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【论文阅读笔记】NeurIPS2020文章列表Part1_cross-lingual retrieval for iterative self-supervi
作者：2023面试高手 | 2024-04-07 01:16:28
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cross-lingual retrieval for iterative self-supervised training
- A graph similarity for deep learning
- An Unsupervised Information-Theoretic Perceptual Quality Metric
- Self-Supervised MultiModal Versatile Networks
- Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method
- Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
- Neural Methods for Point-wise Dependency Estimation
- Fast and Flexible Temporal Point Processes with Triangular Maps
- Backpropagating Linearly Improves Transferability of Adversarial Examples
- PyGlove: Symbolic Programming for Automated Machine Learning
- Fourier Sparse Leverage Scores and Approximate Kernel Learning
- Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds
- Synbols: Probing Learning Algorithms with Synthetic Datasets
- Adversarially Robust Streaming Algorithms via Differential Privacy
- Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
- Cascaded Text Generation with Markov Transformers
- Improving Local Identifiability in Probabilistic Box Embeddings
- Permute-and-Flip: A new mechanism for differentially private selection
- Deep reconstruction of strange attractors from time series
- Reciprocal Adversarial Learning via Characteristic Functions
- Statistical Guarantees of Distributed Nearest Neighbor Classification
- Stein Self-Repulsive Dynamics: Benefits From Past Samples
- The Statistical Complexity of Early-Stopped Mirror Descent
- Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
- Quantitative Propagation of Chaos for SGD in Wide Neural Networks
- A Causal View on Robustness of Neural Networks
- Minimax Classification with 0-1 Loss and Performance Guarantees
- How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
- Coresets for Regressions with Panel Data
- Learning Composable Energy Surrogates for PDE Order Reduction
- Efficient Contextual Bandits with Continuous Actions
- Achieving Equalized Odds by Resampling Sensitive Attributes
- Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
- Hard Shape-Constrained Kernel Machines
- A Closer Look at the Training Strategy for Modern Meta-Learning
- On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law
- Generalised Bayesian Filtering via Sequential Monte Carlo
- Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time
- Flows for simultaneous manifold learning and density estimation
- Simultaneous Preference and Metric Learning from Paired Comparisons
- Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
- Learning Manifold Implicitly via Explicit Heat-Kernel Learning
- Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network
- One-bit Supervision for Image Classification
- What is being transferred in transfer learning?
- Submodular Maximization Through Barrier Functions
- Neural Networks with Recurrent Generative Feedback
- Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
- Exploiting weakly supervised visual patterns to learn from partial annotations
- Improving Inference for Neural Image Compression
- Neuron Merging: Compensating for Pruned Neurons
- FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
- Towards Playing Full MOBA Games with Deep Reinforcement Learning
- Rankmax: An Adaptive Projection Alternative to the Softmax Function
- Online Agnostic Boosting via Regret Minimization
- Causal Intervention for Weakly-Supervised Semantic Segmentation
- Belief Propagation Neural Networks
- Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
- Post-training Iterative Hierarchical Data Augmentation for Deep Networks
- Debugging Tests for Model Explanations
- Robust compressed sensing using generative models
- Fairness without Demographics through Adversarially Reweighted Learning
- Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
- Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
- The route to chaos in routing games: When is price of anarchy too optimistic?
