History-Dependent Dynamics in a Generic Model of Ion Channels - An Analytic Study. - Daniel Soudry; Ron Meir (Frontiers Comput. Neurosci., 2010)

"Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter". - Dmitri B. Chklovskii; Daniel Soudry (NIPS, 2012)

Conductance-Based Neuron Models and the Slow Dynamics of Excitability. - Daniel Soudry; Ron Meir (Frontiers Comput. Neurosci., 2012)

Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights. - Daniel Soudry; Itay Hubara; Ron Meir (NIPS, 2014)

Diffusion approximation-based simulation of stochastic ion channels: which method to use? - Danilo Pezo; Daniel Soudry; Patricio Orio (Frontiers Comput. Neurosci., 2014)

The neuronal response at extended timescales: long-term correlations without long-term memory. - Daniel Soudry; Ron Meir (Frontiers Comput. Neurosci., 2014)

The neuronal response at extended timescales: a linearized spiking input-output relation. - Daniel Soudry; Ron Meir (Frontiers Comput. Neurosci., 2014)

Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation. - Zhiyong Cheng 001; Daniel Soudry; Zexi Mao; Zhen-zhong Lan (CoRR, 2015)

Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data. - Daniel Soudry; Suraj Keshri; Patrick Stinson; Min-hwan Oh; Garud Iyengar; Liam Paninski (PLoS Comput. Biol., 2015)

Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training. - Daniel Soudry; Dotan Di Castro; Asaf Gal; Avinoam Kolodny; Shahar Kvatinsky (IEEE Trans. Neural Networks Learn. Syst., 2015)

A fully analog memristor-based neural network with online gradient training. - Eyal Rosenthal; Sergey Greshnikov; Daniel Soudry; Shahar Kvatinsky (ISCAS, 2016)

Binarized Neural Networks. - Itay Hubara; Matthieu Courbariaux; Daniel Soudry; Ran El-Yaniv; Yoshua Bengio (NIPS, 2016)

No bad local minima: Data independent training error guarantees for multilayer neural networks. - Daniel Soudry; Yair Carmon (CoRR, 2016)

Train longer, generalize better: closing the generalization gap in large batch training of neural networks. - Elad Hoffer; Itay Hubara; Daniel Soudry (NIPS, 2017)

The Implicit Bias of Gradient Descent on Separable Data. - Daniel Soudry; Elad Hoffer; Nathan Srebro (CoRR, 2017)

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. - Itay Hubara; Matthieu Courbariaux; Daniel Soudry; Ran El-Yaniv; Yoshua Bengio (J. Mach. Learn. Res., 2017)

Multi-scale approaches for high-speed imaging and analysis of large neural populations. - Johannes Friedrich; Weijian Yang; Daniel Soudry; Yu Mu; Misha B. Ahrens; Rafael Yuste; Darcy S. Peterka; Liam Paninski (PLoS Comput. Biol., 2017)

Fix your classifier: the marginal value of training the last weight layer. - Elad Hoffer; Itay Hubara; Daniel Soudry (ICLR (Poster), 2018)

Exponentially vanishing sub-optimal local minima in multilayer neural networks. - Daniel Soudry; Elad Hoffer (ICLR (Workshop), 2018)

The Implicit Bias of Gradient Descent on Separable Data. - Daniel Soudry; Elad Hoffer; Mor Shpigel Nacson; Nathan Srebro (ICLR (Poster), 2018)

Characterizing Implicit Bias in Terms of Optimization Geometry. - Suriya Gunasekar; Jason D. Lee; Daniel Soudry; Nathan Srebro (ICML, 2018)

Scalable methods for 8-bit training of neural networks. - Ron Banner; Itay Hubara; Elad Hoffer; Daniel Soudry (NeurIPS, 2018)

Implicit Bias of Gradient Descent on Linear Convolutional Networks. - Suriya Gunasekar; Jason D. Lee; Daniel Soudry; Nati Srebro (NeurIPS, 2018)

