2 citations · 7 across the 17 of their papers we have counts for
19 papers
Decoder Design Matters for ECG Delineation
Joseph Scharpf, William Han, Chaojing Duan +3
Electrocardiogram (ECG) delineation identifies the boundaries of P waves, QRS complexes, and T waves, providing structural annotations that can guide AI models in learning to inter…
ELF: A Family of Encoder-Free ECG-Language Models
William Han, Tony Chen, Chaojing Duan +6
ECG-Language Models (ELMs) extend recent advances in Multimodal Large Language Models (MLLMs) to automated ECG interpretation. However, most existing ELMs inherit Vision-Language M…
Retrieval-Augmented Generation for Electrocardiogram-Language Models
Xiaoyu Song, William Han, Tony Chen +4
Interest in generative Electrocardiogram-Language Models (ELMs) is growing, as they can produce textual responses conditioned on ECG signals and textual queries. Unlike traditional…
Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework
William Han, Chaojing Duan, Zhepeng Cen +7
Recent advances have increasingly applied large language models (LLMs) to electrocardiogram (ECG) interpretation, giving rise to Electrocardiogram-Language Models (ELMs). Condition…
Behavior Injection: Preparing Language Models for Reinforcement Learning
Zhepeng Cen, Yihang Yao, William Han +2
Reinforcement learning (RL) has emerged as a powerful post-training technique to incentivize the reasoning ability of large language models (LLMs). However, LLMs can respond very i…
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction
Kai Li, Junhao Wang, William Han +1
Minimally invasive surgery (MIS) requires high-fidelity, real-time visual feedback of dynamic and low-texture surgical scenes. To address these requirements, we introduce FeatureEn…
Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety
Yuyou Zhang, Miao Li, William Han +3
Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit weaknesses in traditional safety alignment, which often relies on rigid refusal heuristics or represen…
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training
Yihang Yao, Zhepeng Cen, Miao Li +6
Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. However, even minor variations in query phrasing, despite preserving the underlyi…
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
William Han, Chaojing Duan, Michael A. Rosenberg +2
Large Language Models (LLMs) have demonstrated exceptional versatility across domains, including applications to electrocardiograms (ECGs). A growing body of work focuses on genera…
Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications
Xilun Zhang, Shiqi Liu, Peide Huang +4
Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies between simulated and real-world dynamics. Traditional methods like Domain Randomization o…
Evaluating Durability: Benchmark Insights into Multimodal Watermarking
Jielin Qiu, William Han, Xuandong Zhao +3
With the development of large models, watermarks are increasingly employed to assert copyright, verify authenticity, or monitor content distribution. As applications become more mu…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Entity6K: A Large Open-Domain Evaluation Dataset for Real-World Entity Recognition
Jielin Qiu, William Han, Winfred Wang +6
Open-domain real-world entity recognition is essential yet challenging, involving identifying various entities in diverse environments. The lack of a suitable evaluation dataset ha…
Interpretation of Intracardiac Electrograms Through Textual Representations
William Jongwon Han, Diana Gomez, Avi Alok +5
Understanding the irregular electrical activity of atrial fibrillation (AFib) has been a key challenge in electrocardiography. For serious cases of AFib, catheter ablations are per…
Embodied Executable Policy Learning with Language-based Scene Summarization
Jielin Qiu, Mengdi Xu, William Han +2
Large Language models (LLMs) have shown remarkable success in assisting robot learning tasks, i.e., complex household planning. However, the performance of pretrained LLMs heavily…
MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
Jielin Qiu, Jiacheng Zhu, William Han +9
Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless, numerous limitations exist within existing public MSMO datasets,…
Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report
Jielin Qiu, Jiacheng Zhu, Shiqi Liu +7
Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies. Despite the growing interest, most…
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
Jielin Qiu, William Han, Jiacheng Zhu +5
Recent advancements in Large Language Models (LLMs) have drawn increasing attention since the learned embeddings pretrained on large-scale datasets have shown powerful ability in v…
Can Brain Signals Reveal Inner Alignment with Human Languages?
William Han, Jielin Qiu, Jiacheng Zhu +4
Brain Signals, such as Electroencephalography (EEG), and human languages have been widely explored independently for many downstream tasks, however, the connection between them has…