Sparsely-gated Mixture of Expert (MoE), an emerging deep model architect...
Backward propagation (BP) is widely used to compute the gradients in neu...
Sequential recommendation demonstrates the capability to recommend items...
Recently, bi-level optimization (BLO) has taken center stage in some ver...
In deep learning, mixture-of-experts (MoE) activates one or few experts
...
The outbreak of COVID-19 has led to a global surge of Sinophobia partly
...
Despite the record-breaking performance in Text-to-Image (T2I) generatio...
Adversarial robustness is a key concept in measuring the ability of neur...
Numerous adversarial attack methods have been developed to generate
impe...
In this paper, we study the problem of temporal video grounding (TVG), w...
Lifelong learning (LL) aims to improve a predictive model as the data so...
Invariant risk minimization (IRM) has received increasing attention as a...
Vision Transformers (ViTs) with self-attention modules have recently ach...
Due to the significant computational challenge of training large-scale g...
Interpreting machine learning models is challenging but crucial for ensu...
Recent studies on backdoor attacks in model training have shown that
pol...
Current deep learning models often suffer from catastrophic forgetting o...
Many real-world problems not only have complicated nonconvex functional
...
We integrate contrastive learning (CL) with adversarial learning to
co-o...
We revisit and advance visual prompting (VP), an input prompting techniq...
In this paper, we propose a data-model-hardware tri-design framework for...
In this work, we leverage visual prompting (VP) to improve adversarial
r...
The deployment constraints in practical applications necessitate the pru...
This work tackles a central machine learning problem of performance
degr...
Graph convolutional networks (GCNs) have recently achieved great empiric...
Objective The evaluation of natural language processing (NLP) models for...
Class-incremental learning (CIL) suffers from the notorious dilemma betw...
Certifiable robustness is a highly desirable property for adopting deep
...
Current deep neural networks (DNNs) are vulnerable to adversarial attack...
Trojan attacks threaten deep neural networks (DNNs) by poisoning them to...
More and more investors and machine learning models rely on social media...
Single-particle cryo-electron microscopy (cryo-EM) has become one of the...
Image manipulation detection algorithms are often trained to discriminat...
The lack of adversarial robustness has been recognized as an important i...
It has been well recognized that neural network based image classifiers ...
Selecting an appropriate optimizer for a given problem is of major inter...
Adversarial robustness studies the worst-case performance of a machine
l...
Self-training, a semi-supervised learning algorithm, leverages a large a...
Machine learning has become successful in solving wireless interference
...
Adversarial training (AT) has become a widely recognized defense mechani...
As a seminal tool in self-supervised representation learning, contrastiv...
Contrastive learning (CL) can learn generalizable feature representation...
This work proposes a novel Deep Neural Network (DNN) quantization framew...
The lottery ticket hypothesis (LTH) states that learning on a
properly p...
We propose a new computationally-efficient first-order algorithm for
Mod...
Motivated by the recent discovery that the interpretation maps of CNNs c...
There have been long-standing controversies and inconsistencies over the...
K-Nearest Neighbor (kNN)-based deep learning methods have been applied t...
Continual learning of new knowledge over time is one desirable capabilit...
Prior works on formalizing explanations of a graph neural network (GNN) ...