conference stringclasses 6
values | title stringlengths 8 176 | abstract stringlengths 228 5k | decision stringclasses 9
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ICLR.cc/2022/Conference | Particle Based Stochastic Policy Optimization | Stochastic polic have been widely applied for their good property in exploration and uncertainty quantification. Modeling policy distribution by joint state-action distribution within the exponential family has enabled flexibility in exploration and learning multi-modal policies and also involved the probabilistic per... | Reject |
ICLR.cc/2021/Conference | DiP Benchmark Tests: Evaluation Benchmarks for Discourse Phenomena in MT | Despite increasing instances of machine translation (MT) systems including extrasentential context information, the evidence for translation quality improvement is sparse, especially for discourse phenomena. Popular metrics like BLEU are not expressive or sensitive enough to capture quality improvements or drops that a... | Reject |
ICLR.cc/2020/Conference | Combining Q-Learning and Search with Amortized Value Estimates | We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-estimates are then used... | Accept (Poster) |
ICLR.cc/2022/Conference | Composing Partial Differential Equations with Physics-Aware Neural Networks | We introduce a compositional physics-aware neural network (FINN) for learning spatiotemporal advection-diffusion processes. FINN implements a new way of combining the learning abilities of artificial neural networks with physical and structural knowledge from numerical simulation by modeling the constituents of partial... | Reject |
ICLR.cc/2023/Conference | Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach | We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case `target' domain and adapt a model to that worst-case domain, thereby improving the model’s robustness. To synthesise data that is challenging yet semantics-preserving, we gener... | Accept: poster |
ICLR.cc/2021/Conference | Understanding the failure modes of out-of-distribution generalization | Empirical studies suggest that machine learning models often rely on features, such as the background, that may be spuriously correlated with the label only during training time, resulting in poor accuracy during test-time. In this work, we identify the fundamental factors that give rise to this behavior, by explaining... | Accept (Poster) |
ICLR.cc/2023/Conference | Approximating How Single Head Attention Learns | Why do models often attend to salient words, and how does this evolve throughout training? We approximate model training as a two stage process: early on in training when the attention weights are uniform, the model learns to translate individual input word `i` to `o` if they co-occur frequently. Later, the model learn... | Reject |
ICLR.cc/2020/Conference | Feature-map-level Online Adversarial Knowledge Distillation | Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. Thus in this paper, we propose an online knowledge distillation method that transfers not only the knowledge of the class probabilities but a... | Reject |
ICLR.cc/2019/Conference | VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning | To extract and relate visual and linguistic concepts from images and textual descriptions for text-based zero-shot learning (ZSL), we develop variational hetero-encoder (VHE) that decodes text via a deep probabilisitic topic model, the variational posterior of whose local latent variables is encoded from an image via a... | Reject |
ICLR.cc/2023/Conference | Adaptive Block-wise Learning for Knowledge Distillation | Knowledge distillation allows the student network to improve its performance under the supervision of transferred knowledge. Existing knowledge distillation methods are implemented under the implicit hypothesis that knowledge from teacher and student contributes to each layer of the student network to the same extent.... | Reject |
ICLR.cc/2022/Conference | Learning an Object-Based Memory System | A robot operating in a household makes observations of multiple objects as it moves around over the course of days or weeks. The objects may be moved by inhabitants, but not completely at random. The robot may be called upon later to retrieve objects and will need a long-term object-based memory in order to know how ... | Reject |
ICLR.cc/2021/Conference | On Episodes, Prototypical Networks, and Few-Shot Learning | Episodic learning is a popular practice among researchers and practitioners interested in few-shot learning. It consists of organising training in a series of learning problems, each relying on small “support” and “query” sets to mimic the few-shot circumstances encountered during evaluation.
