1 | null | [
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "ooJpiued",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or ... |
https://paperswithcode.com/paper/pac-bayes-bounds-for-stable-algorithms-with | 1806.06827 | null | null | PAC-Bayes bounds for stable algorithms with instance-dependent priors | PAC-Bayes bounds have been proposed to get risk estimates based on a training
sample. In this paper the PAC-Bayes approach is combined with stability of the
hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting
is used with a Gaussian prior centered at the expected output. Thus a novelty
of our ... | null | http://arxiv.org/abs/1806.06827v2 | http://arxiv.org/pdf/1806.06827v2.pdf | NeurIPS 2018 12 | [
"Omar Rivasplata",
"Emilio Parrado-Hernandez",
"John Shawe-Taylor",
"Shiliang Sun",
"Csaba Szepesvari"
] | [] | 2018-06-18T00:00:00 | http://papers.nips.cc/paper/8134-pac-bayes-bounds-for-stable-algorithms-with-instance-dependent-priors | http://papers.nips.cc/paper/8134-pac-bayes-bounds-for-stable-algorithms-with-instance-dependent-priors.pdf | pac-bayes-bounds-for-stable-algorithms-with-1 | null | [
{
"code_snippet_url": "",
"description": "A **Support Vector Machine**, or **SVM**, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes... |
https://paperswithcode.com/paper/automated-bridge-component-recognition-using | 1806.06820 | null | null | Automated Bridge Component Recognition using Video Data | This paper investigates the automated recognition of structural bridge
components using video data. Although understanding video data for structural
inspections is straightforward for human inspectors, the implementation of the
same task using machine learning methods has not been fully realized. In
particular, single-... | null | http://arxiv.org/abs/1806.06820v2 | http://arxiv.org/pdf/1806.06820v2.pdf | null | [
"Yasutaka Narazaki",
"Vedhus Hoskere",
"Tu A. Hoang",
"Billie F. Spencer Jr"
] | [
"BIG-bench Machine Learning"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/gradient-descent-with-identity-initialization-1 | 1802.06093 | null | null | Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks | We analyze algorithms for approximating a function $f(x) = \Phi x$ mapping
$\Re^d$ to $\Re^d$ using deep linear neural networks, i.e. that learn a
function $h$ parameterized by matrices $\Theta_1,...,\Theta_L$ and defined by
$h(x) = \Theta_L \Theta_{L-1} ... \Theta_1 x$. We focus on algorithms that
learn through gradie... | null | http://arxiv.org/abs/1802.06093v4 | http://arxiv.org/pdf/1802.06093v4.pdf | ICML 2018 | [
"Peter L. Bartlett",
"David P. Helmbold",
"Philip M. Long"
] | [] | 2018-02-16T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/temporal-coherence-based-self-supervised | 1806.06811 | null | null | Temporal coherence-based self-supervised learning for laparoscopic workflow analysis | In order to provide the right type of assistance at the right time,
computer-assisted surgery systems need context awareness. To achieve this,
methods for surgical workflow analysis are crucial. Currently, convolutional
neural networks provide the best performance for video-based workflow analysis
tasks. For training s... | To achieve this, methods for surgical workflow analysis are crucial. | http://arxiv.org/abs/1806.06811v2 | http://arxiv.org/pdf/1806.06811v2.pdf | null | [
"Isabel Funke",
"Alexander Jenke",
"Sören Torge Mees",
"Jürgen Weitz",
"Stefanie Speidel",
"Sebastian Bodenstedt"
] | [
"Self-Supervised Learning",
"Surgical phase recognition"
] | 2018-06-18T00:00:00 | null | null | null | null | [
{
"code_snippet_url": null,
"description": "",
"full_name": null,
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Contrastive Learning",
"source_title": null... |
https://paperswithcode.com/paper/better-runtime-guarantees-via-stochastic | 1801.04487 | null | null | Better Runtime Guarantees Via Stochastic Domination | Apart from few exceptions, the mathematical runtime analysis of evolutionary
algorithms is mostly concerned with expected runtimes. In this work, we argue
that stochastic domination is a notion that should be used more frequently in
this area. Stochastic domination allows to formulate much more informative
performance ... | null | http://arxiv.org/abs/1801.04487v5 | http://arxiv.org/pdf/1801.04487v5.pdf | null | [
"Benjamin Doerr"
] | [
"Evolutionary Algorithms"
] | 2018-01-13T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/scaling-neural-machine-translation | 1806.00187 | null | null | Scaling Neural Machine Translation | Sequence to sequence learning models still require several days to reach
state of the art performance on large benchmark datasets using a single
machine. This paper shows that reduced precision and large batch training can
speedup training by nearly 5x on a single 8-GPU machine with careful tuning and
implementation. O... | Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. | http://arxiv.org/abs/1806.00187v3 | http://arxiv.org/pdf/1806.00187v3.pdf | WS 2018 10 | [
"Myle Ott",
"Sergey Edunov",
"David Grangier",
"Michael Auli"
] | [
"GPU",
"Machine Translation",
"Question Answering",
"Translation"
] | 2018-06-01T00:00:00 | https://aclanthology.org/W18-6301 | https://aclanthology.org/W18-6301.pdf | scaling-neural-machine-translation-1 | null | [] |
https://paperswithcode.com/paper/almost-exact-matching-with-replacement-for | 1806.06802 | null | null | Interpretable Almost Matching Exactly for Causal Inference | We aim to create the highest possible quality of treatment-control matches for categorical data in the potential outcomes framework. Matching methods are heavily used in the social sciences due to their interpretability, but most matching methods do not pass basic sanity checks: they fail when irrelevant variables are ... | Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible. | https://arxiv.org/abs/1806.06802v6 | https://arxiv.org/pdf/1806.06802v6.pdf | null | [
