
-
Focal Frequency Loss for Generative Models
Despite the remarkable success of generative models in creating photorea...
read it
-
Offline Policy Selection under Uncertainty
The presence of uncertainty in policy evaluation significantly complicat...
read it
-
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Discrete structures play an important role in applications like program ...
read it
-
Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs
Natural images are projections of 3D objects on a 2D image plane. While ...
read it
-
Named Entity Recognition for Social Media Texts with Semantic Augmentation
Existing approaches for named entity recognition suffer from data sparsi...
read it
-
CoinDICE: Off-Policy Confidence Interval Estimation
We study high-confidence behavior-agnostic off-policy evaluation in rein...
read it
-
Off-Policy Evaluation via the Regularized Lagrangian
The recently proposed distribution correction estimation (DICE) family o...
read it
-
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
Structural equation models (SEMs) are widely used in sciences, ranging f...
read it
-
Unsupervised Landmark Learning from Unpaired Data
Recent attempts for unsupervised landmark learning leverage synthesized ...
read it
-
Scalable Deep Generative Modeling for Sparse Graphs
Learning graph generative models is a challenging task for deep learning...
read it
-
Video Representation Learning with Visual Tempo Consistency
Visual tempo, which describes how fast an action goes, has shown its pot...
read it
-
Novel Policy Seeking with Constrained Optimization
In this work, we address the problem of learning to seek novel policies ...
read it
-
Evolutionary Stochastic Policy Distillation
Solving the Goal-Conditioned Reward Sparse (GCRS) task is a challenging ...
read it
-
Temporal Pyramid Network for Action Recognition
Visual tempo characterizes the dynamics and the temporal scale of an act...
read it
-
Self-Supervised Scene De-occlusion
Natural scene understanding is a challenging task, particularly when enc...
read it
-
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
Model-free deep reinforcement learning (RL) has demonstrated its superio...
read it
-
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
Learning a good image prior is a long-term goal for image restoration an...
read it
-
Energy-Based Processes for Exchangeable Data
Recently there has been growing interest in modeling sets with exchangea...
read it
-
Batch Stationary Distribution Estimation
We consider the problem of approximating the stationary distribution of ...
read it
-
GenDICE: Generalized Offline Estimation of Stationary Values
An important problem that arises in reinforcement learning and Monte Car...
read it
-
Differentiable Top-k Operator with Optimal Transport
The top-k operation, i.e., finding the k largest or smallest elements fr...
read it
-
Real or Not Real, that is the Question
While generative adversarial networks (GAN) have been widely adopted in ...
read it
-
Reinforcement Learning via Fenchel-Rockafellar Duality
We review basic concepts of convex duality, focusing on the very general...
read it
-
Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry. ...
read it
-
AlgaeDICE: Policy Gradient from Arbitrary Experience
In many real-world applications of reinforcement learning (RL), interact...
read it
-
Overcoming Catastrophic Forgetting by Generative Regularization
In this paper, we propose a new method to overcome catastrophic forgetti...
read it
-
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Energy-based models (EBMs) are powerful probabilistic models, but suffer...
read it
-
Recursive Visual Sound Separation Using Minus-Plus Net
Sounds provide rich semantics, complementary to visual data, for many ta...
read it
-
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
In many real-world reinforcement learning applications, access to the en...
read it
-
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
We propose a new approach, called cooperative neural networks (CoNN), wh...
read it
-
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
read it
-
Feature Intertwiner for Object Detection
A well-trained model should classify objects with a unanimous score for ...
read it
-
Learning to Plan via Neural Exploration-Exploitation Trees
Sampling-based algorithms such as RRT and its variants are powerful tool...
read it
-
Bayesian Meta-network Architecture Learning
For deep neural networks, the particular structure often plays a vital r...
read it
-
Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum log-likelihood estimation for exponenti...
read it
-
Learning to Defense by Learning to Attack
Adversarial training provides a principled approach for training robust ...
read it
-
A Neural Compositional Paradigm for Image Captioning
Mainstream captioning models often follow a sequential structure to gene...
read it
-
Neural Network Encapsulation
A capsule is a collection of neurons which represents different variants...
read it
-
Move Forward and Tell: A Progressive Generator of Video Descriptions
We present an efficient framework that can generate a coherent paragraph...
read it
-
Rethinking the Form of Latent States in Image Captioning
RNNs and their variants have been widely adopted for image captioning. I...
read it
-
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem...
read it
-
Learning towards Minimum Hyperspherical Energy
Neural networks are a powerful class of nonlinear functions that can be ...
read it
-
Decoupled Networks
Inner product-based convolution has been a central component of convolut...
read it
-
Syntax-Directed Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous...
read it
-
Smoothed Dual Embedding Control
We revisit the Bellman optimality equation with Nesterov's smoothing tec...
read it
-
Boosting the Actor with Dual Critic
This paper proposes a new actor-critic-style algorithm called Dual Actor...
read it
-
Deep Hyperspherical Learning
Convolution as inner product has been the founding basis of convolutiona...
read it
-
Towards Black-box Iterative Machine Teaching
In this paper, we make an important step towards the black-box machine t...
read it
-
Contrastive Learning for Image Captioning
Image captioning, a popular topic in computer vision, has achieved subst...
read it
-
Iterative Machine Teaching
In this paper, we consider the problem of machine teaching, the inverse ...
read it