63 citations · 150 across the 32 of their papers we have counts for
9 papers · 1 filter
Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks
Roy Turgeman, Tom Tirer
The data processing inequality is an information-theoretic principle stating that the information content of a signal cannot be increased by processing the observations. In particu…
Enhancing Conformal Prediction via Class Similarity
Ariel Fargion, Lahav Dabah, Tom Tirer
Conformal Prediction (CP) has emerged as a powerful statistical framework for high-stakes classification applications. Instead of predicting a single class, CP generates a predicti…
Jacobian-Aware Posterior Sampling for Inverse Problems
Liav Hen, Tom Tirer, Raja Giryes +1
Diffusion models provide powerful generative priors for solving inverse problems by sampling from a posterior distribution conditioned on corrupted measurements. Existing methods p…
(SP)-Net: A Neural Spatial Spectrum Method for DOA Estimation
Lioz Berman, Sharon Gannot, Tom Tirer
We consider the problem of estimating the directions of arrival (DOAs) of multiple sources from a single snapshot of an antenna array, a task with many practical applications. In s…
Efficient Conformal Prediction for Regression Models under Label Noise
Yahav Cohen, Jacob Goldberger, Tom Tirer
In high-stakes scenarios, such as medical imaging applications, it is critical to equip the predictions of a regression model with reliable confidence intervals. Recently, Conforma…
Catastrophic Forgetting Mitigation Through Plateau Phase Activity Profiling
Idan Mashiach, Oren Glickman, Tom Tirer
Catastrophic forgetting in deep neural networks occurs when learning new tasks degrades performance on previously learned tasks due to knowledge overwriting. Among the approaches t…
SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training
Yehonathan Refael, Guy Smorodinsky, Tom Tirer +1
Low-rank gradient-based optimization methods have significantly improved memory efficiency during the training of large language models (LLMs), enabling operations within constrain…
ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality
Matias Cosarinsky, Ramiro Billot, Lucas Mansilla +5
Assessing the quality of automatic image segmentation is crucial in clinical practice, but often very challenging due to the limited availability of ground truth annotations. Rever…
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM
Yehonathan Refael, Iftach Arbel, Ofir Lindenbaum +1
We study robust parameter-efficient fine-tuning (PEFT) techniques designed to improve accuracy and generalization while operating within strict computational and memory hardware co…