261 citations · 471 across the 20 of their papers we have counts for
20 papers
Mapping the Increasing Use of LLMs in Scientific Papers
Weixin Liang, Yaohui Zhang, Zhengxuan Wu +11
Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledg…
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
Weixin Liang, Zachary Izzo, Yaohui Zhang +9
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum l…
Data Reconstruction Attacks and Defenses: A Systematic Evaluation
Sheng Liu, Zihan Wang, Yuxiao Chen +1
Reconstruction attacks and defenses are essential in understanding the data leakage problem in machine learning. However, prior work has centered around empirical observations of g…
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Sheng Liu, Haotian Ye, Lei Xing +1
Large language models (LLMs) demonstrate emergent in-context learning capabilities, where they adapt to new tasks based on example demonstrations. However, in-context learning has…
Channel-Wise Contrastive Learning for Learning with Noisy Labels
Hui Kang, Sheng Liu, Huaxi Huang +1
In real-world datasets, noisy labels are pervasive. The challenge of learning with noisy labels (LNL) is to train a classifier that discerns the actual classes from given instances…
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels
Hui Kang, Sheng Liu, Huaxi Huang +4
In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean da…
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
Huaxi Huang, Hui Kang, Sheng Liu +4
Convolutional Neural Networks (CNNs) have demonstrated superiority in learning patterns, but are sensitive to label noises and may overfit noisy labels during training. The early s…
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse
Xiao Li, Sheng Liu, Jinxin Zhou +4
With the ever-increasing complexity of large-scale pre-trained models coupled with a shortage of labeled data for downstream training, transfer learning has become the primary appr…
Avoiding spurious correlations via logit correction
Sheng Liu, Xu Zhang, Nitesh Sekhar +3
Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labe…
Are All Losses Created Equal: A Neural Collapse Perspective
Jinxin Zhou, Chong You, Xiao Li +4
While cross entropy (CE) is the most commonly used loss to train deep neural networks for classification tasks, many alternative losses have been developed to obtain better empiric…
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
Kangning Liu, Weicheng Zhu, Yiqiu Shen +4
Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown p…
On Learning Contrastive Representations for Learning with Noisy Labels
Li Yi, Sheng Liu, Qi She +2
Deep neural networks are able to memorize noisy labels easily with a softmax cross-entropy (CE) loss. Previous studies attempted to address this issue focus on incorporating a nois…
Robust Training under Label Noise by Over-parameterization
Sheng Liu, Zhihui Zhu, Qing Qu +1
Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, wh…
Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu +8
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained…
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
Sheng Liu, Kangning Liu, Weicheng Zhu +2
Deep learning in the presence of noisy annotations has been studied extensively in classification, but much less in segmentation tasks. In this work, we study the learning dynamics…
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Sheng Liu, Xiao Li, Yuexiang Zhai +4
Normalization techniques have become a basic component in modern convolutional neural networks (ConvNets). In particular, many recent works demonstrate that promoting the orthogona…
Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian +1
We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed…
On the design of convolutional neural networks for automatic detection of Alzheimer's disease
Sheng Liu, Chhavi Yadav, Carlos Fernandez-Granda +1
Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural ne…
Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems
Brett Bernstein, Sheng Liu, Chrysa Papadaniil +1
Extracting information from nonlinear measurements is a fundamental challenge in data analysis. In this work, we consider separable inverse problems, where the data are modeled as…
Time-Series Analysis via Low-Rank Matrix Factorization Applied to Infant-Sleep Data
Sheng Liu, Mark Cheng, Hayley Brooks +4
We propose a nonparametric model for time series with missing data based on low-rank matrix factorization. The model expresses each instance in a set of time series as a linear com…