"Yameng Liu",
"Aw Dieng",
"Sudeepa Roy",
"Cynthia Rudin",
"Alexander Volfovsky"
] | [
"Causal Inference"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/deep-spatiotemporal-representation-of-the | 1806.06793 | null | null | Deep Spatiotemporal Representation of the Face for Automatic Pain Intensity Estimation | Automatic pain intensity assessment has a high value in disease diagnosis
applications. Inspired by the fact that many diseases and brain disorders can
interrupt normal facial expression formation, we aim to develop a computational
model for automatic pain intensity assessment from spontaneous and micro facial
variatio... | null | http://arxiv.org/abs/1806.06793v1 | http://arxiv.org/pdf/1806.06793v1.pdf | null | [
"Mohammad Tavakolian",
"Abdenour Hadid"
] | [] | 2018-06-18T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/73642d9425a358b51a683cf6f95852d06cba1096/torch/nn/modules/conv.py#L421",
"description": "A **3D Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) where the kernel slides in 3 dimensions as opposed to 2 dimen... |
https://paperswithcode.com/paper/flexible-collaborative-estimation-of-the | 1806.06784 | null | null | Robust inference on the average treatment effect using the outcome highly adaptive lasso | Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize flexible techniques such as semiparametric regression or machine learning to estimate these quantities. However, optimal estimation of these r... | null | https://arxiv.org/abs/1806.06784v3 | https://arxiv.org/pdf/1806.06784v3.pdf | null | [
"Cheng Ju",
"David Benkeser",
"Mark J. Van Der Laan"
] | [
"regression"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/consistent-individualized-feature-attribution | 1802.03888 | null | null | Consistent Individualized Feature Attribution for Tree Ensembles | A unified approach to explain the output of any machine learning model. | A unified approach to explain the output of any machine learning model. | http://arxiv.org/abs/1802.03888v3 | http://arxiv.org/pdf/1802.03888v3.pdf | null | [
"Scott M. Lundberg",
"Gabriel G. Erion",
"Su-In Lee"
] | [
"BIG-bench Machine Learning"
] | 2018-02-12T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/bingan-learning-compact-binary-descriptors | 1806.06778 | null | null | BinGAN: Learning Compact Binary Descriptors with a Regularized GAN | In this paper, we propose a novel regularization method for Generative
Adversarial Networks, which allows the model to learn discriminative yet
compact binary representations of image patches (image descriptors). We employ
the dimensionality reduction that takes place in the intermediate layers of the
discriminator net... | In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of image patches (image descriptors). | http://arxiv.org/abs/1806.06778v5 | http://arxiv.org/pdf/1806.06778v5.pdf | NeurIPS 2018 12 | [
"Maciej Zieba",
"Piotr Semberecki",
"Tarek El-Gaaly",
"Tomasz Trzcinski"
] | [
"Dimensionality Reduction",
"Retrieval"
] | 2018-06-18T00:00:00 | http://papers.nips.cc/paper/7619-bingan-learning-compact-binary-descriptors-with-a-regularized-gan | http://papers.nips.cc/paper/7619-bingan-learning-compact-binary-descriptors-with-a-regularized-gan.pdf | bingan-learning-compact-binary-descriptors-1 | null | [
{
"code_snippet_url": null,
"description": "Need help with a Lufthansa Airlines reservation, cancellation, or flight change? Speaking directly with a live Lufthansa agent at ☎️1→(855)*(200)→(2631) [US/OTA] (Live Person) who can save your time, eliminate confusion, and ensure your travel needs are met quickl... |
https://paperswithcode.com/paper/multifit-a-multivariate-multiscale-framework | 1806.06777 | null | null | Multiscale Fisher's Independence Test for Multivariate Dependence | Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them to massive data. Moreover, resampling i... | Identifying dependency in multivariate data is a common inference task that arises in numerous applications. | https://arxiv.org/abs/1806.06777v7 | https://arxiv.org/pdf/1806.06777v7.pdf | null | [
"Shai Gorsky",
"Li Ma"
] | [] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/kernel-based-outlier-detection-using-the | 1806.06775 | null | null | Kernel-based Outlier Detection using the Inverse Christoffel Function | Outlier detection methods have become increasingly relevant in recent years
due to increased security concerns and because of its vast application to
different fields. Recently, Pauwels and Lasserre (2016) noticed that the
sublevel sets of the inverse Christoffel function accurately depict the shape
of a cloud of data ... | null | http://arxiv.org/abs/1806.06775v1 | http://arxiv.org/pdf/1806.06775v1.pdf | null | [
"Armin Askari",
"Forest Yang",
"Laurent El Ghaoui"
] | [
"Outlier Detection"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/kid-net-convolution-networks-for-kidney | 1806.06769 | null | null | Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes | Semantic image segmentation plays an important role in modeling
patient-specific anatomy. We propose a convolution neural network, called
Kid-Net, along with a training schema to segment kidney vessels: artery, vein
and collecting system. Such segmentation is vital during the surgical planning
phase in which medical de... | null | http://arxiv.org/abs/1806.06769v1 | http://arxiv.org/pdf/1806.06769v1.pdf | null | [
"Ahmed Taha",
"Pechin Lo",
"Junning Li",
"Tao Zhao"
] | [
"Anatomy",
"Image Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-18T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a... |
