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Title: Achieving Small-Batch Accuracy with Large-Batch Scalability via Adaptive Learning Rate Adjustment. Abstract: We consider synchronous data-parallel neural network training with fixed large batch sizes. While the large batch size provides a high degree of parallelism, it likely degrades the generalization performa... | 2withdrawn |
Title: Conditioning Trick for Training Stable GANs. Abstract: In this paper we propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training. We force the generator to get closer to the departure from normality function o... | 2withdrawn |
Title: Object detection deep learning networks for Optical Character Recognition. Abstract: In this article, we show how we applied a simple approach coming from deep learning networks for object detection to the task of optical character recognition in order to build image features taylored for documents. In contrast ... | 0reject |
Title: StARformer: Transformer with State-Action-Reward Representations. Abstract: Reinforcement Learning (RL) can be considered as a sequence modeling task, i.e., given a sequence of past state-action-reward experiences, a model autoregressively predicts a sequence of future actions. Recently, Transformers have been s... | 2withdrawn |
Title: PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction. Abstract: Improving the robustness of neural nets in regression tasks is key to their application in multiple domains. Deep learning-based approaches aim to achieve this goal either by improving their prediction of specific val... | 0reject |
Title: On Hard Episodes in Meta-Learning. Abstract: Existing meta-learners primarily focus on improving the average task accuracy across multiple episodes. Different episodes, however, may vary in hardness and quality leading to a wide gap in the meta-learner's performance across episodes. Understanding this issue is p... | 0reject |
Title: Neural ODE Processes. Abstract: Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt... | 1accept |
Title: GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders. Abstract: Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative models for images... | 0reject |
Title: Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference. Abstract: Computations for the softmax function in neural network models are expensive when the number of output classes is large. This can become a significant issue in both training and inference for such models. In this paper, we... | 0reject |
Title: Now I Remember! Episodic Memory For Reinforcement Learning. Abstract: Humans rely on episodic memory constantly, in remembering the name of someone they met 10 minutes ago, the plot of a movie as it unfolds, or where they parked the car. Endowing reinforcement learning agents with episodic memory is a key step o... | 0reject |
Title: Probabilistic Multimodal Representation Learning. Abstract: Learning multimodal representations is a requirement for many tasks such as image--caption retrieval. Previous work on this problem has only focused on finding good vector representations without any explicit measure of uncertainty. In this work, we arg... | 2withdrawn |
Title: Factorized Multimodal Transformer for Multimodal Sequential Learning. Abstract: The complex world around us is inherently multimodal and sequential (continuous). Information is scattered across different modalities and requires multiple continuous sensors to be captured. As machine learning leaps towards better ... | 2withdrawn |
Title: Everybody's Talkin': Let Me Talk as You Want. Abstract: We present a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video. This method is unique because it is highly dynamic. It does not assume a person-specific rendering network yet capable of tr... | 2withdrawn |
Title: Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling. Abstract: Universal user representation is an important research topic in industry, and is widely used in diverse downstream user analysis tasks, such as user profiling and user preference prediction. With the rapid developm... | 0reject |
Title: Improving Local Effectiveness for Global Robustness Training. Abstract: Despite its increasing popularity, deep neural networks are easily fooled. To alleviate this deficiency, researchers are actively developing new training strategies, which encourage models that are robust to small input perturbations. Severa... | 0reject |
Title: Data augmentation for deep learning based accelerated MRI reconstruction. Abstract: Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an image from a noisy or corrupted measurement of that... | 0reject |
Title: GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images. Abstract: We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, except for it being zero-mean, additive, and... | 1accept |
Title: Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling. Abstract: We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as ... | 0reject |
Title: Quadrature-based features for kernel approximation. Abstract: We consider the problem of improving kernel approximation via feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. We propose to use more efficient... | 0reject |
Title: Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks. Abstract: We study the dynamics of a neural network in function space when optimizing the mean squared error via gradient flow. We show that in the underparameterized regime the network learns eigenfunctions of an integral operato... | 1accept |
