先睹为快:神经网络顶会ICLR 2019论文热点分析 - lqfarmer的文章 - 知乎 https://zhuanlan.zhihu.com/p/53011934
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ICLR-2019（International Conference on Learning Representations 2019），将于2019年5月9日在美国路易斯安那州的新奥尔良举行，这也是2019年最新的一个国际性的AI顶会。目前，ICLR-2019的最新接受的论文已经Release出来了，本文对本届会议接受的论文进行整理，按照统计方法，抽取出了其中集中程度最高的27个主题，并抽样了每个主题下的一些最新论文，提供给需要的朋友周末充电。
ICLR-2019接受全部论文地址
https://openreview.net/group?id=ICLR.cc/2019/Conference#accepted-oral-papers
主题热点
Deep reinforcement learning
Generative adversarial networks
Deep learning
Deep neural Network
Domain adaptation
Recurrent neural network
Neural architecture search
Convolutional networks network
Deep networks
Graph neural network
Bayesian neural Network
Variational autoencoders
Gradient descent optimization
Unsupervised learning
Adversarial examples/Adversarial attacks/Adversarial training
Imitation learning
Generalization bounds
Monte carlo method
Representation learning
Neural program
Experience replay
Batch normalization
Word embeddings
Neural machine translation
Transfer learning
Program synthesis
Image-to-image translation
热点论文推荐
Reinforcement learning
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning
Information-Directed Exploration for Deep Reinforcement Learning
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Adversarial Imitation via Variational Inverse Reinforcement Learning
Deep reinforcement learning with relational inductive biases
Variance Reduction for Reinforcement Learning in Input-Driven Environments
Recall Traces: Backtracking Models for Efficient Reinforcement Learning
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
Contingency-Aware Exploration in Reinforcement Learning
Learning to Schedule Communication in Multi-agent Reinforcement Learning
Modeling the Long Term Future in Model-Based Reinforcement Learning
Visceral Machines: Reinforcement Learning with Intrinsic Physiological Rewards
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
Recurrent Experience Replay in Distributed Reinforcement Learning
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Hierarchical Reinforcement Learning with Hindsight
Generative adversarial networks
A generative adversarial network for style modeling in a text-to-speech system
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS
Improving Generalization and Stability of Generative Adversarial Networks
On Self Modulation for Generative Adversarial Networks
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
Visualizing and Understanding Generative Adversarial Networks
Learning from Incomplete Data with Generative Adversarial Networks
A Direct Approach to Robust Deep Learning Using Adversarial Networks
A Variational Inequality Perspective on Generative Adversarial Networks
On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
RelGAN: Relational Generative Adversarial Networks for Text Generation
Diversity-Sensitive Conditional Generative Adversarial Networks
Scalable Reversible Generative Models with Free-form Continuous Dynamics
Optimal Transport Maps For Distribution Preserving Operations on Latent Spaces of Generative Models
Do Deep Generative Models Know What They Don't Know?