https://paperswithcode.com/paper/modularity-matters-learning-invariant | 1806.06765 | null | null | Modularity Matters: Learning Invariant Relational Reasoning Tasks | We focus on two supervised visual reasoning tasks whose labels encode a
semantic relational rule between two or more objects in an image: the MNIST
Parity task and the colorized Pentomino task. The objects in the images undergo
random translation, scaling, rotation and coloring transformations. Thus these
tasks involve... | null | http://arxiv.org/abs/1806.06765v1 | http://arxiv.org/pdf/1806.06765v1.pdf | null | [
"Jason Jo",
"Vikas Verma",
"Yoshua Bengio"
] | [
"Mixture-of-Experts",
"Relational Reasoning",
"Visual Reasoning"
] | 2018-06-18T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - me... |
https://paperswithcode.com/paper/closing-the-generalization-gap-of-adaptive | 1806.06763 | null | null | Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks | Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the gene... | Experiments on standard benchmarks show that our proposed algorithm can maintain a fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. | https://arxiv.org/abs/1806.06763v3 | https://arxiv.org/pdf/1806.06763v3.pdf | null | [
"Jinghui Chen",
"Dongruo Zhou",
"Yiqi Tang",
"Ziyan Yang",
"Yuan Cao",
"Quanquan Gu"
] | [] | 2018-06-18T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "https://github.com/paultsw/nice_pytorch/blob/15cfc543fc3dc81ee70398b8dfc37b67269ede95/nice/layers.py#L109",
"description": "**Affine Coupling** is a method for implementing a normalizing flow (where we stack a sequence of invertible bijective transformation functions). Affine coupling... |
https://paperswithcode.com/paper/a-memory-network-approach-for-story-based | 1805.02838 | null | null | A Memory Network Approach for Story-based Temporal Summarization of 360° Videos | We address the problem of story-based temporal summarization of long
360{\deg} videos. We propose a novel memory network model named Past-Future
Memory Network (PFMN), in which we first compute the scores of 81 normal field
of view (NFOV) region proposals cropped from the input 360{\deg} video, and
then recover a laten... | null | http://arxiv.org/abs/1805.02838v3 | http://arxiv.org/pdf/1805.02838v3.pdf | CVPR 2018 | [
"Sang-ho Lee",
"Jinyoung Sung",
"Youngjae Yu",
"Gunhee Kim"
] | [
"Video Summarization"
] | 2018-05-08T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "https://github.com/aykutaaykut/Memory-Networks",
"description": "A **Memory Network** provides a memory component that can be read from and written to with the inference capabilities of a neural network model. The motivation is that many neural networks lack a long-term memory compone... |
https://paperswithcode.com/paper/pots-protective-optimization-technologies | 1806.02711 | null | null | POTs: Protective Optimization Technologies | Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trust... | Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. | https://arxiv.org/abs/1806.02711v6 | https://arxiv.org/pdf/1806.02711v6.pdf | null | [
"Bogdan Kulynych",
"Rebekah Overdorf",
"Carmela Troncoso",
"Seda Gürses"
] | [
"Decision Making",
"Fairness"
] | 2018-06-07T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/surface-networks | 1705.10819 | null | null | Surface Networks | We study data-driven representations for three-dimensional triangle meshes,
which are one of the prevalent objects used to represent 3D geometry. Recent
works have developed models that exploit the intrinsic geometry of manifolds
and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants,
which learn... | We study data-driven representations for three-dimensional triangle meshes, which are one of the prevalent objects used to represent 3D geometry. | http://arxiv.org/abs/1705.10819v2 | http://arxiv.org/pdf/1705.10819v2.pdf | CVPR 2018 6 | [
"Ilya Kostrikov",
"Zhongshi Jiang",
"Daniele Panozzo",
"Denis Zorin",
"Joan Bruna"
] | [
"3D geometry"
] | 2017-05-30T00:00:00 | http://openaccess.thecvf.com/content_cvpr_2018/html/Kostrikov_Surface_Networks_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kostrikov_Surface_Networks_CVPR_2018_paper.pdf | surface-networks-1 | null | [
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing wit... |
https://paperswithcode.com/paper/extracting-automata-from-recurrent-neural | 1711.09576 | null | null | Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples | We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin's L* algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained... | We do this using Angluin's L* algorithm as a learner and the trained RNN as an oracle. | https://arxiv.org/abs/1711.09576v4 | https://arxiv.org/pdf/1711.09576v4.pdf | ICML 2018 7 | [
"Gail Weiss",
"Yoav Goldberg",
"Eran Yahav"
] | [] | 2017-11-27T00:00:00 | https://icml.cc/Conferences/2018/Schedule?showEvent=2276 | http://proceedings.mlr.press/v80/weiss18a/weiss18a.pdf | extracting-automata-from-recurrent-neural-1 | null | [] |
https://paperswithcode.com/paper/unsupervised-word-segmentation-from-speech | 1806.06734 | null | null | Unsupervised Word Segmentation from Speech with Attention | We present a first attempt to perform attentional word segmentation directly
from the speech signal, with the final goal to automatically identify lexical
units in a low-resource, unwritten language (UL). Our methodology assumes a
pairing between recordings in the UL with translations in a well-resourced
language. It u... | null | http://arxiv.org/abs/1806.06734v1 | http://arxiv.org/pdf/1806.06734v1.pdf | null | [
"Pierre Godard",
"Marcely Zanon-Boito",
"Lucas Ondel",
"Alexandre Berard",