Title: Not All Memories are Created Equal: Learning to Expire. Abstract: Attention mechanisms have shown promising results in sequence modeling tasks that require long-term memory. Recent work has investigated mechanisms to reduce the computational cost of preserving and storing the memories. However, not all content i... | 0reject |
Title: Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value. Abstract: Explaining deep convolutional neural networks has been recently drawing increasing attention since it helps to understand the networks' internal operations and why they make certain decisions. Saliency maps, which e... | 2withdrawn |
Title: CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training. Abstract: We introduce causal implicit generative models (CiGMs): models that allow sampling from not only the true observational but also the true interventional distributions. We show that adversarial training can be used to learn... | 1accept |
Title: Transferring Inductive Biases through Knowledge Distillation. Abstract: Having the right inductive biases can be crucial in many tasks or scenarios where data or computing resources are a limiting factor, or where training data is not perfectly representative of the conditions at test time. However, defining, de... | 0reject |
Title: Task-agnostic Continual Learning via Growing Long-Term Memory Networks. Abstract: As our experience shows, humans can learn and deploy a myriad of different skills to tackle the situations they encounter daily. Neural networks, in contrast, have a fixed memory capacity that prevents them from learning more than ... | 2withdrawn |
Title: SCALOR: Generative World Models with Scalable Object Representations. Abstract: Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning. Most of the previous models have been shown to work only on scenes with a few objects. In th... | 1accept |
Title: Improved Training Techniques for Online Neural Machine Translation. Abstract: Neural sequence-to-sequence models are at the basis of state-of-the-art solutions for sequential prediction problems such as machine translation and speech recognition. The models typically assume that the entire input is available whe... | 0reject |
Title: Coordination Among Neural Modules Through a Shared Global Workspace. Abstract: Deep learning has seen a movement away from representing examples with a monolithic hidden state towards a richly structured state. For example, Transformers segment by position, and object-centric architectures decompose images into... | 1accept |
Title: MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning. Abstract: Few-shot learning aims to enable models generalize to new categories (query instances) with only limited labeled samples (support instances) from each category. Metric-based mechanism is a promising direction which compares feature embe... | 2withdrawn |
Title: Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder. Abstract: We introduce a deep generative model for representation learning of biological sequences that, unlike existing models, explicitly represents the evolutionary process. The model makes use of a t... | 1accept |
Title: An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack. Abstract: There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations. The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level. The ... | 0reject |
Title: Multi-agent Reinforcement Learning for Networked System Control. Abstract: This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate suc... | 1accept |
Title: SGD Converges to Global Minimum in Deep Learning via Star-convex Path. Abstract: Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how and why SGD can train these complex networks towards... | 1accept |
Title: An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks. Abstract: Generative adversarial networks (GANs) are usually trained by a minimax game which is notoriously and empirically known to be unstable. Recently, a totally new methodology called
Compo... | 2withdrawn |
Title: Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN. Abstract: Conditional generation is a subclass of generative problems when the output of generation is conditioned by a class attributes’ information. In this paper, we present a new stochastic contrastive conditional generative adversari... | 0reject |
Title: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation. Abstract: Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements. For example, air quality monitoring system records PM2.5, CO, etc. ... | 0reject |
Title: Neural Network Branching for Neural Network Verification . Abstract: Formal verification of neural networks is essential for their deployment in safety-critical areas. Many available formal verification methods have been shown to be instances of a unified Branch and Bound (BaB) formulation. We propose a novel fr... | 1accept |
Title: On the implicit minimization of alternative loss functions when training deep networks. Abstract: Understanding the implicit bias of optimization algorithms is important in order to improve generalization of neural networks. One approach to try to exploit such understanding would be to then make the bias explici... | 0reject |
Title: Connecting Graph Convolution and Graph PCA. Abstract: Graph convolution operator of the GCN model is originally motivated from a localized first-order approximation of spectral graph convolutions. This work stands on a different view; establishing a mathematical connection between graph convolution and graph-reg... | 0reject |