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
Distribution-Interpolation Trade off in Generative Models
Kernel Change-point Detection with Auxiliary Deep Generative Models
Multi-Domain Adversarial Learning
SPIGAN: Privileged Adversarial Learning from Simulation
Deep learning
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Dynamic Sparse Graph for Efficient Deep Learning
Quasi-hyperbolic momentum and Adam for deep learning
DeepOBS: A Deep Learning Optimizer Benchmark Suite
Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution
DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS
Deep learning generalizes because the parameter-function map is biased towards simple functions
Deep neural Network
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
On the loss landscape of a class of deep neural networks with no bad local valleys
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
Adaptive Estimators Show Information Compression in Deep Neural Networks
Domain adaptation
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
Unsupervised Domain Adaptation for Distance Metric Learning
ADVERSARIAL DOMAIN ADAPTATION FOR STABLE BRAIN-MACHINE INTERFACES
LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION
Improving the Generalization of Adversarial Training with Domain Adaptation
Regularized Learning for Domain Adaptation under Label Shifts
Recurrent neural network
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
A MAX-AFFINE SPLINE PERSPECTIVE OF RECURRENT NEURAL NETWORKS
Quaternion Recurrent Neural Networks
Variational Smoothing in Recurrent Neural Network Language Models
Generalized Tensor Models for Recurrent Neural Networks
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Neural architecture search
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Learnable Embedding Space for Efficient Neural Architecture Compression
Graph HyperNetworks for Neural Architecture Search
SNAS: stochastic neural architecture search
DARTS: Differentiable Architecture Search
Convolutional networks network
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Deep Convolutional Networks as shallow Gaussian Processes
STCN: Stochastic Temporal Convolutional Networks
Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation
A rotation-equivariant convolutional neural network model of primary visual cortex
Human-level Protein Localization with Convolutional Neural Networks
Deep networks
Critical Learning Periods in Deep Networks
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
Predicting the Generalization Gap in Deep Networks with Margin Distributions
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
Graph neural network
How Powerful are Graph Neural Networks?
Capsule Graph Neural Network
Adversarial Attacks on Graph Neural Networks via Meta Learning
Supervised Community Detection with Line Graph Neural Networks
Bayesian neural Network
Deterministic Variational Inference for Robust Bayesian Neural Networks
Function Space Particle Optimization for Bayesian Neural Networks
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS
Variational autoencoders
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
Variational Autoencoders with Jointly Optimized Latent Dependency Structure
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Gradient descent optimization
Gradient descent aligns the layers of deep linear networks
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Fluctuation-dissipation relations for stochastic gradient descent
Unsupervised learning
Learning Unsupervised Learning Rules
Unsupervised Learning of the Set of Local Maxima
Unsupervised Learning via Meta-Learning
Adversarial examples/Adversarial attacks/Adversarial training
Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
The Limitations of Adversarial Training and the Blind-Spot Attack
Generalizable Adversarial Training via Spectral Normalization
Cost-Sensitive Robustness against Adversarial Examples
Characterizing Audio Adversarial Examples Using Temporal Dependency
Are adversarial examples inevitable?
Imitation learning
Sample Efficient Imitation Learning for Continuous Control
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Generative predecessor models for sample-efficient imitation learning
Generalization bounds
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
Monte carlo method
Probabilistic Planning with Sequential Monte Carlo methods
Bayesian Modelling and Monte Carlo Inference for GAN
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Representation learning
Measuring Compositionality in Representation Learning
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
The Laplacian in RL: Learning Representations with Efficient Approximations
Learning Actionable Representations with Goal Conditioned Policies
Learning Programmatically Structured Representations with Perceptor Gradients
Neural program
Neural Program Repair by Jointly Learning to Localize and Repair
Experience replay
DHER: Hindsight Experience Replay for Dynamic Goals
Competitive experience replay
Batch normalization
Towards Understanding Regularization in Batch Normalization
A Mean Field Theory of Batch Normalization
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
Word embeddings
Understanding Composition of Word Embeddings via Tensor Decomposition
Unsupervised Hyper-alignment for Multilingual Word Embeddings
Poincare Glove: Hyperbolic Word Embeddings
Neural machine translation
Identifying and Controlling Important Neurons in Neural Machine Translation
Multilingual Neural Machine Translation with Knowledge Distillation
Multilingual Neural Machine Translation With Soft Decoupled Encoding
Transfer learning
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning
Transfer Learning for Sequences via Learning to Collocate
An analytic theory of generalization dynamics and transfer learning in deep linear networks
Program synthesis
Execution-Guided Neural Program Synthesis
Learning a Meta-Solver for Syntax-Guided Program Synthesis
Synthetic Datasets for Neural Program Synthesis
Image-to-image translation
Harmonic Unpaired Image-to-image Translation
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
Instance-aware Image-to-Image Translation
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