**For a full list see bellow or go to Google Scholar.** The sign * indicates equal contribution of the authors.

We find an analytical relation between compute time properties and scalability limitations, caused by the compute variance of straggling workers in a distributed setting. Then, we propose a simple yet effective decentralized method to reduce the variation among workers and thus improve the robustness of synchronous training.

*N. Giladi *, S. Gottlieb * , M. Shkolnik, A. Karnieli, R. Banner, E. Hoffer, K. Y. Levy, D. Soudry*

** **

We propose an extended anti-aliasing method that tackles both downsampling and non-linear layers, thus creating truly alias-free, shift-invariant CNNs.

*H. Michaeli, T. Michaeli, D. Soudry*

** See more details about this paper **

We study the type of solutions to which stochastic gradient descent converges when used to train a single hidden-layer multivariate ReLU network with the quadratic loss. Our results are based on a dynamical stability analysis.

*M. Shpigel Nacson, R. Mulayoff, G. Ongie, T. Michaeli, D. Soudry*

** **

Previous works separately showed that accurate 4-bit quantization of the neural gradients needs to (1) be unbiased and (2) have a log scale. However, no previous work aimed to combine both ideas, as we do in this work. Specifically, we examine the importance of having unbiased quantization in quantized neural network training, where to maintain it, and how to combine it with logarithmic.

*B. Chmiel, R. Banner, E. Hoffer, H. Ben Yaacov, D. Soudry*

** **

To better understand catastrophic forgetting, we study fitting an overparameterized linear model to a sequence of tasks with different input distributions. We analyze how much the model forgets the true labels of earlier tasks after training on subsequent tasks, obtaining exact expressions and bounds.

*I. Evron, E. Moroshko, R. Ward, N. Srebro, D. Soudry*

** **

We frame Out Of Distribution (OOD) detection in DNNs as a statistical hypothesis testing problem. Tests generated within our proposed framework combine evidence from the entire network.

*M. Haroush, T. Frostig, R. Heller, D. Soudry*

** **

In this work, we first suggest a new measure called mask-diversity which correlates with the expected accuracy of the different types of structural pruning.

*I. Hubara, B. Chmiel, M. Island, R. Banner, S. Naor, D. Soudry*

** See more details about this paper **

We find that the distribution of neural gradients is approximately lognormal. Considering this, we suggest two closed-form analytical methods to reduce the computational and memory burdens of neural gradients.

*B. Chmiel * , L. Ben-Uri * , M. Shkolnik, E. Hoffer, R. Banner, D. Soudry*

** **

We provide a detailed asymptotic study of gradient flow trajectories and their implicit optimization bias when minimizing the exponential loss over “diagonal linear networks”. This is the simplest model displaying a transition between “kernel” and non-kernel (“rich” or “active”) regimes.

*E. Moroshko, S. Gunasekar, B. Woodworth, J. D. Lee, N. Srebro, D. Soudry*

**NeurIPS 2020, Spotlight (3% acceptance rate) (2020)**

** **

Recently, an extensive amount of research has been focused on compressing and accelerating Deep Neural Networks (DNN). So far, high compression rate algorithms require part of the training dataset for a low precision calibration, or a fine-tuning process. However, this requirement is unacceptable when the data is unavailable or contains sensitive information, as in medical and biometric use-cases. We present three methods for generating synthetic samples from trained models.

*M. Haroush, I. Hubara, E. Hoffer, D. Soudry*

** **

We examine asynchronous training from the perspective of dynamical stability. We find that the degree of delay interacts with the learning rate, to change the set of minima accessible by an asynchronous stochastic gradient descent algorithm. We derive closed-form rules on how the learning rate could be changed, while keeping the accessible set the same.

*N. Giladi *, M. Shpigel Nacson *, E. Hoffer, D. Soudry*

** **

We apply mean-field techniques to networks with quantized activations in order to evaluate the degree to which quantization degrades signal propagation at initialization. We derive initialization schemes which maximize signal propagation in such networks and suggest why this is helpful for generalization.

*Y. Blumenfeld, D. Gilboa, D. Soudry*

** **

This paper introduces the first practical 4-bit post training quantization approach.

*R. Banner, Y. Nahshan, D. Soudry*

** See more details about this paper **

We consider the question of what functions can be captured by ReLU networks with an unbounded number of units (infinite width), but where the overall network Euclidean norm (sum of squares of all weights in the system, except for an unregularized bias term for each unit) is bounded.

*P. Savarese, I. Evron, D. Soudry, N. Srebro*

** **

Our theoretical analysis suggests that most of the training process is robust to substantial precision reduction, and points to only a few specific operations that require higher precision. Armed with this knowledge, we quantize the model parameters, activations and layer gradients to 8-bit, leaving at a higher precision only the final step in the computation of the weight gradients. Additionally, as QNNs require batch-normalization to be trained at high precision, we introduce Range Batch-Normalization (BN) which has significantly higher tolerance to quantization noise and improved computational complexity.