- Online Algorithm for Unsupervised Sequential Selection with Contextual Information
- Adapting Neural Architectures Between Domains
- What went wrong and when? Instance-wise feature importance for time-series black-box models
- Towards Better Generalization of Adaptive Gradient Methods
- Learning Guidance Rewards with Trajectory-space Smoothing
- Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
- Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding
- Deep Structural Causal Models for Tractable Counterfactual Inference
- Convolutional Generation of Textured 3D Meshes
- A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
- Better Set Representations For Relational Reasoning
- AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
- A Combinatorial Perspective on Transfer Learning
- Hardness of Learning Neural Networks with Natural Weights
- Higher-Order Spectral Clustering of Directed Graphs
- Primal-Dual Mesh Convolutional Neural Networks
- The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
- Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
- Sinkhorn Barycenter via Functional Gradient Descent
- Coresets for Near-Convex Functions
- Bayesian Deep Ensembles via the Neural Tangent Kernel
- Improved Schemes for Episodic Memory-based Lifelong Learning
- Adaptive Sampling for Stochastic Risk-Averse Learning
- Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring
- Discovering Reinforcement Learning Algorithms
- Taming Discrete Integration via the Boon of Dimensionality
- Blind Video Temporal Consistency via Deep Video Prior
- Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
- Model Selection for Production System via Automated Online Experiments
- On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
- Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
- Adaptation Properties Allow Identification of Optimized Neural Codes
- Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems
- Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
- Conservative Q-Learning for Offline Reinforcement Learning
- Online Influence Maximization under Linear Threshold Model
- Ensembling geophysical models with Bayesian Neural Networks
- Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
- Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
- Understanding Deep Architecture with Reasoning Layer
- Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
- Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration
- Detection as Regression: Certified Object Detection with Median Smoothing
- Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
- ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
- FleXOR: Trainable Fractional Quantization
- The Implications of Local Correlation on Learning Some Deep Functions
- Learning to search efficiently for causally near-optimal treatments
- A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
- Recurrent Quantum Neural Networks
- No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix
- A Unifying View of Optimism in Episodic Reinforcement Learning
- Continuous Submodular Maximization: Beyond DR-Submodularity
- An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits
- Assessing SATNet’s Ability to Solve the Symbol Grounding Problem
- A Bayesian Nonparametrics View into Deep Representations
- On the Similarity between the Laplace and Neural Tangent Kernels
- A causal view of compositional zero-shot recognition
- HiPPO: Recurrent Memory with Optimal Polynomial Projections
- Auto Learning Attention
- CASTLE: Regularization via Auxiliary Causal Graph Discovery
- Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
- Explainable Voting
- Deep Archimedean Copulas
- Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
- UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
- Thunder: a Fast Coordinate Selection Solver for Sparse Learning
- Neural Networks Fail to Learn Periodic Functions and How to Fix It
- Distribution Matching for Crowd Counting
- Correspondence learning via linearly-invariant embedding
- Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
- On Adaptive Attacks to Adversarial Example Defenses
- Sinkhorn Natural Gradient for Generative Models
- Online Sinkhorn: Optimal Transport distances from sample streams
- Ultrahyperbolic Representation Learning
- Locally-Adaptive Nonparametric Online Learning
- Compositional Generalization via Neural-Symbolic Stack Machines
- Graphon Neural Networks and the Transferability of Graph Neural Networks
- Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
- Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction
- Deep Transformers with Latent Depth
- Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows
- Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso
- A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
- Efficient Exact Verification of Binarized Neural Networks
- Ultra-Low Precision 4-bit Training of Deep Neural Networks
- Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
- On Numerosity of Deep Neural Networks
- Outlier Robust Mean Estimation with Subgaussian Rates via Stability
- Self-Supervised Relationship Probing
- Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
- Prophet Attention: Predicting Attention with Future Attention
- Language Models are Few-Shot Learners
- Margins are Insufficient for Explaining Gradient Boosting
- Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics
- MomentumRNN: Integrating Momentum into Recurrent Neural Networks
- Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
- Projected Stein Variational Gradient Descent
- Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
- SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
- On the equivalence of molecular graph convolution and molecular wave function with poor basis set
- The Power of Predictions in Online Control
- Learning Affordance Landscapes for Interaction Exploration in 3D Environments
- Cooperative Multi-player Bandit Optimization
- Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
- Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
- A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
- Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains
- Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
- Optimizing Neural Networks via Koopman Operator Theory
- SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
- Adversarial Robustness of Supervised Sparse Coding
- Differentiable Meta-Learning of Bandit Policies
- Biologically Inspired Mechanisms for Adversarial Robustness
- Statistical-Query Lower Bounds via Functional Gradients
- Near-Optimal Reinforcement Learning with Self-Play
- Network Diffusions via Neural Mean-Field Dynamics
- Self-Distillation as Instance-Specific Label Smoothing
- Towards Problem-dependent Optimal Learning Rates
- Cross-lingual Retrieval for Iterative Self-Supervised Training
- Rethinking pooling in graph neural networks
