research
∙
08/19/2023
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Sparsely-gated Mixture of Expert (MoE), an emerging deep model architect...
share
research
∙
08/18/2023
Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks
Backward propagation (BP) is widely used to compute the gradients in neu...
research
∙
08/14/2023
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sequential recommendation demonstrates the capability to recommend items...
research
∙
08/01/2023
An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning
Recently, bi-level optimization (BLO) has taken center stage in some ver...
research
∙
06/07/2023
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks
In deep learning, mixture-of-experts (MoE) activates one or few experts ...
research
∙
04/07/2023
Detecting Chinese Fake News on Twitter during the COVID-19 Pandemic
The outbreak of COVID-19 has led to a global surge of Sinophobia partly ...
research
∙
03/29/2023
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion
Despite the record-breaking performance in Text-to-Image (T2I) generatio...
research
∙
03/17/2023
Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified ℓ_p Attacks
Adversarial robustness is a key concept in measuring the ability of neur...
research
∙
03/13/2023
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?
Numerous adversarial attack methods have been developed to generate impe...
research
∙
03/09/2023
Text-Visual Prompting for Efficient 2D Temporal Video Grounding
In this paper, we study the problem of temporal video grounding (TVG), w...
research
∙
03/07/2023
Robustness-preserving Lifelong Learning via Dataset Condensation
Lifelong learning (LL) aims to improve a predictive model as the data so...
research
∙
03/04/2023
What Is Missing in IRM Training and Evaluation? Challenges and Solutions
Invariant risk minimization (IRM) has received increasing attention as a...
research
∙
02/12/2023
A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity
Vision Transformers (ViTs) with self-attention modules have recently ach...
research
∙
02/06/2023
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Due to the significant computational challenge of training large-scale g...
research
∙
01/26/2023
Certified Interpretability Robustness for Class Activation Mapping
Interpreting machine learning models is challenging but crucial for ensu...
research
∙
01/20/2023
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning
Recent studies on backdoor attacks in model training have shown that pol...
research
∙
01/18/2023
Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning
Current deep learning models often suffer from catastrophic forgetting o...
research
∙
12/19/2022
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization
Many real-world problems not only have complicated nonconvex functional ...
research
∙
11/21/2022
CLAWSAT: Towards Both Robust and Accurate Code Models
We integrate contrastive learning (CL) with adversarial learning to co-o...
research
∙
11/21/2022
Understanding and Improving Visual Prompting: A Label-Mapping Perspective
We revisit and advance visual prompting (VP), an input prompting techniq...
research
∙
10/16/2022
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices
In this paper, we propose a data-model-hardware tri-design framework for...
research
∙
10/12/2022
Visual Prompting for Adversarial Robustness
In this work, we leverage visual prompting (VP) to improve adversarial r...
research
∙
10/08/2022
Advancing Model Pruning via Bi-level Optimization
The deployment constraints in practical applications necessitate the pru...
research
∙
09/09/2022
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
This work tackles a central machine learning problem of performance degr...
research
∙
07/07/2022
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Graph convolutional networks (GCNs) have recently achieved great empiric...
research
∙
06/28/2022
The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models
Objective The evaluation of natural language processing (NLP) models for...
research
∙
06/15/2022
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning
Class-incremental learning (CIL) suffers from the notorious dilemma betw...
research
∙
06/15/2022
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Certifiable robustness is a highly desirable property for adopting deep ...
research
∙
06/13/2022
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Current deep neural networks (DNNs) are vulnerable to adversarial attack...
research
∙
05/24/2022
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free
Trojan attacks threaten deep neural networks (DNNs) by poisoning them to...
research
∙
05/01/2022
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction
More and more investors and machine learning models rely on social media...
research
∙
04/15/2022
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection
Single-particle cryo-electron microscopy (cryo-EM) has become one of the...
research
∙
03/29/2022
Proactive Image Manipulation Detection
Image manipulation detection algorithms are often trained to discriminat...
research
∙
03/27/2022
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
The lack of adversarial robustness has been recognized as an important i...
research
∙
03/26/2022
Reverse Engineering of Imperceptible Adversarial Image Perturbations
It has been well recognized that neural network based image classifiers ...
research
∙
03/12/2022
Optimizer Amalgamation
Selecting an appropriate optimizer for a given problem is of major inter...
research
∙
02/15/2022
Holistic Adversarial Robustness of Deep Learning Models
Adversarial robustness studies the worst-case performance of a machine l...
research
∙
01/21/2022
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large a...
research
∙
12/28/2021
To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
Machine learning has become successful in solving wireless interference ...
research
∙
12/23/2021
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
Adversarial training (AT) has become a widely recognized defense mechani...
research
∙
12/08/2021
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
As a seminal tool in self-supervised representation learning, contrastiv...
research
∙
11/01/2021
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
Contrastive learning (CL) can learn generalizable feature representation...
research
∙
10/30/2021
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
This work proposes a novel Deep Neural Network (DNN) quantization framew...
research
∙
10/12/2021
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
The lottery ticket hypothesis (LTH) states that learning on a properly p...
research
∙
09/15/2021
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD
We propose a new computationally-efficient first-order algorithm for Mod...
research
∙
07/04/2021
Certifiably Robust Interpretation via Renyi Differential Privacy
Motivated by the recent discovery that the interpretation maps of CNNs c...
research
∙
07/01/2021
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
There have been long-standing controversies and inconsistencies over the...
research
∙
06/27/2021
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense
K-Nearest Neighbor (kNN)-based deep learning methods have been applied t...
research
∙
04/28/2021
Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors
Continual learning of new knowledge over time is one desirable capabilit...
research
∙
03/25/2021
