747 citations · 2.1k across the 38 of their papers we have counts for
30 papers · 1 filter
SensorLM: Learning the Language of Wearable Sensors
Yuwei Zhang, Kumar Ayush, Siyuan Qiao +17
We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language. Despite its pervasive nature, aligning and…
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…
Scaling Wearable Foundation Models
Girish Narayanswamy, Xin Liu, Kumar Ayush +15
Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large vo…
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
Alessandro De Palma, Rudy Bunel, Alban Desmaison +4
We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. First, we propose novel bounding algorithms based o…
Autoencoding Variational Autoencoder
A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham +2
Does a Variational AutoEncoder (VAE) consistently encode typical samples generated from its decoder? This paper shows that the perhaps surprising answer to this question is `No'; a…
Towards transformation-resilient provenance detection of digital media
Jamie Hayes, Krishnamurthy, Dvijotham +4
Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are difficult to distinguish from natural signals, creating opportu…