In this paper, we investig... | Reject |
ICLR.cc/2021/Conference | Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways | Object recognition is often viewed as a feedforward, bottom-up process in machine learning, but in real neural systems, object recognition is a complicated process which involves the interplay between two signal pathways. One is the parvocellular pathway (P-pathway), which is slow and extracts fine features of objects;... | Reject |
ICLR.cc/2021/Conference | L2E: Learning to Exploit Your Opponent | Opponent modeling is essential to exploit sub-optimal opponents in strategic interactions. One key challenge facing opponent modeling is how to fast adapt to opponents with diverse styles of strategies. Most previous works focus on building explicit models to predict the opponents’ styles or strategies directly. Howeve... | Reject |
ICLR.cc/2022/Conference | FP-DETR: Detection Transformer Advanced by Fully Pre-training | Large-scale pre-training has proven to be effective for visual representation learning on downstream tasks, especially for improving robustness and generalization. However, the recently developed detection transformers only employ pre-training on its backbone while leaving the key component, i.e., a 12-layer transforme... | Accept (Poster) |
ICLR.cc/2022/Conference | Brain insights improve RNNs' accuracy and robustness for hierarchical control of continually learned autonomous motor motifs | We study the problem of learning dynamics that can produce hierarchically organized continuous outputs consisting of the flexible chaining of re-usable motor ‘motifs’ from which complex behavior is generated. Can a motif library be efficiently and extendably learned without interference between motifs, and can these mo... | Reject |
ICLR.cc/2022/Conference | Task Relatedness-Based Generalization Bounds for Meta Learning | Supposing the $n$ training tasks and the new task are sampled from the same environment, traditional meta learning theory derives an error bound on the expected loss over the new task in terms of the empirical training loss, uniformly over the set of all hypothesis spaces. However, there is still little research on how... | Accept (Spotlight) |
ICLR.cc/2022/Conference | DEEP GRAPH TREE NETWORKS | We propose Graph Tree Networks (GTree), a self-interpretive deep graph neural network architecture which originates from the tree representation of the graphs. In the tree representation, each node forms its own tree where the node itself is the root node and all its neighbors up to hop-k are the subnodes. Under the tr... | Reject |
ICLR.cc/2022/Conference | Graph Neural Networks with Learnable Structural and Positional Representations | Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical positional informat... | Accept (Poster) |
ICLR.cc/2023/Conference | Shifts 2.0: Extending The Dataset of Real Distributional Shifts | Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such as autonomous driving and medicine. This creates a need to be able to assess how robustly ML models generalize as well as the quality of the... | Reject |
ICLR.cc/2023/Conference | A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta. | Mini-batch SGD with momentum is a fundamental algorithm for learning large predictive models. In this paper we develop a new analytic framework to analyze noise-averaged properties of mini-batch SGD for linear models at constant learning rates, momenta and sizes of batches. Our key idea is to consider the dynamics of t... | Accept: poster |
ICLR.cc/2020/Conference | Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection | Despite their successes, deep neural networks still make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituting a major problem for AI safety. While this motivated a recent surge in interest in developing methods to detect such out-of-distribution... | Reject |
ICLR.cc/2023/Conference | DamoFD: Digging into Backbone Design on Face Detection | Face detection (FD) has achieved remarkable success over the past few years, yet,
these leaps often arrive when consuming enormous computation costs. Moreover,
when considering a realistic situation, i.e., building a lightweight face detector
under a computation-scarce scenario, such heavy computation cost limits the a... | Accept: poster |
ICLR.cc/2020/Conference | Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data | This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in order to help AI agents rapidly learn. We show that such algorithms are possible via Generative Teaching Networks (GTNs), a general approach tha... | Reject |
ICLR.cc/2021/Conference | Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space | We consider off-policy evaluation (OPE) in continuous action domains, such as dynamic pricing and personalized dose finding. In OPE, one aims to learn the value under a new policy using historical data generated by a different behavior policy. Most existing works on OPE focus on discrete action domains. To handle conti... | Reject |
ICLR.cc/2019/Conference | SPIGAN: Privileged Adversarial Learning from Simulation | Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled synthetic data, but introduce a domain gap negatively impacting performance. We propose a new unsupervised domain adaptation algorithm, called SPIGAN, relying on Simul... | Accept (Poster) |
ICLR.cc/2022/Conference | Brittle interpretations: The Vulnerability of TCAV and Other Concept-based Explainability Tools to Adversarial Attack | Methods for model explainability have become increasingly critical for testing the fairness and soundness of deep learning. A number of explainability techniques have been developed which use a set of examples to represent a human-interpretable concept in a model's activations. In this work we show that these explainab... | Reject |