- Pointer Graph Networks
- Gradient Regularized V-Learning for Dynamic Treatment Regimes
- Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
- Forethought and Hindsight in Credit Assignment
- Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
- Rescuing neural spike train models from bad MLE
- Lower Bounds and Optimal Algorithms for Personalized Federated Learning
- Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework
- Deep Imitation Learning for Bimanual Robotic Manipulation
- Stationary Activations for Uncertainty Calibration in Deep Learning
- Ensemble Distillation for Robust Model Fusion in Federated Learning
- Falcon: Fast Spectral Inference on Encrypted Data
- On Power Laws in Deep Ensembles
- Practical Quasi-Newton Methods for Training Deep Neural Networks
- Approximation Based Variance Reduction for Reparameterization Gradients
- Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
- Consistent feature selection for analytic deep neural networks
- Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
- Information Maximization for Few-Shot Learning
- Inverse Reinforcement Learning from a Gradient-based Learner
- Bayesian Multi-type Mean Field Multi-agent Imitation Learning
- Bayesian Robust Optimization for Imitation Learning
- Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
- Riemannian Continuous Normalizing Flows
- Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation
- Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance
- Online Robust Regression via SGD on the l1 loss
- PRANK: motion Prediction based on RANKing
- Fighting Copycat Agents in Behavioral Cloning from Observation Histories
- Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
- Structured Prediction for Conditional Meta-Learning
- Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
- The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
- Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
- Identifying Learning Rules From Neural Network Observables
- Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions
- Weakly-Supervised Reinforcement Learning for Controllable Behavior
- Improving Policy-Constrained Kidney Exchange via Pre-Screening
- Learning abstract structure for drawing by efficient motor program induction
- Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? — A Neural Tangent Kernel Perspective
- Dual Instrumental Variable Regression
- Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
- Interventional Few-Shot Learning
- Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
- Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
- ShiftAddNet: A Hardware-Inspired Deep Network
- Network-to-Network Translation with Conditional Invertible Neural Networks
- Intra-Processing Methods for Debiasing Neural Networks
- Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
- Model-based Policy Optimization with Unsupervised Model Adaptation
- Implicit Regularization and Convergence for Weight Normalization
- Geometric All-way Boolean Tensor Decomposition
- Modular Meta-Learning with Shrinkage
- A/B Testing in Dense Large-Scale Networks: Design and Inference
- What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
- Partially View-aligned Clustering
- Partial Optimal Tranport with applications on Positive-Unlabeled Learning
- Toward the Fundamental Limits of Imitation Learning
- Logarithmic Pruning is All You Need
- Hold me tight! Influence of discriminative features on deep network boundaries
- Learning from Mixtures of Private and Public Populations
- Adversarial Weight Perturbation Helps Robust Generalization
- Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes
- Adversarial Self-Supervised Contrastive Learning
- Normalizing Kalman Filters for Multivariate Time Series Analysis
- Learning to summarize with human feedback
- Fourier Spectrum Discrepancies in Deep Network Generated Images
- Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
- Learning Dynamic Belief Graphs to Generalize on Text-Based Games
- Triple descent and the two kinds of overfitting: where & why do they appear?
- Multimodal Graph Networks for Compositional Generalization in Visual Question Answering
- Learning Graph Structure With A Finite-State Automaton Layer
- A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions
- Unsupervised object-centric video generation and decomposition in 3D
- Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
- Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
- A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances
- Causal analysis of Covid-19 Spread in Germany
- Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
- Adaptive Gradient Quantization for Data-Parallel SGD
- Finite Continuum-Armed Bandits
- Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
- Compact task representations as a normative model for higher-order brain activity
- Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs
- Co-exposure Maximization in Online Social Networks
- UCLID-Net: Single View Reconstruction in Object Space
- Reinforcement Learning for Control with Multiple Frequencies
- Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
- Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs
- A Unified View of Label Shift Estimation
- Optimal Private Median Estimation under Minimal Distributional Assumptions
- Breaking the Communication-Privacy-Accuracy Trilemma
- Audeo: Audio Generation for a Silent Performance Video
- Ode to an ODE
- Self-Distillation Amplifies Regularization in Hilbert Space
- Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
- Community detection using fast low-cardinality semidefinite programming
- Modeling Noisy Annotations for Crowd Counting
- An operator view of policy gradient methods
- Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases
- Online MAP Inference of Determinantal Point Processes
- Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
- Inferring learning rules from animal decision-making
- Input-Aware Dynamic Backdoor Attack
- How hard is to distinguish graphs with graph neural networks?
- Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition
- Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
- Cross-Scale Internal Graph Neural Network for Image Super-Resolution
- Unsupervised Representation Learning by Invariance Propagation
- Restoring Negative Information in Few-Shot Object Detection
- Do Adversarially Robust ImageNet Models Transfer Better?
- Robust Correction of Sampling Bias using Cumulative Distribution Functions
- Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
- Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
- Classification with Valid and Adaptive Coverage
- Learning Global Transparent Models consistent with Local Contrastive Explanations
- Learning to Approximate a Bregman Divergence
- Diverse Image Captioning with Context-Object Split Latent Spaces
- Le
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