224 citations · 3.2k across the 170 of their papers we have counts for
10 papers · 1 filter
Memory Efficient Optimizers with 4-bit States
Bingrui Li, Jianfei Chen, Jun Zhu
Optimizer states are a major source of memory consumption for training neural networks, limiting the maximum trainable model within given memory budget. Compressing the optimizer s…
How Robust is Google's Bard to Adversarial Image Attacks?
Yinpeng Dong, Huanran Chen, Jiawei Chen +6
Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However,…
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Liyuan Wang, Xingxing Zhang, Qian Li +4
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory st…
Towards Accelerated Model Training via Bayesian Data Selection
Zhijie Deng, Peng Cui, Jun Zhu
Mislabeled, duplicated, or biased data in real-world scenarios can lead to prolonged training and even hinder model convergence. Traditional solutions prioritizing easy or hard sam…
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
Jiachen Yao, Chang Su, Zhongkai Hao +3
Physics-informed Neural Networks (PINNs) have recently achieved remarkable progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a weighted sum…
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
Zhongkai Hao, Jiachen Yao, Chang Su +8
While significant progress has been made on Physics-Informed Neural Networks (PINNs), a comprehensive comparison of these methods across a wide range of Partial Differential Equati…
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang +4
Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training.…
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving
Yinpeng Dong, Caixin Kang, Jinlai Zhang +6
3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to rea…
Confidence-based Reliable Learning under Dual Noises
Peng Cui, Yang Yue, Zhijie Deng +1
Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization. Yet,…
A Comprehensive Survey of Continual Learning: Theory, Method and Application
Liyuan Wang, Xingxing Zhang, Hang Su +1
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as co…