"François Yvon",
"Aline Villavicencio",
"Laurent Besacier"
] | [
"Acoustic Unit Discovery",
"Machine Translation",
"Segmentation",
"Translation"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/semantically-selective-augmentation-for-deep | 1806.04074 | null | null | Semantically Selective Augmentation for Deep Compact Person Re-Identification | We present a deep person re-identification approach that combines
semantically selective, deep data augmentation with clustering-based network
compression to generate high performance, light and fast inference networks. In
particular, we propose to augment limited training data via sampling from a
deep convolutional ge... | null | http://arxiv.org/abs/1806.04074v3 | http://arxiv.org/pdf/1806.04074v3.pdf | null | [
"Víctor Ponce-López",
"Tilo Burghardt",
"Sion Hannunna",
"Dima Damen",
"Alessandro Masullo",
"Majid Mirmehdi"
] | [
"Clustering",
"Data Augmentation",
"Generative Adversarial Network",
"Person Re-Identification",
"Specificity"
] | 2018-06-11T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/assessing-robustness-of-radiomic-features-by | 1806.06719 | null | null | Assessing robustness of radiomic features by image perturbation | Image features need to be robust against differences in positioning,
acquisition and segmentation to ensure reproducibility. Radiomic models that
only include robust features can be used to analyse new images, whereas models
with non-robust features may fail to predict the outcome of interest
accurately. Test-retest im... | null | http://arxiv.org/abs/1806.06719v1 | http://arxiv.org/pdf/1806.06719v1.pdf | null | [
"Alex Zwanenburg",
"Stefan Leger",
"Linda Agolli",
"Karoline Pilz",
"Esther G. C. Troost",
"Christian Richter",
"Steffen Löck"
] | [
"Translation"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/reconvnet-video-object-segmentation-with | 1806.05510 | null | null | ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation | We introduce ReConvNet, a recurrent convolutional architecture for
semi-supervised video object segmentation that is able to fast adapt its
features to focus on any specific object of interest at inference time.
Generalization to new objects never observed during training is known to be a
hard task for supervised appro... | null | http://arxiv.org/abs/1806.05510v2 | http://arxiv.org/pdf/1806.05510v2.pdf | null | [
"Francesco Lattari",
"Marco Ciccone",
"Matteo Matteucci",
"Jonathan Masci",
"Francesco Visin"
] | [
"Object",
"Position",
"Semantic Segmentation",
"Semi-Supervised Video Object Segmentation",
"Video Object Segmentation",
"Video Semantic Segmentation"
] | 2018-06-14T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/tree-edit-distance-learning-via-adaptive-1 | 1806.05009 | null | null | Tree Edit Distance Learning via Adaptive Symbol Embeddings | Metric learning has the aim to improve classification accuracy by learning a
distance measure which brings data points from the same class closer together
and pushes data points from different classes further apart. Recent research
has demonstrated that metric learning approaches can also be applied to trees,
such as m... | null | http://arxiv.org/abs/1806.05009v3 | http://arxiv.org/pdf/1806.05009v3.pdf | ICML 2018 7 | [
"Benjamin Paaßen",
"Claudio Gallicchio",
"Alessio Micheli",
"Barbara Hammer"
] | [
"Metric Learning"
] | 2018-06-13T00:00:00 | https://icml.cc/Conferences/2018/Schedule?showEvent=2180 | http://proceedings.mlr.press/v80/paassen18a/paassen18a.pdf | tree-edit-distance-learning-via-adaptive-2 | null | [] |
https://paperswithcode.com/paper/towards-multi-instrument-drum-transcription | 1806.06676 | null | null | Towards multi-instrument drum transcription | Automatic drum transcription, a subtask of the more general automatic music
transcription, deals with extracting drum instrument note onsets from an audio
source. Recently, progress in transcription performance has been made using
non-negative matrix factorization as well as deep learning methods. However,
these works ... | In this work, convolutional and convolutional recurrent neural networks are trained to transcribe a wider range of drum instruments. | http://arxiv.org/abs/1806.06676v2 | http://arxiv.org/pdf/1806.06676v2.pdf | null | [
"Richard Vogl",
"Gerhard Widmer",
"Peter Knees"
] | [
"Drum Transcription",
"Music Transcription"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/subword-and-crossword-units-for-ctc-acoustic | 1712.06855 | null | null | Subword and Crossword Units for CTC Acoustic Models | This paper proposes a novel approach to create an unit set for CTC based
speech recognition systems. By using Byte Pair Encoding we learn an unit set of
an arbitrary size on a given training text. In contrast to using characters or
words as units this allows us to find a good trade-off between the size of our
unit set ... | null | http://arxiv.org/abs/1712.06855v2 | http://arxiv.org/pdf/1712.06855v2.pdf | null | [
"Thomas Zenkel",
"Ramon Sanabria",
"Florian Metze",
"Alex Waibel"
] | [
"Language Modeling",
"Language Modelling",
"speech-recognition",
"Speech Recognition"
] | 2017-12-19T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/cardinality-leap-for-open-ended-evolution | 1806.06628 | null | null | Cardinality Leap for Open-Ended Evolution: Theoretical Consideration and Demonstration by "Hash Chemistry" | Open-ended evolution requires unbounded possibilities that evolving entities
can explore. The cardinality of a set of those possibilities thus has a
significant implication for the open-endedness of evolution. We propose that
facilitating formation of higher-order entities is a generalizable, effective
way to cause a "... | null | http://arxiv.org/abs/1806.06628v4 | http://arxiv.org/pdf/1806.06628v4.pdf | null | [