ICLR.cc/2018/Conference | Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning | We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training an order of magnitude faster. We scale Deep Voice 3 to dataset sizes unprecedented for TTS, training on more than eight h... | Accept (Poster) |
ICLR.cc/2023/Conference | Hierarchies of Reward Machines | Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode landmarks of the task using high-level events. The structure of RMs enables the decomposition of a task into simpler and independently solvable subtasks th... | Reject |
ICLR.cc/2020/Conference | BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning |
Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble’s cost for both training and testing increases linearly with the numb... | Accept (Poster) |
ICLR.cc/2023/Conference | Private Data Stream Analysis for Universal Symmetric Norm Estimation | We study how to release summary statistics on a data stream subject to the constraint of differential privacy. In particular, we focus on releasing the family of \emph{symmetric norms}, which are invariant under sign-flips and coordinate-wise permutations on an input data stream and include $L_p$ norms, $k$-support nor... | Reject |
ICLR.cc/2020/Conference | NESTED LEARNING FOR MULTI-GRANULAR TASKS | Standard deep neural networks (DNNs) used for classification are trained in an end-to-end fashion for very specific tasks - object recognition, face identification, character recognition, etc. This specificity often leads to overconfident models that generalize poorly to samples that are not from the original training ... | Reject |
ICLR.cc/2020/Conference | P-BN: Towards Effective Batch Normalization in the Path Space | Neural networks with ReLU activation functions have demonstrated their success in many applications. Recently, researchers noticed a potential issue with the optimization of ReLU networks: the ReLU activation functions are positively scale-invariant (PSI), while the weights are not. This mismatch may lead to undesirabl... | Reject |
ICLR.cc/2023/Conference | Some Practical Concerns and Solutions for Using Pretrained Representation in Industrial Systems | Deep learning has dramatically changed the way data scientists and engineers craft features -- the once tedious process of measuring and constructing can now be achieved by training learnable representations. Recent work shows pretraining can endow representations with relevant signals, and in practice they are often u... | Reject |
ICLR.cc/2018/Conference | Neural Clustering By Predicting And Copying Noise | We propose a neural clustering model that jointly learns both latent features and how they cluster. Unlike similar methods our model does not require a predefined number of clusters. Using a supervised approach, we agglomerate latent features towards randomly sampled targets within the same space whilst progressively r... | Reject |
ICLR.cc/2023/Conference | Deconstructing Distributions: A Pointwise Framework of Learning | In machine learning, we traditionally evaluate the performance of a single model, averaged over a collection of test inputs. In this work, we propose a new approach: we measure the performance of a collection of models when evaluated at *single input point*. Specifically, we study a point's *profile*: the relationship ... | Accept: poster |
ICLR.cc/2023/Conference | FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation | Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base. However, they are also more complex systems and need to handle long inputs. In this work, we introduce FiD-Light... | Reject |
ICLR.cc/2018/Conference | Stabilizing GAN Training with Multiple Random Projections | Training generative adversarial networks is unstable in high-dimensions as the true data distribution tends to be concentrated in a small fraction of the ambient space. The discriminator is then quickly able to classify nearly all generated samples as fake, leaving the generator without meaningful gradients and causing... | Reject |
ICLR.cc/2020/Conference | A Uniform Generalization Error Bound for Generative Adversarial Networks | This paper focuses on the theoretical investigation of unsupervised generalization theory of generative adversarial networks (GANs). We first formulate a more reasonable definition of general error and generalization bounds for GANs. On top of that, we establish a bound for generalization error with a fixed generator... | Reject |
ICLR.cc/2020/Conference | Extreme Triplet Learning: Effectively Optimizing Easy Positives and Hard Negatives | The Triplet Loss approach to Distance Metric Learning is defined by the strategy to select triplets and the loss function through which those triplets are optimized. During optimization, two especially important cases are easy positive and hard negative mining which consider, the closest example of the same and differ... | Reject |
ICLR.cc/2023/Conference | Gradient-Based Transfer Learning | We formulate transfer learning as a meta-learning problem by extending upon the current meta-learning paradigm in that support and query data are drawn from different, but related distributions of tasks. Inspired by the success of Gradient-Based Meta-Learning (GBML), we propose to expand it to the transfer learning set... | Reject |
ICLR.cc/2023/Conference | Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective | Graph Neural Networks (GNNs) have shown great promise in various graph learning tasks. However, the computational overheads of fitting GNNs to large-scale graphs grow rapidly, posing obstacles to GNNs from scaling up to real-world applications. To tackle this issue, Graph Lottery Ticket (GLT) hypothesis articulates tha... | Accept: poster |
