1 citations · 3 across the 10 of their papers we have counts for
16 papers
TWIN-MoE: Two-stage Intra-Expert Imputation Network for Incomplete Multimodal Learning
Liang Nathan Zheng, Jiyuan Liu, Wenhao Liang +3
Recent Mixture-of-Experts (MoE) approaches address missing modalities either by routing each missingness pattern to a dedicated expert, which scales poorly as the number of modalit…
Spatial Supervision Without Attribution Optimization: Improving Post-Hoc Class Activation Maps via Box-Guided Evidence Routing
Wenhao Liang, Liangwei Nathan Zheng, Lin Yue +3
Post-hoc class activation maps (CAMs) are a standard tool for inspecting the evidence behind an image classifier's predictions, yet nothing in ordinary training encourages these ma…
Auditing Routing Entropy as an Uncertainty Signal in Attention-Residual Transformers
Wenhao Liang, Lin Yue, Wei Emma Zhang +3
Dynamic architectures leave a per-example routing trace beside each prediction, and diffuse routing is easy to read as a sign that the prediction is unreliable. We audit that readi…
Routing Probes Can Improve Without New Information: An Exact-Null Audit of Uncertainty Beyond Model Outputs
Wenhao Liang, Lin Yue, Wei Emma Zhang +3
Routing signals of modern vision transformers -- expert gates, attention-residual weights and halting scores -- often improve probes that predict whether the model is correct, and…
Signal-Routed Temperature Scaling: Low-Capacity Risk-Conditioned Calibration for Small Validation Budgets
Wenhao Liang, Liangwei Nathan Zheng, Lin Yue +4
When a classifier is recalibrated from only a few thousand held-out examples, the capacity of the calibration map becomes a statistical design choice rather than a purely architect…
How Much Can Reliability Drift Under a Fixed Confidence Distribution?
Wenhao Liang, Lin Yue, Wei Emma Zhang +3
A classifier's conditional accuracy can change while its confidence distribution stays exactly the same. We study the worst-case movement of the reliability relation under covariat…
Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey
Liangwei Nathan Zheng, Wei Emma Zhang, Olaf Maennel +2
Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across diverse modalities and tasks. Desp…
Probing Routing-Conditional Calibration in Attention-Residual Transformers
Wenhao Liang, Lin Yue, Wei Emma Zhang +4
Post-hoc calibration is usually evaluated as a function of logits or softmax confidence alone, even as routing-augmented architectures increasingly accompany predictions with sampl…
Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge
Dhaval Patel, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula +4
Competition retrospectives are useful when they explain what a leaderboard measured, how hidden evaluation changed conclusions, and which design patterns were rewarded. We revisit…
Calibration Attention: Learning Reliability-Aware Representations for Vision Transformers
Wenhao Liang, Wei Emma Zhang, Lin Yue +4
Most calibration methods operate at the logit level, implicitly assuming that miscalibration can be corrected without changing the underlying representation. We challenge this assu…
PostHoc FREE Calibrating on Kolmogorov Arnold Networks
Wenhao Liang, Wei Emma Zhang, Lin Yue +3
Kolmogorov Arnold Networks (KANs) are neural architectures inspired by the Kolmogorov Arnold representation theorem that leverage B Spline parameterizations for flexible, locally a…
Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots and Advancing Stability
Liangwewi Nathan Zheng, Wei Emma Zhang, Lin Yue +3
Kolmogorov-Arnold Neural Networks (KANs) have gained significant attention in the machine learning community. However, their implementation often suffers from poor training stabili…
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
Liangwei Nathan Zheng, Zhengyang Li, Chang George Dong +5
Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Tim…
Devil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinction
Liangwei Nathan Zheng, Chang George Dong, Wei Emma Zhang +3
Drug-drug interaction (DDI) identification is a crucial aspect of pharmacology research. There are many DDI types (hundreds), and they are not evenly distributed with equal chance…
Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment
Liangwei Nathan Zheng, Chang George Dong, Wei Emma Zhang +4
Large Language Models (LLMs) have demonstrated impressive performance in time series analysis and seems to understand the time temporal relationship well than traditional transform…
SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series
Chang George Dong, Liangwei Nathan Zheng, Weitong Chen +2
Time series classification (TSC) has emerged as a critical task in various domains, and deep neural models have shown superior performance in TSC tasks. However, these models are v…