"Hiroki Sayama"
] | [] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/warp-wavelets-with-adaptive-recursive | 1711.00789 | null | null | Learning Asymmetric and Local Features in Multi-Dimensional Data through Wavelets with Recursive Partitioning | Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images. It requires methods that are sensitive to local details while fast enough to han... | Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images. | https://arxiv.org/abs/1711.00789v5 | https://arxiv.org/pdf/1711.00789v5.pdf | null | [
"Meng Li",
"Li Ma"
] | [
"Bayesian Inference",
"Image Reconstruction"
] | 2017-11-02T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/on-enhancing-speech-emotion-recognition-using | 1806.06626 | null | null | On Enhancing Speech Emotion Recognition using Generative Adversarial Networks | Generative Adversarial Networks (GANs) have gained a lot of attention from
machine learning community due to their ability to learn and mimic an input
data distribution. GANs consist of a discriminator and a generator working in
tandem playing a min-max game to learn a target underlying data distribution;
when fed with... | null | http://arxiv.org/abs/1806.06626v1 | http://arxiv.org/pdf/1806.06626v1.pdf | null | [
"Saurabh Sahu",
"Rahul Gupta",
"Carol Espy-Wilson"
] | [
"Cross-corpus",
"Emotion Recognition",
"Speech Emotion Recognition"
] | 2018-06-18T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a... |
https://paperswithcode.com/paper/banach-wasserstein-gan | 1806.06621 | null | null | Banach Wasserstein GAN | Wasserstein Generative Adversarial Networks (WGANs) can be used to generate
realistic samples from complicated image distributions. The Wasserstein metric
used in WGANs is based on a notion of distance between individual images, which
induces a notion of distance between probability distributions of images. So
far the ... | Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. | http://arxiv.org/abs/1806.06621v2 | http://arxiv.org/pdf/1806.06621v2.pdf | NeurIPS 2018 12 | [
"Jonas Adler",
"Sebastian Lunz"
] | [] | 2018-06-18T00:00:00 | http://papers.nips.cc/paper/7909-banach-wasserstein-gan | http://papers.nips.cc/paper/7909-banach-wasserstein-gan.pdf | banach-wasserstein-gan-1 | null | [
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a... |
https://paperswithcode.com/paper/comparison-based-random-forests | 1806.06616 | null | null | Comparison-Based Random Forests | Assume we are given a set of items from a general metric space, but we
neither have access to the representation of the data nor to the distances
between data points. Instead, suppose that we can actively choose a triplet of
items (A,B,C) and ask an oracle whether item A is closer to item B or to item
C. In this paper,... | null | http://arxiv.org/abs/1806.06616v1 | http://arxiv.org/pdf/1806.06616v1.pdf | ICML 2018 7 | [
"Siavash Haghiri",
"Damien Garreau",
"Ulrike Von Luxburg"
] | [
"General Classification",
"regression",
"Triplet"
] | 2018-06-18T00:00:00 | https://icml.cc/Conferences/2018/Schedule?showEvent=1979 | http://proceedings.mlr.press/v80/haghiri18a/haghiri18a.pdf | comparison-based-random-forests-1 | null | [] |
https://paperswithcode.com/paper/on-multi-resident-activity-recognition-in | 1806.06611 | null | null | On Multi-resident Activity Recognition in Ambient Smart-Homes | Increasing attention to the research on activity monitoring in smart homes
has motivated the employment of ambient intelligence to reduce the deployment
cost and solve the privacy issue. Several approaches have been proposed for
multi-resident activity recognition, however, there still lacks a comprehensive
benchmark f... | null | http://arxiv.org/abs/1806.06611v1 | http://arxiv.org/pdf/1806.06611v1.pdf | null | [
"Son N. Tran",
"Qing Zhang",
"Mohan Karunanithi"
] | [
"Activity Recognition"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/evaluating-and-characterizing-incremental | 1806.06610 | null | null | Evaluating and Characterizing Incremental Learning from Non-Stationary Data | Incremental learning from non-stationary data poses special challenges to the
field of machine learning. Although new algorithms have been developed for
this, assessment of results and comparison of behaviors are still open
problems, mainly because evaluation metrics, adapted from more traditional
tasks, can be ineffec... | null | http://arxiv.org/abs/1806.06610v1 | http://arxiv.org/pdf/1806.06610v1.pdf | null | [
"Alejandro Cervantes",
"Christian Gagné",
"Pedro Isasi",
"Marc Parizeau"
] | [
"Incremental Learning"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/quantized-compressive-k-means | 1804.10109 | null | null | Quantized Compressive K-Means | The recent framework of compressive statistical learning aims at designing
tractable learning algorithms that use only a heavily compressed
representation-or sketch-of massive datasets. Compressive K-Means (CKM) is such
a method: it estimates the centroids of data clusters from pooled, non-linear,
random signatures of ... | null | http://arxiv.org/abs/1804.10109v2 | http://arxiv.org/pdf/1804.10109v2.pdf | null | [
"Vincent Schellekens",
"Laurent Jacques"
] | [
"Clustering",
"Quantization"
] | 2018-04-26T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/self-attentional-acoustic-models | 1803.09519 | null | null | Self-Attentional Acoustic Models | Self-attention is a method of encoding sequences of vectors by relating these
vectors to each-other based on pairwise similarities. These models have
recently shown promising results for modeling discrete sequences, but they are
non-trivial to apply to acoustic modeling due to computational and modeling
