A statistical framework for efficient out of distribution detection in deep neural networks

no code implementations • ICLR 2022 • Matan Haroush, Tzviel Frostig, Ruth Heller, Daniel Soudry

Our method achieves comparable or better results than state-of-the-art methods on well-accepted OOD benchmarks, without retraining the network parameters or assuming prior knowledge on the test distribution -- and at a fraction of the computational cost.

Autonomous Vehicles Out-of-Distribution Detection +1

The Knowledge Within: Methods for Data-Free Model Compression

no code implementations • CVPR 2020 • Matan Haroush, Itay Hubara, Elad Hoffer, Daniel Soudry

Then, we demonstrate how these samples can be used to calibrate and fine-tune quantized models without using any real data in the process.

Model Compression

Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

no code implementations • NeurIPS 2018 • Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor

Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant.

Deep Reinforcement Learning reinforcement-learning +2