Zepeng Huo — accepted AI-conference papers

2024

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo Reis, Rahul Thapa +24

AAAI 2024technical

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains chall…

Cited by 67 Source ↗PDF ↗Scholar ↗

2023

INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis

Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg, Chia-Chun Chiang, Matthew P. Lungren, Curtis Langlotz +3

NeurIPS 2023poster

Synthesizing information from various data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus on single-modality data due to a lack of publicly available, multimodal medical datasets. To address this limitation, we…

Cited by 11 Source ↗PDF ↗Scholar ↗

2022

Dynimp: Dynamic Imputation for Wearable Sensing Data through Sensory and Temporal Relatedness

Zepeng Huo, Taowei Ji, Yifei Liang, Shuai Huang, Zhangyang Wang, Xiaoning Qian +1

ICASSP 2022accepted

In wearable sensing applications, data is inevitable to be irregularly sampled or partially missing, which pose challenges for any downstream application. An unique aspect of wearable data is that it is time-series data and each channel can be correlated to another one, such as x, y, z axis of accel…

Cited by 0 Source ↗Scholar ↗

2022

VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty

Randy Ardywibowo, Zepeng Huo, Zhangyang Wang, Bobak J Mortazavi, Shuai Huang, Xiaoning Qian

ICML 2022spotlight

Continual Learning (CL) is the problem of sequentially learning a set of tasks and preserving all the knowledge acquired. Many existing methods assume that the data stream is explicitly divided into a sequence of known contexts (tasks), and use this information to know when to transfer knowledge fro…

Cited by 13 Source ↗PDF ↗Scholar ↗

2020

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

Zepeng Huo, Arash PakBin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian +3

AISTATS 2020poster

Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm. We de…

Cited by 19 Source ↗PDF ↗Scholar ↗