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State Entropy Maximization with Random Encoders for Efficient Exploration
Recent exploration methods have proven to be a recipe for improving samp...
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Model-Augmented Q-learning
In recent years, Q-learning has become indispensable for model-free rein...
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MASKER: Masked Keyword Regularization for Reliable Text Classification
Pre-trained language models have achieved state-of-the-art accuracies on...
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Provable Memorization via Deep Neural Networks using Sub-linear Parameters
It is known that Θ(N) parameters are sufficient for neural networks to m...
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Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Model-based reinforcement learning (RL) has shown great potential in var...
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i-Mix: A Strategy for Regularizing Contrastive Representation Learning
Contrastive representation learning has shown to be an effective way of ...
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A Deeper Look at the Layerwise Sparsity of Magnitude-based Pruning
Recent discoveries on neural network pruning reveal that, with a careful...
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Few-shot Visual Reasoning with Meta-analogical Contrastive Learning
While humans can solve a visual puzzle that requires logical reasoning b...
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Time-Reversal Symmetric ODE Network
Time-reversal symmetry, which requires that the dynamics of a system sho...
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Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
While semi-supervised learning (SSL) has proven to be a promising way fo...
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CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Novelty detection, i.e., identifying whether a given sample is drawn fro...
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Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Experience replay, which enables the agents to remember and reuse experi...
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Learning from Failure: Training Debiased Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spur...
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Guiding Deep Molecular Optimization with Genetic Exploration
De novo molecular design attempts to search over the chemical space for ...
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Learning to Generate Noise for Robustness against Multiple Perturbations
Adversarial learning has emerged as one of the successful techniques to ...
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QOPT: Optimistic Value Function Decentralization for Cooperative Multi-Agent Reinforcement Learning
We propose a novel value-based algorithm for cooperative multi-agent rei...
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Learning What to Defer for Maximum Independent Sets
Designing efficient algorithms for combinatorial optimization appears ub...
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Minimum Width for Universal Approximation
The universal approximation property of width-bounded networks has been ...
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Learning Bounds for Risk-sensitive Learning
In risk-sensitive learning, one aims to find a hypothesis that minimizes...
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Consistency Regularization for Certified Robustness of Smoothed Classifiers
A recent technique of randomized smoothing has shown that the worst-case...
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Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) enjoys several benefits, such as...
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M2m: Imbalanced Classification via Major-to-minor Translation
In most real-world scenarios, labeled training datasets are highly class...
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Regularizing Class-wise Predictions via Self-knowledge Distillation
Deep neural networks with millions of parameters may suffer from poor ge...
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Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
Generative adversarial networks (GANs) have shown outstanding performanc...
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Freeze Discriminator: A Simple Baseline for Fine-tuning GANs
Generative adversarial networks (GANs) have shown outstanding performanc...
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Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning
Magnitude-based pruning is one of the simplest methods for pruning neura...
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Mining GOLD Samples for Conditional GANs
Conditional generative adversarial networks (cGANs) have gained a consid...
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Rethinking Data Augmentation: Self-Supervision and Self-Distillation
Data augmentation techniques, e.g., flipping or cropping, which systemat...
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A Simple Randomization Technique for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unse...
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Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
This paper studies how to sketch element-wise functions of low-rank matr...
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Learning What and Where to Transfer
As the application of deep learning has expanded to real-world problems ...
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Spectral Approximate Inference
Given a graphical model (GM), computing its partition function is the mo...
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Training CNNs with Selective Allocation of Channels
Recent progress in deep convolutional neural networks (CNNs) have enable...
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Incremental Learning with Unlabeled Data in the Wild
Deep neural networks are known to suffer from catastrophic forgetting in...
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Bitcoin vs. Bitcoin Cash: Coexistence or Downfall of Bitcoin Cash?
In Aug. 2017, Bitcoin was split into the original Bitcoin (BTC) and Bitc...
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Robust Inference via Generative Classifiers for Handling Noisy Labels
Large-scale datasets may contain significant proportions of noisy (incor...
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InstaGAN: Instance-aware Image-to-Image Translation
Unsupervised image-to-image translation has gained considerable attentio...
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Simulation-based Distributed Coordination Maximization over Networks
In various online/offline multi-agent networked environments, it is very...
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A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Detecting test samples drawn sufficiently far away from the training dis...
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Anytime Neural Prediction via Slicing Networks Vertically
The pioneer deep neural networks (DNNs) have emerged to be deeper or wid...
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Hierarchical Novelty Detection for Visual Object Recognition
Deep neural networks have achieved impressive success in large-scale vis...
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Bucket Renormalization for Approximate Inference
Probabilistic graphical models are a key tool in machine learning applic...
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Optimizing Spectral Sums using Randomized Chebyshev Expansions
The trace of matrix functions, often called spectral sums, e.g., rank, l...
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Gauged Mini-Bucket Elimination for Approximate Inference
Computing the partition function Z of a discrete graphical model is a fu...
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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
The problem of detecting whether a test sample is from in-distribution (...
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Confident Multiple Choice Learning
Ensemble methods are arguably the most trustworthy techniques for boosti...
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Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models
The Gibbs sampler is a particularly popular Markov chain used for learni...
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Sequential Local Learning for Latent Graphical Models
Learning parameters of latent graphical models (GM) is inherently much h...
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Contextual Multi-armed Bandits under Feature Uncertainty
We study contextual multi-armed bandit problems under linear realizabili...
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Gauging Variational Inference
Computing partition function is the most important statistical inference...
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