335 citations · 1k across the 30 of their papers we have counts for
12 papers · 1 filter
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals
Louis Blankemeier, Sebastien Baur, Wei-Hung Weng +5
Health-related acoustic signals, such as cough and breathing sounds, are relevant for medical diagnosis and continuous health monitoring. Most existing machine learning approaches…
AI Increases Global Access to Reliable Flood Forecasts
Grey Nearing, Deborah Cohen, Vusumuzi Dube +15
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely war…
Evaluating AI systems under uncertain ground truth: a case study in dermatology
David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy +17
For safety, medical AI systems undergo thorough evaluations before deployment, validating their predictions against a ground truth which is assumed to be fixed and certain. However…
Fast Inference from Transformers via Speculative Decoding
Yaniv Leviathan, Matan Kalman, Yossi Matias
Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model. In this work we introduce speculative decoding - an algor…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large,…
Adversarial Robustness of Streaming Algorithms through Importance Sampling
Vladimir Braverman, Avinatan Hassidim, Yossi Matias +3
In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more g…