ICLR.cc/2020/Conference | A critical analysis of self-supervision, or what we can learn from a single image | We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels. We show that three different and representative methods, BiGAN, RotNet and DeepCluster, can learn the first few layers of a convolutional network from a single image as well as using millions... | Accept (Poster) |
ICLR.cc/2020/Conference | Graph Residual Flow for Molecular Graph Generation | Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics. Among these models, invertible flow-based approaches are not fully explored yet. In this paper, we propose a powerful invertible flow for molecular graphs, called Graph Residual Flow... | Reject |
ICLR.cc/2023/Conference | Neural Image Compression with a Diffusion-based Decoder | Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images. The resulting codec, which we call \emph{DIffuson-based Residual Aug... | Reject |
ICLR.cc/2023/Conference | Uncertainty-aware off policy learning | Off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has shown importance in various real-world applications, such as search engines, recommender systems, etc. While the ground-truth logging policy, which generates the logged data, is usually unknown, previou... | Reject |
ICLR.cc/2020/Conference | Analytical Moment Regularizer for Training Robust Networks | Despite the impressive performance of deep neural networks (DNNs) on numerous learning tasks, they still exhibit uncouth behaviours. One puzzling behaviour is the subtle sensitive reaction of DNNs to various noise attacks. Such a nuisance has strengthened the line of research around developing and training noise-robus... | Reject |
ICLR.cc/2021/Conference | Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples | To craft black-box adversarial examples, adversaries need to query the victim model and take proper advantage of its feedback. Existing black-box attacks generally suffer from high query complexity, especially when only the top-1 decision (i.e., the hard-label prediction) of the victim model is available. In this pape... | Accept (Poster) |
ICLR.cc/2021/Conference | Parameter Efficient Multimodal Transformers for Video Representation Learning | The recent success of Transformers in the language domain has motivated adapting it to a multimodal setting, where a new visual model is trained in tandem with an already pretrained language model. However, due to the excessive memory requirements from Transformers, existing work typically fixes the language model and ... | Accept (Poster) |
ICLR.cc/2023/Conference | Dense Correlation Fields for Motion Modeling in Action Recognition | The challenge of action recognition is to capture reasoning motion information. Compared to spatial convolution for appearance, the temporal component provides an additional (and important) clue for motion modeling, as a number of actions can be reliably recognized based on the motion information. In this paper, we pre... | Reject |
ICLR.cc/2021/Conference | Drift Detection in Episodic Data: Detect When Your Agent Starts Faltering | Detection of deterioration of agent performance in dynamic environments is challenging due to the non-i.i.d nature of the observed performance. We consider an episodic framework, where the objective is to detect when an agent begins to falter. We devise a hypothesis testing procedure for non-i.i.d rewards, which is opt... | Reject |
ICLR.cc/2022/Conference | Fast and Sample-Efficient Domain Adaptation for Autoencoder-Based End-to-End Communication | The problem of domain adaptation conventionally considers the setting where a source domain has plenty of labeled data, and a target domain (with a different data distribution) has plenty of unlabeled data but none or very limited labeled data. In this paper, we address the setting where the target domain has only limi... | Reject |
ICLR.cc/2023/Conference | Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections | Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a continuous-time latent representation of the underlying dynamical system where new observations at arbitrary time points can be used to update ... | Accept: poster |
ICLR.cc/2023/Conference | Decoupled Training for Long-Tailed Classification With Stochastic Representations | Decoupling representation learning and classifier learning has been shown to be effective in classification with long-tailed data. There are two main ingredients in constructing a decoupled learning scheme; 1) how to train the feature extractor for representation learning so that it provides generalizable representatio... | Accept: poster |
ICLR.cc/2020/Conference | Adapting Behaviour for Learning Progress | Determining what experience to generate to best facilitate learning (i.e. exploration) is one of the distinguishing features and open challenges in reinforcement learning. The advent of distributed agents that interact with parallel instances of the environment has enabled larger scale and greater flexibility, but has ... | Reject |
ICLR.cc/2023/Conference | No Double Descent in PCA: Training and Pre-Training in High Dimensions | With the recent body of work on overparameterized models the gap between theory and practice in contemporary machine learning is shrinking. While many of the present state-of-the-art models have an encoder-decoder architecture, there is little theoretical work for this model structure. To improve our understanding in t... | Reject |
ICLR.cc/2020/Conference | A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES | Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results. Although most techniques developed so far require knowledge of the architecture of the machine learning model and remain h... | Accept (Poster) |
