5 citations · 10 across the 8 of their papers we have counts for
9 papers
TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
Liying Han, Kang Yang, Oliver Wang +9
Large language models (LLMs) and time-series language models (TSLMs) are increasingly applied to time-series question answering (TSQA). Unlike text-only QA, TSQA requires models to…
Spectral Predictability as a Fast Reliability Indicator for Time Series Forecasting Model Selection
Oliver Wang, Pengrui Quan, Kang Yang +1
Practitioners deploying time series forecasting models face a dilemma: exhaustively validating dozens of models is computationally prohibitive, yet choosing the wrong model risks p…
Can Time-Series Foundation Models Perform Building Energy Management Tasks?
Ozan Baris Mulayim, Pengrui Quan, Liying Han +4
Building energy management (BEM) tasks require processing and learning from a variety of time-series data. Existing solutions rely on bespoke task- and data-specific models to perf…
Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models: Capabilities and Challenges
Pengrui Quan, Brian Wang, Kang Yang +2
Spatiotemporal reasoning plays a key role in Cyber-Physical Systems (CPS). Despite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to rea…
Foundation Models for CPS-IoT: Opportunities and Challenges
Ozan Baris, Yizhuo Chen, Gaofeng Dong +9
Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things…
SensorBench: Benchmarking LLMs in Coding-Based Sensor Processing
Pengrui Quan, Xiaomin Ouyang, Jeya Vikranth Jeyakumar +3
Effective processing, interpretation, and management of sensor data have emerged as a critical component of cyber-physical systems. Traditionally, processing sensor data requires p…
On the amplification of security and privacy risks by post-hoc explanations in machine learning models
Pengrui Quan, Supriyo Chakraborty, Jeya Vikranth Jeyakumar +1
A variety of explanation methods have been proposed in recent years to help users gain insights into the results returned by neural networks, which are otherwise complex and opaque…
Towards Imperceptible Query-limited Adversarial Attacks with Perceptual Feature Fidelity Loss
Pengrui Quan, Ruiming Guo, Mani Srivastava
Recently, there has been a large amount of work towards fooling deep-learning-based classifiers, particularly for images, via adversarial inputs that are visually similar to the be…
Efficient Optimization Methods for Extreme Similarity Learning with Nonlinear Embeddings
Bowen Yuan, Yu-Sheng Li, Pengrui Quan +1
We study the problem of learning similarity by using nonlinear embedding models (e.g., neural networks) from all possible pairs. This problem is well-known for its difficulty of tr…