Norm matters: efficient and accurate normalization schemes in deep networks. - Elad Hoffer; Ron Banner; Itay Golan; Daniel Soudry (NeurIPS, 2018)

On the Blindspots of Convolutional Networks. - Elad Hoffer; Shai Fine; Daniel Soudry (CoRR, 2018)

Convergence of Gradient Descent on Separable Data. - Mor Shpigel Nacson; Jason D. Lee; Suriya Gunasekar; Nathan Srebro; Daniel Soudry (CoRR, 2018)

Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning. - Chen Zeno; Itay Golan; Elad Hoffer; Daniel Soudry (CoRR, 2018)

ACIQ: Analytical Clipping for Integer Quantization of neural networks. - Ron Banner; Yury Nahshan; Elad Hoffer; Daniel Soudry (CoRR, 2018)

The Implicit Bias of Gradient Descent on Separable Data. - Daniel Soudry; Elad Hoffer; Mor Shpigel Nacson; Suriya Gunasekar; Nathan Srebro (J. Mach. Learn. Res., 2018)

Seizure pathways: A model-based investigation. - Philippa J. Karoly; Levin Kuhlmann; Daniel Soudry; David B. Grayden; Mark J. Cook; Dean R. Freestone (PLoS Comput. Biol., 2018)

Convergence of Gradient Descent on Separable Data. - Mor Shpigel Nacson; Jason D. Lee; Suriya Gunasekar; Pedro Henrique Pamplona Savarese; Nathan Srebro; Daniel Soudry (AISTATS, 2019)

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. - Mor Shpigel Nacson; Nathan Srebro; Daniel Soudry (AISTATS, 2019)

How do infinite width bounded norm networks look in function space? - Pedro Savarese; Itay Evron; Daniel Soudry; Nathan Srebro (COLT, 2019)

Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. - Mor Shpigel Nacson; Suriya Gunasekar; Jason D. Lee; Nathan Srebro; Daniel Soudry (ICML, 2019)

Post training 4-bit quantization of convolutional networks for rapid-deployment. - Ron Banner; Yury Nahshan; Daniel Soudry (NeurIPS, 2019)

A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off. - Yaniv Blumenfeld; Dar Gilboa; Daniel Soudry (NeurIPS, 2019)

Augment your batch: better training with larger batches. - Elad Hoffer; Tal Ben-Nun; Itay Hubara; Niv Giladi; Torsten Hoefler; Daniel Soudry (CoRR, 2019)

Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency. - Elad Hoffer; Berry Weinstein; Itay Hubara; Tal Ben-Nun; Torsten Hoefler; Daniel Soudry (CoRR, 2019)

MTJ-Based Hardware Synapse Design for Quantized Deep Neural Networks. - Tzofnat Greenberg-Toledo; Ben Perach; Daniel Soudry; Shahar Kvatinsky (CoRR, 2019)

Kernel and Rich Regimes in Overparametrized Models. - Blake E. Woodworth; Suriya Gunasekar; Jason D. Lee; Edward Moroshko; Pedro Savarese; Itay Golan; Daniel Soudry; Nathan Srebro (COLT, 2020)

The Knowledge Within: Methods for Data-Free Model Compression. - Matan Haroush; Itay Hubara; Elad Hoffer; Daniel Soudry (CVPR, 2020)

Augment Your Batch: Improving Generalization Through Instance Repetition. - Elad Hoffer; Tal Ben-Nun; Itay Hubara; Niv Giladi; Torsten Hoefler; Daniel Soudry (CVPR, 2020)

At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks? - Niv Giladi; Mor Shpigel Nacson; Elad Hoffer; Daniel Soudry (ICLR, 2020)

A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. - Greg Ongie; Rebecca Willett; Daniel Soudry; Nathan Srebro (ICLR, 2020)

Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? - Yaniv Blumenfeld; Dar Gilboa; Daniel Soudry (ICML, 2020)