issues. In this... | Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities. | http://arxiv.org/abs/1803.09519v2 | http://arxiv.org/pdf/1803.09519v2.pdf | null | [
"Matthias Sperber",
"Jan Niehues",
"Graham Neubig",
"Sebastian Stüker",
"Alex Waibel"
] | [] | 2018-03-26T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/snap-ml-a-hierarchical-framework-for-machine | 1803.06333 | null | null | Snap ML: A Hierarchical Framework for Machine Learning | We describe a new software framework for fast training of generalized linear
models. The framework, named Snap Machine Learning (Snap ML), combines recent
advances in machine learning systems and algorithms in a nested manner to
reflect the hierarchical architecture of modern computing systems. We prove
theoretically t... | null | http://arxiv.org/abs/1803.06333v3 | http://arxiv.org/pdf/1803.06333v3.pdf | NeurIPS 2018 12 | [
"Celestine Dünner",
"Thomas Parnell",
"Dimitrios Sarigiannis",
"Nikolas Ioannou",
"Andreea Anghel",
"Gummadi Ravi",
"Madhusudanan Kandasamy",
"Haralampos Pozidis"
] | [
"BIG-bench Machine Learning",
"GPU"
] | 2018-03-16T00:00:00 | http://papers.nips.cc/paper/7309-snap-ml-a-hierarchical-framework-for-machine-learning | http://papers.nips.cc/paper/7309-snap-ml-a-hierarchical-framework-for-machine-learning.pdf | snap-ml-a-hierarchical-framework-for-machine-1 | null | [
{
"code_snippet_url": null,
"description": "**Logistic Regression**, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, th... |
https://paperswithcode.com/paper/multilingual-bottleneck-features-for-subword | 1803.08863 | null | null | Multilingual bottleneck features for subword modeling in zero-resource languages | How can we effectively develop speech technology for languages where no
transcribed data is available? Many existing approaches use no annotated
resources at all, yet it makes sense to leverage information from large
annotated corpora in other languages, for example in the form of multilingual
bottleneck features (BNFs... | How can we effectively develop speech technology for languages where no transcribed data is available? | http://arxiv.org/abs/1803.08863v2 | http://arxiv.org/pdf/1803.08863v2.pdf | null | [
"Enno Hermann",
"Sharon Goldwater"
] | [
"speech-recognition",
"Speech Recognition"
] | 2018-03-23T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/learning-to-write-stylized-chinese-characters | 1712.06424 | null | null | Learning to Write Stylized Chinese Characters by Reading a Handful of Examples | Automatically writing stylized Chinese characters is an attractive yet
challenging task due to its wide applicabilities. In this paper, we propose a
novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly
generate Chinese characters. Specifically, we propose to capture the different
characterist... | null | http://arxiv.org/abs/1712.06424v3 | http://arxiv.org/pdf/1712.06424v3.pdf | null | [
"Danyang Sun",
"Tongzheng Ren",
"Chongxun Li",
"Hang Su",
"Jun Zhu"
] | [] | 2017-12-06T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/ipose-instance-aware-6d-pose-estimation-of | 1712.01924 | null | null | iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects | We address the task of 6D pose estimation of known rigid objects from single
input images in scenarios where the objects are partly occluded. Recent
RGB-D-based methods are robust to moderate degrees of occlusion. For RGB
inputs, no previous method works well for partly occluded objects. Our main
contribution is to pre... | null | http://arxiv.org/abs/1712.01924v3 | http://arxiv.org/pdf/1712.01924v3.pdf | null | [
"Omid Hosseini Jafari",
"Siva Karthik Mustikovela",
"Karl Pertsch",
"Eric Brachmann",
"Carsten Rother"
] | [
"6D Pose Estimation",
"6D Pose Estimation using RGB",
"Decoder",
"Instance Segmentation",
"Object",
"Pose Estimation",
"Semantic Segmentation"
] | 2017-12-05T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/uncertainty-in-multitask-learning-joint | 1806.06595 | null | null | Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning | Multi-task neural network architectures provide a mechanism that jointly
integrates information from distinct sources. It is ideal in the context of
MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT)
scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic
multi-task networ... | null | http://arxiv.org/abs/1806.06595v1 | http://arxiv.org/pdf/1806.06595v1.pdf | null | [
"Felix J. S. Bragman",
"Ryutaro Tanno",
"Zach Eaton-Rosen",
"Wenqi Li",
"David J. Hawkes",
"Sebastien Ourselin",
"Daniel C. Alexander",
"Jamie R. McClelland",
"M. Jorge Cardoso"
] | [
"Bayesian Inference"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/deep-recurrent-neural-network-for-multi | 1806.06594 | null | null | Deep Recurrent Neural Network for Multi-target Filtering | This paper addresses the problem of fixed motion and measurement models for
multi-target filtering using an adaptive learning framework. This is performed
by defining target tuples with random finite set terminology and utilisation of
recurrent neural networks with a long short-term memory architecture. A novel
data as... | null | http://arxiv.org/abs/1806.06594v2 | http://arxiv.org/pdf/1806.06594v2.pdf | null | [
"Mehryar Emambakhsh",
"Alessandro Bay",
"Eduard Vazquez"
] | [] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/low-resource-speech-to-text-translation | 1803.09164 | null | null | Low-Resource Speech-to-Text Translation | Speech-to-text translation has many potential applications for low-resource
languages, but the typical approach of cascading speech recognition with
machine translation is often impossible, since the transcripts needed to train
a speech recognizer are usually not available for low-resource languages.