ICLR.cc/2020/Conference | Exploring Model-based Planning with Policy Networks | Model-based reinforcement learning (MBRL) with model-predictive control or
online planning has shown great potential for locomotion control tasks in both
sample efficiency and asymptotic performance. Despite the successes, the existing
planning methods search from candidate sequences randomly generated in the
action sp... | Accept (Poster) |
ICLR.cc/2021/Conference | iPTR: Learning a representation for interactive program translation retrieval | Program translation contributes to many real world scenarios, such as porting codebases written in an obsolete or deprecated language to a modern one or re-implementing existing projects in one's preferred programming language. Existing data-driven approaches either require large amounts of training data or neglect sig... | Reject |
ICLR.cc/2020/Conference | Learning to Move with Affordance Maps | The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry, but fail to model ... | Accept (Poster) |
ICLR.cc/2021/Conference | Private Split Inference of Deep Networks | Splitting network computations between the edge device and the cloud server is a promising approach for enabling low edge-compute and private inference of neural networks. Current methods for providing the privacy train the model to minimize information leakage for a given set of private attributes. In practice, howeve... | Reject |
ICLR.cc/2018/Conference | Generalizing Across Domains via Cross-Gradient Training | We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniqu... | Accept (Poster) |
ICLR.cc/2022/Conference | Mismatched No More: Joint Model-Policy Optimization for Model-Based RL | Many model-based reinforcement learning (RL) methods follow a similar template: fit a model to previously observed data, and then use data from that model for RL or planning. However, models that achieve better training performance (e.g., lower MSE) are not necessarily better for control: an RL agent may seek out the s... | Reject |
ICLR.cc/2021/Conference | Revisiting Locally Supervised Learning: an Alternative to End-to-end Training | Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper aims to address this problem by revisiting the locally supervised learning, where a network is split into gradient-isolated modules and train... | Accept (Poster) |
ICLR.cc/2023/Conference | HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Network | Private Inference (PI) enables users to enjoy secure AI inference services while companies comply with regulations. Fully Homomorphic Encryption (FHE) based Convolutional Neural Network (CNN) inference is promising as users can offload the whole computation process to the server while protecting the privacy of sensitiv... | Reject |
ICLR.cc/2021/Conference | Improving Hierarchical Adversarial Robustness of Deep Neural Networks | Do all adversarial examples have the same consequences? An autonomous driving system misclassifying a pedestrian as a car may induce a far more dangerous --and even potentially lethal-- behavior than, for instance, a car as a bus. In order to better tackle this important problematic, we introduce the concept of hierarc... | Reject |
ICLR.cc/2018/Conference | Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering | Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared la... | Accept (Poster) |
ICLR.cc/2018/Conference | Inference Suboptimality in Variational Autoencoders | Amortized inference has led to efficient approximate inference for large datasets. The quality of posterior inference is largely determined by two factors: a) the ability of the variational distribution to model the true posterior and b) the capacity of the recognition network to generalize inference over all datapoint... | Invite to Workshop Track |
ICLR.cc/2019/Conference | Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference | Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic feature... | Reject |
ICLR.cc/2021/Conference | News-Driven Stock Prediction Using Noisy Equity State Representation | News-driven stock prediction investigates the correlation between news events and stock price movements.
Previous work has considered effective ways for representing news events and their sequences, but rarely exploited the representation of underlying equity states.
We address this issue by making use of a recurrent n... | Reject |
ICLR.cc/2022/Conference | Mix-MaxEnt: Creating High Entropy Barriers To Improve Accuracy and Uncertainty Estimates of Deterministic Neural Networks | We propose an extremely simple approach to regularize a single deterministic neural network to obtain improved accuracy and reliable uncertainty estimates. Our approach, on top of the cross-entropy loss, simply puts an entropy maximization regularizer corresponding to the predictive distribution in the regions of the e... | Reject |
ICLR.cc/2019/Conference | Learning to Search Efficient DenseNet with Layer-wise Pruning | Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to... | Reject |
ICLR.cc/2023/Conference | Symmetries, Flat Minima, and the Conserved Quantities of Gradient Flow | Empirical studies of the loss landscape of deep networks have revealed that many local minima are connected through low-loss valleys. Yet, little is known about the theoretical origin of such valleys. We present a general framework for finding continuous symmetries in the parameter space, which carve out low-loss valle... | Accept: poster |
ICLR.cc/2023/Conference | Quantization-aware Policy Distillation (QPD) | Recent advancements have made Deep Reinforcement Learning (DRL) exceedingly more powerful, but the produced models remain very computationally complex and therefore difficult to deploy on edge devices.
