18 citations · 21 across the 15 of their papers we have counts for
15 papers
CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG
Lang Zhou, Yingjian Chen, Shuxuan Li +2
Multi-turn information-seeking conversations require both multi-hop reasoning and long-range dependency tracking across turns. However, existing RAG systems typically represent con…
From Approximation to Emergence: A Theory of Deep Learning
Zhilin Zhao
Deep learning has outgrown any single mathematical explanation. From Approximation to Emergence develops a unified, proof-oriented account of modern deep learning theory, tracing a…
Bridging Domain Expertise and Generalization for Performance Estimation
Shuxuan Li, Zhilin Zhao, Quyu Kong +1
Performance estimation under distribution shift aims to predict how a model behaves on an unlabeled test set whose distribution differs from the training data, a scenario that requ…
UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference
Lang Zhou, Shuxuan Li, Zhuohao Li +3
Long-context inference remains challenging for large language models due to attention dilution and out-of-distribution degradation. Context selection mitigates this limitation by a…
Federated Neural Nonparametric Point Processes
Hui Chen, Xuhui Fan, Hengyu Liu +5
Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a…
ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks
Zhangkai Wu, Xuhui Fan, Jin Li +3
The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discret…
XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition
Kun-Yu Lin, Henghui Ding, Jia-Run Du +6
Inspired by the impressive success of image-text foundation models, recent works have proposed to adapt these foundation models to video data, leading to efficient and effective vi…
Distilling the Unknown to Unveil Certainty
Zhilin Zhao, Longbing Cao, Yixuan Zhang +2
Out-of-distribution (OOD) detection is critical for identifying test samples that deviate from in-distribution (ID) data, ensuring network robustness and reliability. This paper pr…
R-divergence for Estimating Model-oriented Distribution Discrepancy
Zhilin Zhao, Longbing Cao
Real-life data are often non-IID due to complex distributions and interactions, and the sensitivity to the distribution of samples can differ among learning models. Accordingly, a…
Dual Representation Learning for Out-of-Distribution Detection
Zhilin Zhao, Longbing Cao
To classify in-distribution samples, deep neural networks explore strongly label-related information and discard weakly label-related information according to the information bottl…
Out-of-distribution Detection by Cross-class Vicinity Distribution of In-distribution Data
Zhilin Zhao, Longbing Cao, Kun-Yu Lin
Deep neural networks for image classification only learn to map in-distribution inputs to their corresponding ground truth labels in training without differentiating out-of-distrib…
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection
Zhilin Zhao, Longbing Cao, Kun-Yu Lin
The discrepancy between in-distribution (ID) and out-of-distribution (OOD) samples can lead to \textit{distributional vulnerability} in deep neural networks, which can subsequently…
Gray Learning from Non-IID Data with Out-of-distribution Samples
Zhilin Zhao, Longbing Cao, Chang-Dong Wang
The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-IID datasets comprising both in- and out-of-distribution samples. In an i…
Mixture of Online and Offline Experts for Non-stationary Time Series
Zhilin Zhao, Longbing Cao, Yuanyu Wan
We consider a general and realistic scenario involving non-stationary time series, consisting of several offline intervals with different distributions within a fixed offline time…
Revealing the Distributional Vulnerability of Discriminators by Implicit Generators
Zhilin Zhao, Longbing Cao, Kun-Yu Lin
In deep neural learning, a discriminator trained on in-distribution (ID) samples may make high-confidence predictions on out-of-distribution (OOD) samples. This triggers a signific…