Recent work has fo... | null | http://arxiv.org/abs/1803.09164v2 | http://arxiv.org/pdf/1803.09164v2.pdf | null | [
"Sameer Bansal",
"Herman Kamper",
"Karen Livescu",
"Adam Lopez",
"Sharon Goldwater"
] | [
"Decoder",
"Machine Translation",
"speech-recognition",
"Speech Recognition",
"Speech-to-Text",
"Speech-to-Text Translation",
"Translation"
] | 2018-03-24T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/computational-theories-of-curiosity-driven | 1802.10546 | null | null | Computational Theories of Curiosity-Driven Learning | What are the functions of curiosity? What are the mechanisms of
curiosity-driven learning? We approach these questions about the living using
concepts and tools from machine learning and developmental robotics. We argue
that curiosity-driven learning enables organisms to make discoveries to solve
complex problems with ... | null | http://arxiv.org/abs/1802.10546v2 | http://arxiv.org/pdf/1802.10546v2.pdf | null | [
"Pierre-Yves Oudeyer"
] | [
"BIG-bench Machine Learning",
"Lifelong learning"
] | 2018-02-28T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/nonparametric-topic-modeling-with-neural | 1806.06583 | null | null | Nonparametric Topic Modeling with Neural Inference | This work focuses on combining nonparametric topic models with Auto-Encoding
Variational Bayes (AEVB). Specifically, we first propose iTM-VAE, where the
topics are treated as trainable parameters and the document-specific topic
proportions are obtained by a stick-breaking construction. The inference of
iTM-VAE is model... | null | http://arxiv.org/abs/1806.06583v1 | http://arxiv.org/pdf/1806.06583v1.pdf | null | [
"Xuefei Ning",
"Yin Zheng",
"Zhuxi Jiang",
"Yu Wang",
"Huazhong Yang",
"Junzhou Huang"
] | [
"Topic Models"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/wsd-algorithm-based-on-a-new-method-of-vector | 1805.09559 | null | null | WSD algorithm based on a new method of vector-word contexts proximity calculation via epsilon-filtration | The problem of word sense disambiguation (WSD) is considered in the article.
Given a set of synonyms (synsets) and sentences with these synonyms. It is
necessary to select the meaning of the word in the sentence automatically. 1285
sentences were tagged by experts, namely, one of the dictionary meanings was
selected by... | It is necessary to select the meaning of the word in the sentence automatically. | http://arxiv.org/abs/1805.09559v2 | http://arxiv.org/pdf/1805.09559v2.pdf | null | [
"Alexander Kirillov",
"Natalia Krizhanovsky",
"Andrew Krizhanovsky"
] | [
"Sentence",
"Word Sense Disambiguation"
] | 2018-05-24T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/the-kanerva-machine-a-generative-distributed | 1804.01756 | null | S1HlA-ZAZ | The Kanerva Machine: A Generative Distributed Memory | We present an end-to-end trained memory system that quickly adapts to new
data and generates samples like them. Inspired by Kanerva's sparse distributed
memory, it has a robust distributed reading and writing mechanism. The memory
is analytically tractable, which enables optimal on-line compression via a
Bayesian updat... | null | http://arxiv.org/abs/1804.01756v3 | http://arxiv.org/pdf/1804.01756v3.pdf | ICLR 2018 1 | [
"Yan Wu",
"Greg Wayne",
"Alex Graves",
"Timothy Lillicrap"
] | [] | 2018-04-05T00:00:00 | https://openreview.net/forum?id=S1HlA-ZAZ | https://openreview.net/pdf?id=S1HlA-ZAZ | the-kanerva-machine-a-generative-distributed-1 | null | [] |
https://paperswithcode.com/paper/rendernet-a-deep-convolutional-network-for | 1806.06575 | null | null | RenderNet: A deep convolutional network for differentiable rendering from 3D shapes | Traditional computer graphics rendering pipeline is designed for procedurally
generating 2D quality images from 3D shapes with high performance. The
non-differentiability due to discrete operations such as visibility computation
makes it hard to explicitly correlate rendering parameters and the resulting
image, posing ... | We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes. | http://arxiv.org/abs/1806.06575v3 | http://arxiv.org/pdf/1806.06575v3.pdf | NeurIPS 2018 12 | [
"Thu Nguyen-Phuoc",
"Chuan Li",
"Stephen Balaban",
"Yong-Liang Yang"
] | [
"Inverse Rendering"
] | 2018-06-18T00:00:00 | http://papers.nips.cc/paper/8014-rendernet-a-deep-convolutional-network-for-differentiable-rendering-from-3d-shapes | http://papers.nips.cc/paper/8014-rendernet-a-deep-convolutional-network-for-differentiable-rendering-from-3d-shapes.pdf | rendernet-a-deep-convolutional-network-for-1 | null | [] |
https://paperswithcode.com/paper/distributed-learning-with-compressed | 1806.06573 | null | null | Distributed learning with compressed gradients | Asynchronous computation and gradient compression have emerged as two key
techniques for achieving scalability in distributed optimization for
large-scale machine learning. This paper presents a unified analysis framework
for distributed gradient methods operating with staled and compressed
