4 citations · 14 across the 11 of their papers we have counts for
6 papers · 1 filter
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs
Akhiad Bercovich, Tomer Ronen, Talor Abramovich +23
Large language models (LLMs) offer remarkable capabilities, yet their high inference costs restrict wider adoption. While increasing parameter counts improves accuracy, it also bro…
Hierarchical Selective Classification
Shani Goren, Ido Galil, Ran El-Yaniv
Deploying deep neural networks for risk-sensitive tasks necessitates an uncertainty estimation mechanism. This paper introduces hierarchical selective classification, extending sel…
A framework for benchmarking class-out-of-distribution detection and its application to ImageNet
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
When deployed for risk-sensitive tasks, deep neural networks must be able to detect instances with labels from outside the distribution for which they were trained. In this paper w…
What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism. Here we examine the relationship between deep architectures and their…
Which models are innately best at uncertainty estimation?
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
Due to the comprehensive nature of this paper, it has been updated and split into two separate papers: "A Framework For Benchmarking Class-out-of-distribution Detection And Its App…
Disrupting Deep Uncertainty Estimation Without Harming Accuracy
Ido Galil, Ran El-Yaniv
Deep neural networks (DNNs) have proven to be powerful predictors and are widely used for various tasks. Credible uncertainty estimation of their predictions, however, is crucial f…