Title: Towards Unknown-aware Deep Q-Learning. Abstract: Deep reinforcement learning (RL) has achieved remarkable success in known environments where the agents are trained, yet the agents do not necessarily know what they don’t know. In particular, RL agents deployed in the open world are naturally subject to environme... | 2withdrawn |
Title: A Variance Principle Explains why Dropout Finds Flatter Minima. Abstract: Although dropout has achieved great success in deep learning, little is known about how it helps the training find a good generalization solution in the high-dimensional parameter space. In this work, we show that the training with dropout... | 0reject |
Title: Set Prediction without Imposing Structure as Conditional Density Estimation. Abstract: Set prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric space over sets. We focus on stochastic and un... | 1accept |
Title: Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms. Abstract: The question why deep learning algorithms generalize so well has attracted increasing
research interest. However, most of the well-established approaches,
such as hypothesis capacity, stability or sparseness, have not provid... | 0reject |
Title: Deformable DETR: Deformable Transformers for End-to-End Object Detection. Abstract: DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, d... | 1accept |
Title: DropGrad: Gradient Dropout Regularization for Meta-Learning. Abstract: With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinforcement learning, and domain generalization. However, meta-lea... | 2withdrawn |
Title: Making Efficient Use of Demonstrations to Solve Hard Exploration Problems. Abstract: This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions. We also introduce a suite of eight ... | 1accept |
Title: Dual-Tree Wavelet Packet CNNs for Image Classification. Abstract: In this paper, we target an important issue of deep convolutional neural networks (CNNs) — the lack of a mathematical understanding of their properties. We present an explicit formalism that is motivated by the similarities between trained CNN ker... | 0reject |
Title: A Novel Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization. Abstract: This paper studies the lower bound complexity for the optimization problem whose objective function is the average of $n$ individual smooth convex functions. We consider the algorithm which gets access to gradient and pr... | 0reject |
Title: Model-based imitation learning from state trajectories. Abstract: Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions. However, in real life expert demonstrations, often the action information is missing and only state trajectories are availa... | 0reject |
Title: Automatic generation of object shapes with desired functionalities. Abstract: 3D objects (artefacts) are made to fulfill functions. Designing an object often starts with defining a list of functionalities that it should provide, also known as functional requirements. Today, the design of 3D object models is stil... | 0reject |
Title: Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations. Abstract: Deep neural networks have become the state-of-the-art models in numerous machine learning tasks. However, general guidance to network architecture design is still missing. In our work, we bridge deep ... | 0reject |
Title: Empirical Studies on the Convergence of Feature Spaces in Deep Learning. Abstract: While deep learning is effective to learn features/representations from data, the distributions of samples in feature spaces learned by various architectures for different training tasks (e.g., latent layers of AEs and feature vec... | 0reject |
Title: The Close Relationship Between Contrastive Learning and Meta-Learning. Abstract: Contrastive learning has recently taken off as a paradigm for learning from unlabeled data. In this paper, we discuss the close relationship between contrastive learning and meta-learning under a certain task distribution. We comple... | 1accept |
Title: Generative Restricted Kernel Machines. Abstract: We introduce a novel framework for generative models based on Restricted Kernel Machines (RKMs) with multi-view generation and uncorrelated feature learning capabilities, called Gen-RKM. To incorporate multi-view generation, this mechanism uses a shared representa... | 0reject |
Title: Learning the Representation of Behavior Styles with Imitation Learning. Abstract: Imitation learning is one of the methods for reproducing expert demonstrations adaptively by learning a mapping between observations and actions. However, behavior styles such as motion trajectory and driving habit depend largely o... | 0reject |
Title: Incorporating User-Item Similarity in Hybrid Neighborhood-based Recommendation System. Abstract: Modern hybrid recommendation systems require a sufficient amount of data. However, several internet privacy issues make users skeptical about sharing their personal information with online service providers. This wor... | 2withdrawn |
Title: No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models. Abstract: Recent research has shown the existence of significant redundancy in large Transformer models. One can prune the redundant parameters without significantly sacrificing the generalization performa... | 1accept |