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. - Edward Moroshko; Blake E. Woodworth; Suriya Gunasekar; Jason D. Lee; Nati Srebro; Daniel Soudry (NeurIPS, 2020)

Neural gradients are lognormally distributed: understanding sparse and quantized training. - Brian Chmiel; Liad Ben-Uri; Moran Shkolnik; Elad Hoffer; Ron Banner; Daniel Soudry (CoRR, 2020)

Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming. - Itay Hubara; Yury Nahshan; Yair Hanani; Ron Banner; Daniel Soudry (CoRR, 2020)

The Global Optimization Geometry of Shallow Linear Neural Networks. - Zhihui Zhu; Daniel Soudry; Yonina C. Eldar; Michael B. Wakin (J. Math. Imaging Vis., 2020)

Neural gradients are near-lognormal: improved quantized and sparse training. - Brian Chmiel; Liad Ben-Uri; Moran Shkolnik; Elad Hoffer; Ron Banner; Daniel Soudry (ICLR, 2021)

On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. - Shahar Azulay; Edward Moroshko; Mor Shpigel Nacson; Blake E. Woodworth; Nathan Srebro; Amir Globerson; Daniel Soudry (ICML, 2021)

Accurate Post Training Quantization With Small Calibration Sets. - Itay Hubara; Yury Nahshan; Yair Hanani; Ron Banner; Daniel Soudry (ICML, 2021)

Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling. - Niv Giladi; Zvika Ben-Haim; Sella Nevo; Yossi Matias; Daniel Soudry (NeurIPS, 2021)

Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. - Itay Hubara; Brian Chmiel; Moshe Island; Ron Banner; Joseph Naor; Daniel Soudry (NeurIPS, 2021)

The Implicit Bias of Minima Stability: A View from Function Space. - Rotem Mulayoff; Tomer Michaeli; Daniel Soudry (NeurIPS, 2021)

Statistical Testing for Efficient Out of Distribution Detection in Deep Neural Networks. - Matan Haroush; Tzviel Frostig; Ruth Heller; Daniel Soudry (CoRR, 2021)

Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning. - Brian Chmiel; Ron Banner; Elad Hoffer; Hilla Ben-Yaacov; Daniel Soudry (CoRR, 2021)

Task-Agnostic Continual Learning Using Online Variational Bayes With Fixed-Point Updates. - Chen Zeno; Itay Golan; Elad Hoffer; Daniel Soudry (Neural Comput., 2021)

Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability. - Aviv Tamar; Daniel Soudry; Ev Zisselman (AAAI, 2022)

How catastrophic can catastrophic forgetting be in linear regression? - Itay Evron; Edward Moroshko; Rachel A. Ward; Nathan Srebro; Daniel Soudry (COLT, 2022)

A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks. - Matan Haroush; Tzviel Frostig; Ruth Heller; Daniel Soudry (ICLR, 2022)

Implicit Bias of the Step Size in Linear Diagonal Neural Networks. - Mor Shpigel Nacson; Kavya Ravichandran; Nathan Srebro; Daniel Soudry (ICML, 2022)

Optimal Fine-Grained N: M sparsity for Activations and Neural Gradients. - Brian Chmiel; Itay Hubara; Ron Banner; Daniel Soudry (CoRR, 2022)

The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study. - Itay Evron; Ophir Onn; Tamar Weiss Orzech; Hai Azeroual; Daniel Soudry (AISTATS, 2023)

Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations. - Hagay Michaeli; Tomer Michaeli; Daniel Soudry (CVPR, 2023)

Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats. - Brian Chmiel; Ron Banner; Elad Hoffer; Hilla Ben-Yaacov; Daniel Soudry (ICLR, 2023)

Minimum Variance Unbiased N: M Sparsity for the Neural Gradients. - Brian Chmiel; Itay Hubara; Ron Banner; Daniel Soudry (ICLR, 2023)

The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks. - Mor Shpigel Nacson; Rotem Mulayoff; Greg Ongie; Tomer Michaeli; Daniel Soudry (ICLR, 2023)