Compression methods such as quantization and distillation can be used to increase the applicability of DRL models on t... | Reject |
ICLR.cc/2020/Conference | Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis | Recent work has explored sequence-to-sequence latent variable models for expressive speech synthesis (supporting control and transfer of prosody and style), but has not presented a coherent framework for understanding the trade-offs between the competing methods. In this paper, we propose embedding capacity (the amount... | Reject |
ICLR.cc/2020/Conference | On Stochastic Sign Descent Methods | Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models. Sign-based methods, such as signSGD (Bernstein et al., 2018), have recently been gaining popularity because of their simple compression rule and connection to adapti... | Reject |
ICLR.cc/2020/Conference | The Early Phase of Neural Network Training | Recent studies have shown that many important aspects of neural network learning take place within the very earliest iterations or epochs of training. For example, sparse, trainable sub-networks emerge (Frankle et al., 2019), gradient descent moves into a small subspace (Gur-Ari et al., 2018), and the network undergoes... | Accept (Poster) |
ICLR.cc/2023/Conference | Revisiting Structured Dropout | Large neural networks are often overparameterised and prone to overfitting, Dropout is a widely used regularization technique to combat overfitting and improve model generalization. However, unstructured Dropout is not always effective for specific network architectures and this has led to the formation of multiple str... | Reject |
ICLR.cc/2021/Conference | LAYER SPARSITY IN NEURAL NETWORKS | Sparsity has become popular in machine learning, because it can save computational resources, facilitate interpretations, and prevent overfitting. In this paper, we discuss sparsity in the framework of neural networks. In particular, we formulate a new notion of sparsity that concerns the networks’ layers and, therefor... | Reject |
ICLR.cc/2022/Conference | DKM: Differentiable k-Means Clustering Layer for Neural Network Compression | Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight clustering-based DNN model c... | Accept (Poster) |
ICLR.cc/2022/Conference | Deep Q-Network with Proximal Iteration | We employ Proximal Iteration for value-function optimization in reinforcement learning. Proximal Iteration is a computationally efficient technique that enables us to bias the optimization procedure towards more desirable solutions. As a concrete application of Proximal Iteration in deep reinforcement learning, we endo... | Reject |
ICLR.cc/2022/Conference | Interpreting Reinforcement Policies through Local Behaviors | Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood by domain users has received much less attention. In this paper, we propose a novel perspective to understanding RL policies based on identif... | Reject |
ICLR.cc/2023/Conference | Topic Aware Transformer: Domain Shift for Unconditional Text Generation Model | Our goal is to adapt pre-trained language models (PLMs) to support unconditional text generation tasks.
