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09/12/2023
Digital Twin System for Home Service Robot Based on Motion Simulation
In order to improve the task execution capability of home service robot,...
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08/29/2023
Robust Long-Tailed Learning via Label-Aware Bounded CVaR
Data in the real-world classification problems are always imbalanced or ...
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08/16/2023
On fusogenicity of positively charged phased-separated lipid vesicles: experiments and computational simulations
This paper studies the fusogenicity of cationic liposomes in relation to...
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06/07/2023
On the Generalization of Multi-modal Contrastive Learning
Multi-modal contrastive learning (MMCL) has recently garnered considerab...
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05/29/2023
Counterpart Fairness – Addressing Systematic between-group Differences in Fairness Evaluation
When using machine learning (ML) to aid decision-making, it is critical ...
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05/16/2023
Contrastive Label Enhancement
Label distribution learning (LDL) is a new machine learning paradigm for...
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03/12/2023
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond
Oversmoothing is a common phenomenon in a wide range of Graph Neural Net...
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03/08/2023
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
In recent years, contrastive learning achieves impressive results on sel...
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03/04/2023
Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism
Recently, a variety of methods under the name of non-contrastive learnin...
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03/02/2023
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning
Recent works have shown that self-supervised learning can achieve remark...
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02/28/2023
Multi-view Semantic Consistency based Information Bottleneck for Clustering
Multi-view clustering can make use of multi-source information for unsup...
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02/26/2023
MCoCo: Multi-level Consistency Collaborative Multi-view Clustering
Multi-view clustering can explore consistent information from different ...
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02/12/2023
USER: Unsupervised Structural Entropy-based Robust Graph Neural Network
Unsupervised/self-supervised graph neural networks (GNN) are vulnerable ...
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12/18/2022
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization
Despite impressive success in many tasks, deep learning models are shown...
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11/01/2022
Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits
In the infinite-armed bandit problem, each arm's average reward is sampl...
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10/15/2022
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Masked Autoencoders (MAE) based on a reconstruction task have risen to b...
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10/14/2022
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Vision Transformers (ViTs) have recently achieved competitive performanc...
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10/13/2022
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors
Deep models often fail to generalize well in test domains when the data ...
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09/30/2022
Overparameterized ReLU Neural Networks Learn the Simplest Models: Neural Isometry and Exact Recovery
The practice of deep learning has shown that neural networks generalize ...
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08/09/2022
Motif-based Graph Representation Learning with Application to Chemical Molecules
This work considers the task of representation learning on the attribute...
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06/29/2022
Optimization-Induced Graph Implicit Nonlinear Diffusion
Due to the over-smoothing issue, most existing graph neural networks can...
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06/02/2022
A DTCWT-SVD Based Video Watermarking resistant to frame rate conversion
Videos can be easily tampered, copied and redistributed by attackers for...
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04/19/2022
A stochastic Stein Variational Newton method
Stein variational gradient descent (SVGD) is a general-purpose optimizat...
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04/15/2022
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph
Knowledge graph (KG) representation learning aims to encode entities and...
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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 ...
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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...
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11/19/2021
Fooling Adversarial Training with Inducing Noise
Adversarial training is widely believed to be a reliable approach to imp...
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10/28/2021
Residual Relaxation for Multi-view Representation Learning
Multi-view methods learn representations by aligning multiple views of t...
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10/13/2021
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program
We study non-convex subgradient flows for training two-layer ReLU neural...
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10/13/2021
Parallel Deep Neural Networks Have Zero Duality Gap
Training deep neural networks is a well-known highly non-convex problem....
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10/12/2021
Predicting the Stereoselectivity of Chemical Transformations by Machine Learning
Stereoselective reactions (both chemical and enzymatic reactions) have b...
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07/01/2021
Reparameterized Sampling for Generative Adversarial Networks
Recently, sampling methods have been successfully applied to enhance the...
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05/15/2021
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
We propose a randomized algorithm with quadratic convergence rate for co...
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02/22/2021
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have attracted more and more attenti...
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08/05/2020
Optimizing AD Pruning of Sponsored Search with Reinforcement Learning
Industrial sponsored search system (SSS) can be logically divided into t...
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07/02/2020
Decoder-free Robustness Disentanglement without (Additional) Supervision
Adversarial Training (AT) is proposed to alleviate the adversarial vulne...
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06/25/2020
Experimental validation of a phase-field model to predict coarsening dynamics of lipid domains in multicomponent membranes
Membrane phase-separation is a mechanism that biological membranes often...
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11/01/2019
Regularized Non-negative Spectral Embedding for Clustering
Spectral Clustering is a popular technique to split data points into gro...
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09/04/2019
Accelerated Information Gradient flow
We present a systematic framework for the Nesterov's accelerated gradien...
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07/13/2017