Continual Learning in Linear Classification on Separable Data. - Itay Evron; Edward Moroshko; Gon Buzaglo; Maroun Khriesh; Badea Marjieh; Nathan Srebro; Daniel Soudry (ICML, 2023)

Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond. - Itai Kreisler; Mor Shpigel Nacson; Daniel Soudry; Yair Carmon (ICML, 2023)

DropCompute: simple and more robust distributed synchronous training via compute variance reduction. - Niv Giladi; Shahar Gottlieb; Moran Shkolnik; Asaf Karnieli; Ron Banner; Elad Hoffer; Kfir Y. Levy; Daniel Soudry (NeurIPS, 2023)

How do Minimum-Norm Shallow Denoisers Look in Function Space? - Chen Zeno; Greg Ongie; Yaniv Blumenfeld; Nir Weinberger; Daniel Soudry (NeurIPS, 2023)

Explore to Generalize in Zero-Shot RL. - Ev Zisselman; Itai Lavie; Daniel Soudry; Aviv Tamar (NeurIPS, 2023)

Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators. - Yaniv Blumenfeld; Itay Hubara; Daniel Soudry (ICLR, 2024)

The Joint Effect of Task Similarity and Overparameterization on Catastrophic Forgetting - An Analytical Model. - Daniel Goldfarb; Itay Evron; Nir Weinberger; Daniel Soudry; Paul Hand (ICLR, 2024)

How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers. - Gon Buzaglo; Itamar Harel; Mor Shpigel Nacson; Alon Brutzkus; Nathan Srebro; Daniel Soudry (ICML, 2024)

Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes. - Dan Qiao 002; Kaiqi Zhang 002; Esha Singh; Daniel Soudry; Yu-Xiang Wang 003 (NeurIPS, 2024)

Exponential Quantum Communication Advantage in Distributed Inference and Learning. - Dar Gilboa; Hagay Michaeli; Daniel Soudry; Jarrod R. McClean (NeurIPS, 2024)

Provable Tempered Overfitting of Minimal Nets and Typical Nets. - Itamar Harel; William Hoza; Gal Vardi; Itay Evron; Nati Srebro; Daniel Soudry (NeurIPS, 2024)

The Implicit Bias of Gradient Descent on Separable Multiclass Data. - Hrithik Ravi; Clayton Scott; Daniel Soudry; Yutong Wang 002 (NeurIPS, 2024)

Scaling FP8 training to trillion-token LLMs. - Maxim Fishman; Brian Chmiel; Ron Banner; Daniel Soudry (ICLR, 2025)

Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks. - Edan Kinderman; Itay Hubara; Haggai Maron; Daniel Soudry (Trans. Mach. Learn. Res., 2025)

Better Rates for Random Task Orderings in Continual Linear Models. - Itay Evron; Ran Levinstein; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Nathan Srebro (CoRR, 2025)

PLUMAGE: Probabilistic Low rank Unbiased Min Variance Gradient Estimator for Efficient Large Model Training. - Matan Haroush; Daniel Soudry (CoRR, 2025)

Temperature is All You Need for Generalization in Langevin Dynamics and other Markov Processes. - Itamar Harel; Yonathan Wolanowsky; Gal Vardi; Nathan Srebro; Daniel Soudry (CoRR, 2025)

FP4 All the Way: Fully Quantized Training of LLMs. - Brian Chmiel; Maxim Fishman; Ron Banner; Daniel Soudry (CoRR, 2025)

Optimal Rates in Continual Linear Regression via Increasing Regularization. - Ran Levinstein; Amit Attia; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Itay Evron (CoRR, 2025)

When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets. - Chen Zeno; Hila Manor; Greg Ongie; Nir Weinberger; Tomer Michaeli; Daniel Soudry (CoRR, 2025)

Tensor-Parallelism with Partially Synchronized Activations. - Itay Lamprecht; Asaf Karnieli; Yair Hanani; Niv Giladi; Daniel Soudry (CoRR, 2025)