Because Transformer-based models are pre-trained on more massive and heterogeneous corpora than specific target corpus,
the gap between these corpora and the target corpus raises the question of whether these PLMs wi... | Reject |
ICLR.cc/2021/Conference | Viewmaker Networks: Learning Views for Unsupervised Representation Learning | Many recent methods for unsupervised representation learning train models to be invariant to different "views," or distorted versions of an input. However, designing these views requires considerable trial and error by human experts, hindering widespread adoption of unsupervised representation learning methods across d... | Accept (Poster) |
ICLR.cc/2021/Conference | Success-Rate Targeted Reinforcement Learning by Disorientation Penalty | Current reinforcement learning generally uses discounted return as its learning objective. However, real-world tasks may often demand a high success rate, which can be quite different from optimizing rewards. In this paper, we explicitly formulate the success rate as an undiscounted form of return with {0, 1}-binary re... | Reject |
ICLR.cc/2021/Conference | Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning | Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the difficulty in reproducing the data distribution hinder directly applying meta-learning techniques. Although the knowledge of governing parti... | Reject |
ICLR.cc/2023/Conference | DEFENDING BACKDOOR ATTACKS VIA ROBUSTNESS AGAINST NOISY LABEL | Many deep neural networks are vulnerable to backdoor poisoning attacks, in which an adversary strategically injects a backdoor trigger into a small fraction of the training data. The trigger can later be applied during inference to manipulate prediction labels. While the data label could be changed to arbitrary values ... | Reject |
ICLR.cc/2021/Conference | Robust Loss Functions for Complementary Labels Learning | In ordinary-label learning, the correct label is given to each training sample. Similarly, a complementary label is also provided for each training sample in complementary-label learning. A complementary label indicates a class that the example does not belong to. Robust learning of classifiers has been investigated fr... | Reject |
ICLR.cc/2022/Conference | Policy Smoothing for Provably Robust Reinforcement Learning | The study of provable adversarial robustness for deep neural networks (DNNs) has mainly focused on $\textit{static}$ supervised learning tasks such as image classification. However, DNNs have been used extensively in real-world $\textit{adaptive}$ tasks such as reinforcement learning (RL), making such systems vulnerabl... | Accept (Poster) |
ICLR.cc/2020/Conference | Neural networks are a priori biased towards Boolean functions with low entropy | Understanding the inductive bias of neural networks is critical to explaining their ability to generalise. Here,
for one of the simplest neural networks -- a single-layer perceptron with $n$ input neurons, one output neuron, and no threshold bias term -- we prove that upon random initialisation of weights, the a pr... | Reject |
ICLR.cc/2022/Conference | Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In many practical FL scenarios, participants have heterogeneous resources due to disparities in hardware and inference dynamics that require quickly loading models of different s... | Accept (Poster) |
ICLR.cc/2021/Conference | Decoupled Greedy Learning of Graph Neural Networks | Graph Neural Networks (GNNs) become very popular for graph-related applications due to their superior performance. However, they have been shown to be computationally expensive in large scale settings, because their produced node embeddings have to be computed recursively, which scales exponentially with the number of ... | Reject |
ICLR.cc/2022/Conference | Mirror Descent Policy Optimization | Mirror descent (MD), a well-known first-order method in constrained convex optimization, has recently been shown as an important tool to analyze trust-region algorithms in reinforcement learning (RL). However, there remains a considerable gap between such theoretically analyzed algorithms and the ones used in practice.... | Accept (Poster) |
ICLR.cc/2018/Conference | Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input | The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neur... | Accept (Oral) |
ICLR.cc/2020/Conference | Manifold Learning and Alignment with Generative Adversarial Networks | We present a generative adversarial network (GAN) that conducts manifold learning and alignment (MLA): A task to learn the multi-manifold structure underlying data and to align those manifolds without any correspondence information. Our main idea is to exploit the powerful abstraction ability of encoder architecture. S... | Reject |
ICLR.cc/2019/Conference | A Walk with SGD: How SGD Explores Regions of Deep Network Loss? | The non-convex nature of the loss landscape of deep neural networks (DNN) lends them the intuition that over the course of training, stochastic optimization algorithms explore different regions of the loss surface by entering and escaping many local minima due to the noise induced by mini-batches. But is this really th... | Reject |
ICLR.cc/2021/Conference | Contrastive estimation reveals topic posterior information to linear models | Contrastive learning is an approach to representation learning that utilizes naturally occurring similar and dissimilar pairs of data points to find useful embeddings of data. In the context of document classification under topic modeling assumptions, we prove that contrastive learning is capable of recovering a repres... | Reject |
ICLR.cc/2021/Conference | PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction | 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 values (i.e., point prediction), or by producing prediction intervals (PIs) that quantify uncertainty. We ... | Reject |
ICLR.cc/2022/Conference | Generative Modeling with Optimal Transport Maps | With the discovery of Wasserstein GANs, Optimal Transport (OT) has become a powerful tool for large-scale generative modeling tasks. In these tasks, OT cost is typically used as the loss for training GANs. In contrast to this approach, we show that the OT map itself can be used as a generative model, providing comparab... | Accept (Poster) |
ICLR.cc/2023/Conference | Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs | The formalization of existing mathematical proofs is a notoriously difficult process. Despite decades of research on automation and proof assistants, writing formal proofs remains arduous and only accessible to a few experts. While previous studies to automate formalization focused on powerful search algorithms, no att... | Accept: notable-top-5% |
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