Title: Policy improvement by planning with Gumbel. Abstract: AlphaZero is a powerful reinforcement learning algorithm based on approximate policy iteration and tree search. However, AlphaZero can fail to improve its policy network, if not visiting all actions at the root of a search tree. To address this issue, we prop... | 1accept |
Title: Improved knowledge distillation by utilizing backward pass knowledge in neural networks. Abstract: Knowledge distillation (KD) is one of the prominent techniques for model compression. In this method, the knowledge
of a large network (teacher) is distilled into a model (student) with usually significantly fewer ... | 2withdrawn |
Title: Transformer-XL: Language Modeling with Longer-Term Dependency. Abstract: We propose a novel neural architecture, Transformer-XL, for modeling longer-term dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empiricall... | 0reject |
Title: Automatic Concept Extraction for Concept Bottleneck-based Video Classification. Abstract: Recent efforts in interpretable deep learning models have shown that concept-based explanation methods achieve competitive accuracy with standard end-to-end models and enable reasoning and intervention about extracted high-... | 0reject |
Title: CNN Based Analysis of the Luria’s Alternating Series Test for Parkinson’s Disease Diagnostics. Abstract: Deep-learning based image classification is applied in this studies to the Luria's alternating series tests to support diagnostics of the Parkinson's disease. Luria's alternating series tests belong to the fa... | 2withdrawn |
Title: A Neural Tangent Kernel Perspective of Infinite Tree Ensembles. Abstract: In practical situations, the tree ensemble is one of the most popular models along with neural networks. A soft tree is a variant of a decision tree. Instead of using a greedy method for searching splitting rules, the soft tree is trained ... | 1accept |
Title: VILD: Variational Imitation Learning with Diverse-quality Demonstrations. Abstract: The goal of imitation learning (IL) is to learn a good policy from high-quality demonstrations. However, the quality of demonstrations in reality can be diverse, since it is easier and cheaper to collect demonstrations from a mix... | 0reject |
Title: Compact Encoding of Words for Efficient Character-level Convolutional Neural Networks Text Classification. Abstract: This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This representation is... | 0reject |
Title: Model-based Saliency for the Detection of Adversarial Examples. Abstract: Adversarial perturbations cause a shift in the salient features of an image, which may result in a misclassification. We demonstrate that gradient-based saliency approaches are unable to capture this shift, and develop a new defense which ... | 0reject |
Title: Signal Coding and Reconstruction using Spike Trains. Abstract: In many animal sensory pathways, the transformation from external stimuli to spike trains is essentially deterministic. In this context, a new mathematical framework for coding and reconstruction, based on a biologically plausible model of the spikin... | 0reject |
Title: Using MMD GANs to correct physics models and improve Bayesian parameter estimation. Abstract: Bayesian parameter estimation methods are robust techniques for quantifying properties of physical systems which cannot be observed directly. In estimating such parameters, one first requires a physics model of the phen... | 2withdrawn |
Title: Fidelity-based Deep Adiabatic Scheduling. Abstract: Adiabatic quantum computation is a form of computation that acts by slowly interpolating a quantum system between an easy to prepare initial state and a final state that represents a solution to a given computational problem. The choice of the interpolation sch... | 1accept |
Title: Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations. Abstract: We propose a unified framework for building unsupervised representations of entities and their compositions, by viewing each entity as a histogram (or distribution) over its contexts. This enables us to ... | 0reject |
Title: HyperNetworks with statistical filtering for defending adversarial examples. Abstract: Deep learning algorithms have been known to be vulnerable to adversarial perturbations in various tasks such as image classification. This problem was addressed by employing several defense methods for detection and rejection ... | 2withdrawn |
Title: Learning to Extend Molecular Scaffolds with Structural Motifs. Abstract: Recent advancements in deep learning-based modeling of molecules promise to accelerate in silico drug discovery. A plethora of generative models is available, building molecules either atom-by-atom and bond-by-bond or fragment-by-fragment. ... | 1accept |
Title: Learning advanced mathematical computations from examples. Abstract: Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect prediction of qualitative cha... | 1accept |
Title: Bayesian Online Meta-Learning. Abstract: Neural networks are known to suffer from catastrophic forgetting when trained on sequential datasets. While there have been numerous attempts to solve this problem for large-scale supervised classification, little has been done to overcome catastrophic forgetting for few-... | 0reject |
Title: Towards Structured Dynamic Sparse Pre-Training of BERT. Abstract: Identifying algorithms for computational efficient unsupervised training of large language models is an important and active area of research.
