7 citations · 36 across the 24 of their papers we have counts for
10 papers · 1 filter
Where's the liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content
Haoyue Bai, Yiyou Sun, Wei Cheng +1
The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real…
TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
Yushan Jiang, Wenchao Yu, Geon Lee +5
Time series analysis provides essential insights for real-world system dynamics and informs downstream decision-making, yet most existing methods often overlook the rich contextual…
DISC: Dynamic Decomposition Improves LLM Inference Scaling
Jonathan Light, Wei Cheng, Benjamin Riviere +6
Inference scaling methods for LLMs often rely on decomposing problems into steps (or groups of tokens), followed by sampling and selecting the best next steps. However, these steps…
On the Effect of Sampling Diversity in Scaling LLM Inference
Tianchun Wang, Zichuan Liu, Yuanzhou Chen +5
Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it. Motivated by the observed…
Harnessing Vision Models for Time Series Analysis: A Survey
Jingchao Ni, Ziming Zhao, ChengAo Shen +5
Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs).…
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization
Yuyang Ye, Lu-An Tang, Haoyu Wang +5
Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity fo…
Parametric Augmentation for Time Series Contrastive Learning
Xu Zheng, Tianchun Wang, Wei Cheng +4
Modern techniques like contrastive learning have been effectively used in many areas, including computer vision, natural language processing, and graph-structured data. Creating po…
DyExplainer: Explainable Dynamic Graph Neural Networks
Tianchun Wang, Dongsheng Luo, Wei Cheng +2
Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box…
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
Xu Zheng, Farhad Shirani, Tianchun Wang +5
Graph Neural Networks (GNNs) are neural models that leverage the dependency structure in graphical data via message passing among the graph nodes. GNNs have emerged as pivotal arch…
Interpretable Imitation Learning with Dynamic Causal Relations
Tianxiang Zhao, Wenchao Yu, Suhang Wang +6
Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving…