92 citations · 365 across the 53 of their papers we have counts for
58 papers
TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series
Aditya Radhakrishnan, Maxwell A. Xu, Wanting Mao +3
Contrastive learning for time series depends on deciding which pairs of inputs should be treated as similar and dissimilar. Because few augmentations reliably preserve meaning in t…
Variable Input Masked Autoencoders for Wrist-Worn Accelerometry
Harish Haresamudram, Wanting Mao, Aditya Radhakrishnan +2
Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing models are pretrained using…
How Well Do Multimodal Models Reason on ECG Signals?
Maxwell A. Xu, Harish Haresamudram, Catherine W. Liu +11
While multimodal large language models offer a promising solution to the "black box" nature of health AI by generating interpretable reasoning traces, verifying the validity of the…
Self-Supervised Dynamical System Representations for Physiological Time-Series
Yenho Chen, Maxwell A. Xu, James M. Rehg +1
The effectiveness of self-supervised learning (SSL) for physiological time series depends on the ability of a pretraining objective to preserve information about the underlying phy…
Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals
Wanting Mao, Maxwell A Xu, Harish Haresamudram +3
Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide…
LSM-2: Learning from Incomplete Wearable Sensor Data
Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush +22
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffer…
Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings
Mithun Saha, Maxwell A. Xu, Wanting Mao +3
Photoplethysmography (PPG)-based foundation models are gaining traction due to the widespread use of PPG in biosignal monitoring and their potential to generalize across diverse he…
PyPulse: A Python Library for Biosignal Imputation
Kevin Gao, Maxwell A. Xu, James M. Rehg +1
We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. Missingness is commonplace in these settings and can arise from m…
RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
Maxwell A. Xu, Jaya Narain, Gregory Darnell +7
We present RelCon, a novel self-supervised Relative Contrastive learning approach for training a motion foundation model from wearable accelerometry sensors. First, a learnable dis…
Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
Hui Wei, Maxwell A. Xu, Colin Samplawski +3
Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subje…
Which way is `right'?: Uncovering limitations of Vision-and-Language Navigation model
Meera Hahn, Amit Raj, James M. Rehg
The challenging task of Vision-and-Language Navigation (VLN) requires embodied agents to follow natural language instructions to reach a goal location or object (e.g. `walk down th…
REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning
Maxwell A. Xu, Alexander Moreno, Hui Wei +2
The success of self-supervised contrastive learning hinges on identifying positive data pairs, such that when they are pushed together in embedding space, the space encodes useful…
Explaining a machine learning decision to physicians via counterfactuals
Supriya Nagesh, Nina Mishra, Yonatan Naamad +3
Machine learning models perform well on several healthcare tasks and can help reduce the burden on the healthcare system. However, the lack of explainability is a major roadblock t…
Listen to Look into the Future: Audio-Visual Egocentric Gaze Anticipation
Bolin Lai, Fiona Ryan, Wenqi Jia +2
Egocentric gaze anticipation serves as a key building block for the emerging capability of Augmented Reality. Notably, gaze behavior is driven by both visual cues and audio signals…
ShapeClipper: Scalable 3D Shape Learning from Single-View Images via Geometric and CLIP-based Consistency
Zixuan Huang, Varun Jampani, Anh Thai +3
We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images. Instead of relying on laborious 3D, multi-view or camera pose ann…
Egocentric Auditory Attention Localization in Conversations
Fiona Ryan, Hao Jiang, Abhinav Shukla +2
In a noisy conversation environment such as a dinner party, people often exhibit selective auditory attention, or the ability to focus on a particular speaker while tuning out othe…
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation
Maxwell A. Xu, Alexander Moreno, Supriya Nagesh +4
The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise h…
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
Stefan Stojanov, Anh Thai, Zixuan Huang +1
A hallmark of the deep learning era for computer vision is the successful use of large-scale labeled datasets to train feature representations for tasks ranging from object recogni…
Transformer-based Localization from Embodied Dialog with Large-scale Pre-training
Meera Hahn, James M. Rehg
We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who i…
In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation
Bolin Lai, Miao Liu, Fiona Ryan +1
In this paper, we present the first transformer-based model to address the challenging problem of egocentric gaze estimation. We observe that the connection between the global scen…
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
Zixuan Huang, Stefan Stojanov, Anh Thai +2
We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image. Our approach uses a set of single-view images…
Kernel Deformed Exponential Families for Sparse Continuous Attention
Alexander Moreno, Supriya Nagesh, Zhenke Wu +2
Attention mechanisms take an expectation of a data representation with respect to probability weights. This creates summary statistics that focus on important features. Recently, (…
Transformers for prompt-level EMA non-response prediction
Supriya Nagesh, Alexander Moreno, Stephanie M. Carpenter +9
Ecological Momentary Assessments (EMAs) are an important psychological data source for measuring current cognitive states, affect, behavior, and environmental factors from particip…
Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling
Yu-Ying Liu, Alexander Moreno, Maxwell A. Xu +7
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in…