75 citations · 147 across the 12 of their papers we have counts for
12 papers
Understanding Evaluation Illusion in Diffusion Large Language Models
Hengxiang Zhang, Jiaxi Ren, Renchunzi Xie +1
Despite the capability of parallel decoding, diffusion large language models (dLLMs) require many denoising steps to maintain generation quality, motivating recent research on effi…
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov +3
Leveraging the models' outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples w…
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov +3
Estimating the test performance of a model, possibly under distribution shift, without having access to the ground-truth labels is a challenging, yet very important problem for the…
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Renchunzi Xie, Hongxin Wei, Lei Feng +2
Estimating the generalization performance is practically challenging on out-of-distribution (OOD) data without ground-truth labels. While previous methods emphasize the connection…
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
Hongxin Wei, Huiping Zhuang, Renchunzi Xie +4
In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks. Cross Entropy (CE) loss has been s…
Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
Hongxin Wei, Lue Tao, Renchunzi Xie +2
Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance. Recent studies found that directly training with out-of-distribution dat…
Mitigating Neural Network Overconfidence with Logit Normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng +3
Detecting out-of-distribution inputs is critical for safe deployment of machine learning models in the real world. However, neural networks are known to suffer from the overconfide…
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection
Huiping Zhuang, Zhenyu Weng, Hongxin Wei +3
Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss…
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation
Renchunzi Xie, Hongxin Wei, Lei Feng +1
This paper studies weakly supervised domain adaptation(WSDA) problem, where we only have access to the source domain with noisy labels, from which we need to transfer useful inform…
Automatic Online Multi-Source Domain Adaptation
Renchunzi Xie, Mahardhika Pratama
Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions of each stream but also because of rapidly changing and…
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
Hongxin Wei, Lue Tao, Renchunzi Xie +1
Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for…
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
Mahardhika Pratama, Marcus de Carvalho, Renchunzi Xie +2
Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the ta…