6 citations · 11 across the 8 of their papers we have counts for
8 papers
CAMEL: An ECG Language Model for Forecasting Cardiac Events
Neelay Velingker, Alaia Solko-Breslin, Mayank Keoliya +9
Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing cardiovascular conditions. ECG language models (ELMs) have recently emerged as a pr…
ESCA: Contextualizing Embodied Agents via Scene-Graph Generation
Jiani Huang, Amish Sethi, Matthew Kuo +6
Multi-modal large language models (MLLMs) are making rapid progress toward general-purpose embodied agents. However, existing MLLMs do not reliably capture fine-grained links betwe…
Once Upon an Input: Reasoning via Per-Instance Program Synthesis
Adam Stein, Neelay Velingker, Mayur Naik +1
Large language models (LLMs) excel at zero-shot inference but continue to struggle with complex, multi-step reasoning. Recent methods that augment LLMs with intermediate reasoning…
The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models
Adam Stein, Aaditya Naik, Neelay Velingker +2
Neuro-symbolic learning was proposed to address challenges with training neural networks for complex reasoning tasks with the added benefits of interpretability, reliability, and e…
Relational Programming with Foundation Models
Ziyang Li, Jiani Huang, Jason Liu +6
Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them w…
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
Yinjun Wu, Mayank Keoliya, Kan Chen +7
Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical sett…
Data-Efficient Learning with Neural Programs
Alaia Solko-Breslin, Seewon Choi, Ziyang Li +4
Many computational tasks can be naturally expressed as a composition of a DNN followed by a program written in a traditional programming language or an API call to an LLM. We call…
PolyDroid: Learning-Driven Specialization of Mobile Applications
Brian Heath, Neelay Velingker, Osbert Bastani +1
The increasing prevalence of mobile apps has led to a proliferation of resource usage scenarios in which they are deployed. This motivates the need to specialize mobile apps based…