1 citations · 2 across the 5 of their papers we have counts for
6 papers
Revisiting Glorot Initialization for Long-Range Linear Recurrences
Noga Bar, Mariia Seleznova, Yotam Alexander +2
Proper initialization is critical for Recurrent Neural Networks (RNNs), particularly in long-range reasoning tasks, where repeated application of the same weight matrix can cause v…
ZOQO: Zero-Order Quantized Optimization
Noga Bar, Raja Giryes
The increasing computational and memory demands in deep learning present significant challenges, especially in resource-constrained environments. We introduce a zero-order quantize…
Diverse Subset Selection via Norm-Based Sampling and Orthogonality
Noga Bar, Raja Giryes
Large annotated datasets are crucial for the success of deep neural networks, but labeling data can be prohibitively expensive in domains such as medical imaging. This work tackles…
Group Orthogonalization Regularization For Vision Models Adaptation and Robustness
Yoav Kurtz, Noga Bar, Raja Giryes
As neural networks become deeper, the redundancy within their parameters increases. This phenomenon has led to several methods that attempt to reduce the correlation between convol…
Pruning at Initialization -- A Sketching Perspective
Noga Bar, Raja Giryes
The lottery ticket hypothesis (LTH) has increased attention to pruning neural networks at initialization. We study this problem in the linear setting. We show that finding a sparse…
Multiplicative Reweighting for Robust Neural Network Optimization
Noga Bar, Tomer Koren, Raja Giryes
Neural networks are widespread due to their powerful performance. Yet, they degrade in the presence of noisy labels at training time. Inspired by the setting of learning with exper…