3 citations · 4 across the 8 of their papers we have counts for
8 papers
DO-CGI: deep-optimized illumination patterns for computational ghost imaging at low sampling ratios
Mor Hale, Ofir Lindenbaum, Eliahu Cohen
Computational ghost imaging (CGI) reconstructs objects from known illumination patterns and bucket-detector measurements, but quality deteriorates at low sampling ratios (SRs). We…
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…
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…
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…
Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction
Idan Cohen, Ofir Lindenbaum, Sharon Gannot
Classical methods for acoustic scene mapping require the estimation of time difference of arrival (TDOA) between microphones. Unfortunately, TDOA estimation is very sensitive to re…