3 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates
Itamar Tsayag, Ofir Lindenbaum
Over-parameterized neural networks incur prohibitive memory and computational costs for resource-constrained deployment. The Strong Lottery Ticket (SLT) hypothesis suggests that ra…
Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings
Erel Naor, Ofir Lindenbaum
Deep neural networks often under-perform on tabular data due to their sensitivity to irrelevant features and a spectral bias toward smooth, low-frequency functions. These limitatio…
ICR-RL: Deep Reinforcement Learning via In-Context Regression
David Schiff, Ofir Lindenbaum, Yonathan Efroni
Recent advancements in machine learning have largely been driven by foundation models (FMs) trained on large, diverse datasets, enabling them to generalize effectively to new, rela…
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels
Yuval Grinberg, Nimrod Harel, Jacob Goldberger +1
Falsely annotated samples, also known as noisy labels, can significantly harm the performance of deep learning models. Two main approaches for learning with noisy labels are global…
Neuronal Cell Type Classification using Deep Learning
Ofek Ophir, Orit Shefi, Ofir Lindenbaum
The brain is likely the most complex organ, given the variety of functions it controls, the number of cells it comprises, and their corresponding diversity. Studying and identifyin…