*R. Banner, I. Hubara, E. Hoffer, D. Soudry*

** **

We show that gradient descent on an unregularized logistic regression problem, for almost all separable datasets, converges to the same direction as the max-margin solution. The result generalizes also to other monotone decreasing loss functions with an infimum at infinity, and we also discuss a multi-class generalizations to the cross entropy loss. Furthermore, we show this convergence is very slow, and only logarithmic in the convergence of the loss itself.

** D. Soudry **, E. Hoffer, M. Shpigel Nacson, N. Srebro

** **

We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency.

*I. Hubara *, M. Courbariaux *, D. Soudry, R. El-Yaniv, Y. Bengio*

** **

How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers

*G. Buzaglo *, I. Harel *, M. Shpigel Nacson *, A. Brutzkus, N. Srebro, D. Soudry *

Arxiv

Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators

*Y. Blumenfeld, I. Hubara, D. Soudry *

ICLR 2024

The Joint Effect of Task Similarity and Overparameterization on Catastrophic Forgetting - An Analytical Model

*D. Goldfarb *, I. Evron *, N. Weinberger, D. Soudry, P. Hand *

ICLR 2024

How do Minimum-Norm Shallow Denoisers Look in Function Space?

*C. Zeno, G. Ongie, Y. Blumenfeld, N. Weinberger, D. Soudry *

NeurIPS 2023

DropCompute: simple and more robust distributed synchronous training via compute variance reduction

*N. Giladi *, S. Gottlieb * , M. Shkolnik, A. Karnieli, R. Banner, E. Hoffer, K. Y. Levy, D. Soudry *

NeurIPS 2023

Explore to Generalize in Zero-Shot RL

*E. Zisselman, I. Lavie, D. Soudry, A. Tamar *

NeurIPS 2023

Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond

*I Kreisler * , M. Shpigel Nacson * , D. Soudry, Yair Carmon *

ICML 2023

Continual Learning in Linear Classification on Separable Data

*I. Evron, E. Moroshko, G. Buzaglo, M. Khriesh, B. Marjieh, N. Srebro, D. Soudry *

ICML 2023

Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations

*H. Michaeli, T. Michaeli, D. Soudry *

CVPR 2023

The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks

*M. Shpigel Nacson, R. Mulayoff, G. Ongie, T. Michaeli, D. Soudry *

ICLR 2023

Optimal Fine-Grained N:M sparsity for Activations and Neural Gradients

*B. Chmiel, I. Hubara, R. Banner, D. Soudry *

ICLR 2023 (“notable top 25%” of accepted papers)

Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats

*B. Chmiel, R. Banner, E. Hoffer, H. Ben Yaacov, D. Soudry *

ICLR 2023

The Role of Codeword-to-Class Assignments in Error Correcting Codes: An Empirical Study

*I. Evron * , O. Onn * , T. Weiss, H. Azeroual, D. Soudry *

AISTAT 2023

How catastrophic can catastrophic forgetting be in linear regression?

*I. Evron, E. Moroshko, R. Ward, N. Srebro, D. Soudry *

COLT 2022

Implicit Bias of the Step Size in Linear Diagonal Neural Networks

*M. Shpigel-Nacson, K. Ravichandran, N. Srebro, D. Soudry *

ICML 2022

A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks

*M. Haroush, T. Frostig, R. Heller, D. Soudry *

ICLR 2022 (2022)

Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability

*A. Tamar, D. Soudry, E. Zisselman *

AAAI 2022 (15% acceptance rate)

Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks

*I. Hubara, B. Chmiel, M. Island, R. Banner, S. Naor, D. Soudry *

NeurIPS 2021 (2021)

Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling

*N. Giladi, Z. Ben-Haim, S. Nevo, Y. Matias, D. Soudry *

NeurIPS 2021

The Implicit Bias of Minima Stability: A View from Function Space

*R. Mulayoff, T. Michaeli, D. Soudry *

NeurIPS 2021

On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent

*S. Azulay, E. Moroshko, M. Shpigel Nacson, B. Woodworth, N. Srebro, A. Globerson, D. Soudry *

ICML 2021, Long talk (3% acceptance rate).

Accurate Post Training Quantization With Small Calibration Sets

*I. Hubara * , Y. Nahshan * , Y. Hanani*, R. Banner, D. Soudry *

ICML 2021

Neural gradients are near-lognormal: understanding sparse and quantized training

*B. Chmiel * , L. Ben-Uri * , M. Shkolnik, E. Hoffer, R. Banner, D. Soudry *

ICLR 2021 (2021)

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

*E. Moroshko, S. Gunasekar, B. Woodworth, J. D. Lee, N. Srebro, D. Soudry *

NeurIPS 2020, Spotlight (3% acceptance rate) (2020)

Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?

*Y. Blumenfeld, D. Gilboa, D. Soudry *

ICML 2020

Kernel and Rich Regimes in Overparametrized Models

*B. Woodworth, S. Gunasekar, P. Savarese, E. Moroshko, I. Golan, J. Lee, D. Soudry, N. Srebro *

COLT 2020

The Knowledge Within: Methods for Data-Free Model Compression

*M. Haroush, I. Hubara, E. Hoffer, D. Soudry *

CVPR 2020

Augment Your Batch: Improving Generalization Through Instance Repetition

*E. Hoffer, T. Ben-Nun, N. Giladi, I. Hubara, T. Hoefler, D. Soudry *

CVPR 2020

At Stability’s Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?

