research
∙
08/22/2023
ShadowNet for Data-Centric Quantum System Learning
Understanding the dynamics of large quantum systems is hindered by the c...
share
research
∙
08/03/2023
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants
How to enable learnability for new classes while keeping the capability ...
research
∙
06/21/2023
Task-Robust Pre-Training for Worst-Case Downstream Adaptation
Pre-training has achieved remarkable success when transferred to downstr...
research
∙
05/29/2023
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
Large language models (LLMs) have scaled up to unlock a wide range of co...
research
∙
05/22/2023
Policy Representation via Diffusion Probability Model for Reinforcement Learning
Popular reinforcement learning (RL) algorithms tend to produce a unimoda...
research
∙
05/10/2023
Visual Tuning
Fine-tuning visual models has been widely shown promising performance on...
research
∙
03/17/2023
LION: Implicit Vision Prompt Tuning
Despite recent competitive performance across a range of vision tasks, v...
research
∙
03/08/2023
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
In recent years, contrastive learning achieves impressive results on sel...
research
∙
02/28/2023
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising energy-efficient models for...
research
∙
02/06/2023
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning
Few-shot class-incremental learning (FSCIL) has been a challenging probl...
research
∙
02/01/2023
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
Spiking neural networks (SNNs) with event-based computation are promisin...
research
∙
01/03/2023
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation
Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part ...
research
∙
10/12/2022
Towards Theoretically Inspired Neural Initialization Optimization
Automated machine learning has been widely explored to reduce human effo...
research
∙
10/09/2022
Online Training Through Time for Spiking Neural Networks
Spiking neural networks (SNNs) are promising brain-inspired energy-effic...
research
∙
10/09/2022
Invertible Rescaling Network and Its Extensions
Image rescaling is a commonly used bidirectional operation, which first ...
research
∙
09/19/2022
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
Most knowledge graphs (KGs) are incomplete, which motivates one importan...
research
∙
08/13/2022
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
Adaptive gradient algorithms borrow the moving average idea of heavy bal...
research
∙
08/07/2022
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
Steerable models can provide very general and flexible equivariance by f...
research
∙
06/29/2022
Optimization-Induced Graph Implicit Nonlinear Diffusion
Due to the over-smoothing issue, most existing graph neural networks can...
research
∙
06/20/2022
Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction
Link prediction is one important application of graph neural networks (G...
research
∙
05/27/2022
Global Convergence of Over-parameterized Deep Equilibrium Models
A deep equilibrium model (DEQ) is implicitly defined through an equilibr...
research
∙
05/26/2022
SymNMF-Net for The Symmetric NMF Problem
Recently, many works have demonstrated that Symmetric Non-negative Matri...
research
∙
05/01/2022
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
Spiking Neural Network (SNN) is a promising energy-efficient AI model wh...
research
∙
03/25/2022
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap
Recently, contrastive learning has risen to be a promising approach for ...
research
∙
03/25/2022
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
Adversarial Training (AT) is known as an effective approach to enhance t...
research
∙
03/17/2022
Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Modern deep neural networks for classification usually jointly learn a b...
research
∙
01/27/2022
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the O(ε^-7/4) Complexity
This paper studies the accelerated gradient descent for general nonconve...
research
∙
11/09/2021
On Training Implicit Models
This paper focuses on training implicit models of infinite layers. Speci...
research
∙
11/03/2021
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness
Adversarial robustness, which mainly contains sensitivity-based robustne...
research
∙
10/28/2021
Residual Relaxation for Multi-view Representation Learning
Multi-view methods learn representations by aligning multiple views of t...
research
∙
09/29/2021
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
Spiking neural networks (SNNs) are brain-inspired models that enable ene...
research
∙
09/09/2021
Is Attention Better Than Matrix Decomposition?
As an essential ingredient of modern deep learning, attention mechanism,...
research
∙
09/01/2021
Under-bagging Nearest Neighbors for Imbalanced Classification
In this paper, we propose an ensemble learning algorithm called under-ba...
research
∙
07/01/2021
Reparameterized Sampling for Generative Adversarial Networks
Recently, sampling methods have been successfully applied to enhance the...
research
∙
06/18/2021
Quantized Neural Networks via -1, +1 Encoding Decomposition and Acceleration
The training of deep neural networks (DNNs) always requires intensive re...
research
∙
06/10/2021
GBHT: Gradient Boosting Histogram Transform for Density Estimation
In this paper, we propose a density estimation algorithm called Gradient...
research
∙
06/10/2021
Leveraged Weighted Loss for Partial Label Learning
As an important branch of weakly supervised learning, partial label lear...
research
∙
06/03/2021
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression
In this paper, we propose a gradient boosting algorithm for large-scale ...
research
∙
05/27/2021
Optimization Induced Equilibrium Networks
Implicit equilibrium models, i.e., deep neural networks (DNNs) defined b...
research
∙
04/08/2021
PDO-eS^2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs
Spherical signals exist in many applications, e.g., planetary data, LiDA...
research
∙
04/06/2021
Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization
Decentralized optimization over time-varying graphs has been increasingl...
research
∙
04/03/2021
Graph Contrastive Clustering
Recently, some contrastive learning methods have been proposed to simult...
research
∙
03/11/2021
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Aerial Image Segmentation is a particular semantic segmentation problem ...
research
∙
02/22/2021
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have attracted more and more attenti...
research
∙
01/27/2021
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond
Bi-Level Optimization (BLO) is originated from the area of economic game...
research
∙
01/27/2021
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Most differentiable neural architecture search methods construct a super...
research
∙
12/10/2020
Learning Optimization-inspired Image Propagation with Control Mechanisms and Architecture Augmentations for Low-level Vision
In recent years, building deep learning models from optimization perspec...
research
∙
11/06/2020
Towards Efficient Scene Understanding via Squeeze Reasoning
Graph-based convolutional model such as non-local block has shown to be ...
research
∙
10/13/2020
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Neural architecture search (NAS) aims to produce the optimal sparse solu...
research
∙
09/09/2020
