176 citations · 394 across the 16 of their papers we have counts for
8 papers · 1 filter
Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control
Ali Taghibakhshi, Ruisi Cai, Saurav Muralidharan +17
Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiring separate training runs fo…
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…
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman, Ran El-Yaniv
We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce SelectiveNet, a deep neural architecture with an integrated rej…
Deep Active Learning with a Neural Architecture Search
Yonatan Geifman, Ran El-Yaniv
We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropria…
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
Yonatan Geifman, Guy Uziel, Ran El-Yaniv
We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertaint…
Deep Active Learning over the Long Tail
Yonatan Geifman, Ran El-Yaniv
This paper is concerned with pool-based active learning for deep neural networks. Motivated by coreset dataset compression ideas, we present a novel active learning algorithm that…
Selective Classification for Deep Neural Networks
Yonatan Geifman, Ran El-Yaniv
Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially signi…
The Prediction Advantage: A Universally Meaningful Performance Measure for Classification and Regression
Ran El-Yaniv, Yonatan Geifman, Yair Wiener
We introduce the Prediction Advantage (PA), a novel performance measure for prediction functions under any loss function (e.g., classification or regression). The PA is defined as…