From the 1 of 6 linked papers with an AI index.
6 papers
DO-CGI: deep-optimized illumination patterns for computational ghost imaging at low sampling ratios
Mor Hale, Ofir Lindenbaum, Eliahu Cohen
The paper introduces a deep‑learning framework that designs optimized illumination patterns for computational ghost imaging, achieving higher image quality at very low sampling rat…
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…
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…
Supervised Embedded Methods for Hyperspectral Band Selection
Yaniv Zimmer, Ofir Lindenbaum, Oren Glickman
Hyperspectral Imaging (HSI) captures rich spectral information across contiguous wavelength bands, supporting applications in precision agriculture, environmental monitoring, and a…
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…