In this work, we develop and study a straightforward, dynamic always-sparse pre-training approach for B... | 0reject |
Title: Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection. Abstract: Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques. Unfortunately, existing RGB... | 1accept |
Title: Comparing Rewinding and Fine-tuning in Neural Network Pruning. Abstract: Many neural network pruning algorithms proceed in three steps: train the network to completion, remove unwanted structure to compress the network, and retrain the remaining structure to recover lost accuracy. The standard retraining techniq... | 1accept |
Title: Unsupervised Learning of Node Embeddings by Detecting Communities. Abstract: We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph-structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide interesting in... | 0reject |
Title: Global Relational Models of Source Code. Abstract: Models of code can learn distributed representations of a program's syntax and semantics to predict many non-trivial properties of a program. Recent state-of-the-art models leverage highly structured representations of programs, such as trees, graphs and paths t... | 1accept |
Title: Generate Triggers in Neural Relation Extraction. Abstract: In the relation extraction task, the relationship between two entities is determined by some specific words in their source text. These words are called relation triggers, which are the evidence to explain the relationship; other words are called ir-rel... | 0reject |
Title: On Evaluation Metrics for Graph Generative Models. Abstract: In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for ev... | 1accept |
Title: Policy Message Passing: A New Algorithm for Probabilistic Graph Inference. Abstract: A general graph-structured neural network architecture operates on graphs through two core components: (1) complex enough message functions; (2) a fixed information aggregation process. In this paper, we present the Policy Messa... | 0reject |
Title: Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?. Abstract: Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning? This question has largely been studied o... | 1accept |
Title: Balancing Robustness and Sensitivity using Feature Contrastive Learning. Abstract: It is generally believed that robust training of extremely large networks is critical to their success in real-world applications. However, when taken to the extreme, methods that promote robustness can hurt the model’s sensitivit... | 0reject |
Title: Early Stopping in Deep Networks: Double Descent and How to Eliminate it. Abstract: Over-parameterized models, such as large deep networks, often exhibit a double descent phenomenon, whereas a function of model size, error first decreases, increases, and decreases at last. This intriguing double descent behavior ... | 1accept |
Title: A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning. Abstract: Strong progress has been achieved in semi-supervised learning (SSL) by combining several methods, some of which relate to properties of the data distribution p(x), others to the model outputs p(y|x), e.g. minimising t... | 0reject |
Title: DICE: A Simple Sparsification Method for Out-of-distribution Detection. Abstract: Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while... | 2withdrawn |
Title: Knowledge distillation via softmax regression representation learning. Abstract: This paper addresses the problem of model compression via knowledge distillation. We advocate for a method that optimizes the output feature of the penultimate layer of the student network and hence is directly related to representa... | 1accept |
Title: Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering. Abstract: The problem of defending against adversarial attacks has attracted increasing attention in recent years. While various types of defense methods ($\textit{e.g.}$, adversarial training, detection and rejection, and recovery... | 0reject |
Title: Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems. Abstract: First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better convergence rate, are rarely use... | 0reject |
Title: Physics-Aware Flow Data Completion Using Neural Inpainting. Abstract: In this paper we propose a physics-aware neural network for inpainting fluid flow data. We consider that flow field data inherently follows the solution of the Navier-Stokes equations and hence our network is designed to capture physical laws.... | 0reject |
Title: Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping. Abstract: Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing. For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net mod... | 0reject |
Title: Structural Knowledge Distillation. Abstract: Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a smaller one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the ... | 2withdrawn |
Title: DEMI: Discriminative Estimator of Mutual Information . Abstract: Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data. Recent progress has leveraged neural networks to optimize variational lower bounds on mutual information. Al... | 0reject |
Title: Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets. Abstract: Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, conventional NAS methods have mostly tackled the op... | 1accept |
Title: SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing. Abstract: Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e.g., SQL, SPARQL) that can be executed against a structured ontology (e.g. databases, knowledge... | 1accept |
Title: Spherical Motion Dynamics: Learning Dynamics of Neural Network with Normalization, Weight Decay, and SGD. Abstract: In this work, we comprehensively reveal the learning dynamics of neural network with normalization, weight decay (WD), and SGD (with momentum), named as Spherical Motion Dynamics (SMD). Most relate... | 0reject |
Title: Adversarial Learning for Semi-Supervised Semantic Segmentation. Abstract: We propose a method for semi-supervised semantic segmentation using the adversarial network. While most existing discriminators are trained to classify input images as real or fake on the image level, we design a discriminator in a fully c... | 0reject |
Title: The Dual Information Bottleneck. Abstract: The Information-Bottleneck (IB) framework suggests a general characterization of optimal representations in learning, and deep learning in particular. It is based on the optimal trade off between the representation complexity and accuracy, both of which are quantified b... | 0reject |
Title: Individualised Dose-Response Estimation using Generative Adversarial Nets. Abstract: The problem of estimating treatment responses from observational data is by now a well-studied one. Less well studied, though, is the problem of treatment response estimation when the treatments are accompanied by a continuous d... | 0reject |
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