gradients. Non-asymptotic bo... | null | http://arxiv.org/abs/1806.06573v2 | http://arxiv.org/pdf/1806.06573v2.pdf | null | [
"Sarit Khirirat",
"Hamid Reza Feyzmahdavian",
"Mikael Johansson"
] | [
"BIG-bench Machine Learning",
"Distributed Optimization"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/subgram-extending-skip-gram-word | 1806.06571 | null | null | SubGram: Extending Skip-gram Word Representation with Substrings | Skip-gram (word2vec) is a recent method for creating vector representations
of words ("distributed word representations") using a neural network. The
representation gained popularity in various areas of natural language
processing, because it seems to capture syntactic and semantic information
about words without any e... | Skip-gram (word2vec) is a recent method for creating vector representations of words ("distributed word representations") using a neural network. | http://arxiv.org/abs/1806.06571v1 | http://arxiv.org/pdf/1806.06571v1.pdf | null | [
"Tom Kocmi",
"Ondřej Bojar"
] | [] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/learning-from-outside-the-viability-kernel | 1806.06569 | null | null | Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace | Despite impressive results using reinforcement learning to solve complex
problems from scratch, in robotics this has still been largely limited to
model-based learning with very informative reward functions. One of the major
challenges is that the reward landscape often has large patches with no
gradient, making it dif... | null | http://arxiv.org/abs/1806.06569v1 | http://arxiv.org/pdf/1806.06569v1.pdf | null | [
"Steve Heim",
"Alexander Spröwitz"
] | [
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/ista-net-interpretable-optimization-inspired | 1706.07929 | null | null | ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing | With the aim of developing a fast yet accurate algorithm for compressive
sensing (CS) reconstruction of natural images, we combine in this paper the
merits of two existing categories of CS methods: the structure insights of
traditional optimization-based methods and the speed of recent network-based
ones. Specifically,... | With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones. | http://arxiv.org/abs/1706.07929v2 | http://arxiv.org/pdf/1706.07929v2.pdf | CVPR 2018 6 | [
"Jian Zhang",
"Bernard Ghanem"
] | [
"Compressive Sensing"
] | 2017-06-24T00:00:00 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_ISTA-Net_Interpretable_Optimization-Inspired_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_ISTA-Net_Interpretable_Optimization-Inspired_CVPR_2018_paper.pdf | ista-net-interpretable-optimization-inspired-1 | null | [
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key com... |
https://paperswithcode.com/paper/state-gradients-for-rnn-memory-analysis | 1805.04264 | null | null | State Gradients for RNN Memory Analysis | We present a framework for analyzing what the state in RNNs remembers from
its input embeddings. Our approach is inspired by backpropagation, in the sense
that we compute the gradients of the states with respect to the input
embeddings. The gradient matrix is decomposed with Singular Value Decomposition
to analyze whic... | null | http://arxiv.org/abs/1805.04264v2 | http://arxiv.org/pdf/1805.04264v2.pdf | WS 2018 11 | [
"Lyan Verwimp",
"Hugo Van hamme",
"Vincent Renkens",
"Patrick Wambacq"
] | [] | 2018-05-11T00:00:00 | https://aclanthology.org/W18-5443 | https://aclanthology.org/W18-5443.pdf | state-gradients-for-rnn-memory-analysis-1 | null | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\... |
https://paperswithcode.com/paper/convex-optimization-with-unbounded-nonconvex | 1711.02621 | null | null | Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing | We consider the problem of minimizing a convex objective function $F$ when
one can only evaluate its noisy approximation $\hat{F}$. Unless one assumes
some structure on the noise, $\hat{F}$ may be an arbitrary nonconvex function,
making the task of minimizing $F$ intractable. To overcome this, prior work has
often focu... | null | http://arxiv.org/abs/1711.02621v2 | http://arxiv.org/pdf/1711.02621v2.pdf | null | [
"Oren Mangoubi",
"Nisheeth K. Vishnoi"
] | [] | 2017-11-07T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/incremental-sparse-bayesian-ordinal | 1806.06553 | null | null | Incremental Sparse Bayesian Ordinal Regression | Ordinal Regression (OR) aims to model the ordering information between
different data categories, which is a crucial topic in multi-label learning. An
important class of approaches to OR models the problem as a linear combination
of basis functions that map features to a high dimensional non-linear space.
However, most... | Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. | http://arxiv.org/abs/1806.06553v1 | http://arxiv.org/pdf/1806.06553v1.pdf | null | [
"Chang Li",
"Maarten de Rijke"
] | [
"Multi-Label Learning",
"regression"
] | 2018-06-18T00:00:00 | null | null | null | null | [] |
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