*N. Giladi *, M. Shpigel Nacson *, E. Hoffer, D. Soudry *

ICLR 2020

A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case

*G. Ongie, R. Willett, D. Soudry, N. Srebro *

ICLR 2020

A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off

*Y. Blumenfeld, D. Gilboa, D. Soudry *

NeurIPS 2019

Post-training 4-bit quantization of convolution networks for rapid-deployment

*R. Banner, Y. Nahshan, D. Soudry *

NeurIPS 2019

Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models

*M. Shpigel Nacson, S. Gunasekar, J. Lee, N. Srebro, D. Soudry *

ICML 2019

How do infinite width bounded norm networks look in function space?

*P. Savarese, I. Evron, D. Soudry, N. Srebro *

COLT 2019

Convergence of Gradient Descent on Separable Data

*M. Shpigel Nacson, J. Lee, S. Gunasekar, N. Srebro, D. Soudry *

AISTATS 2019, Oral Presentation (2.5% acceptance rate).

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate

*M. Shpigel Nacson, N. Srebro, D. Soudry *

AISTATS 2019

Norm matters: efficient and accurate normalization schemes in deep networks

*E. Hoffer * , R. Banner * , I. Golan * , D. Soudry *

NeurIPS 2018, Spotlight (3.5% acceptance rate)

Implicit Bias of Gradient Descent on Linear Convolutional Networks

*S. Gunasekar, J. D. Lee, D. Soudry, N. Srebro *

NeurIPS 2018

Scalable Methods for 8-bit Training of Neural Networks

*R. Banner, I. Hubara, E. Hoffer, D. Soudry *

NeurIPS 2018

Characterizing Implicit Bias in Terms of Optimization Geometry

*S. Gunasekar, J. Lee, D. Soudry, N. Srebro *

ICML 2018

The Implicit Bias of Gradient Descent on Separable Data

** D. Soudry **, E. Hoffer, M. Shpigel Nacson, N. Srebro

ICLR 2018 (2018)

Fix your classifier: the marginal value of training the last weight layer

*E. Hoffer, I. Hubara, D. Soudry *

ICLR 2018

Train longer, generalize better: closing the generalization gap in large batch training of neural networks

*E. Hoffer * , I. Hubara * , D. Soudry *

NIPS 2017, Oral presentation (1.2% acceptance rate)

Binarized Neural Networks

*I. Hubara *, M. Courbariaux *, D. Soudry, R. El-Yaniv, Y. Bengio *

NIPS 2016 (2016)

A Fully Analog Memristor-Based Multilayer Neural Network with Online Backpropagation Training

*S. Greshnikov, E. Rosenthal, D. Soudry, and S. Kvatinsky *

Proceeding of the IEEE International Conference on Circuits and Systems, pp. 1394-1397, 2016

Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous Or Discrete Weights

** D. Soudry **, I. Hubara and R. Meir

NIPS 2014

Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter

*D. B. Chklovskii and D. Soudry *

NIPS, 2012

Training of Quantized Deep Neural Networks using a Magnetic Tunnel Junction-Based Synapse

*T. Greenberg-Toledo, B. Perach, I. Hubara, D. Soudry, S. Kvatinsky *

to appear in Semiconductor Science and Technology, 2021

Task Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updates

*C. Zeno *, I. Golan *, E. Hoffer, D. Soudry *

Neural Computation, 2021

The Global Optimization Geometry of Shallow Linear Neural Networks

*Z. Zhu, D. Soudry, Y. C. Eldar, M. B. Wakin *

Journal of Mathematical Imaging and Vision, 2019

Seizure pathways: A model-based investigation

*P. J. Karoly, L. Kuhlmann, D. Soudry, D. B. Grayden, M. J. Cook, D. R. Freestone *

PLoS Comput Biol., vol. 14 no. 10, e1006403, 2018

The Implicit Bias of Gradient Descent on Separable Data

** D. Soudry **, E. Hoffer, M. Shpigel Nacson, S. Gunasekar, N. Srebro

JMLR, 2018

Bifurcation Analysis of Two Coupled Jansen-Rit Neural Mass Models

*S. Ahmadizadeh, P. Jane Karoly, D. Nesic, D. Br. Grayden, M. J.Cook, D. Soudry, D. R. Freestone *

PLOS One, vol. 13 no. 3, e0192842, 2018

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

*I. Hubara * , M. Courbariaux * , D. Soudry, R. El-Yaniv, Y. Bengio. *

JMLR, 2018

Multi-scale approaches for high-speed imaging and analysis of large neural populations

*J. Friedrich, W. Yang, D. Soudry, Y. Mu, M. B. Ahrens, R. Yuste, D. S. Peterka, L. Paninski *

PLos Comput Biol, vol., 13 no. 8, e1005685, 2017

Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

*Y. Dordek *, D. Soudry *, R. Meir, D. Derdikman *

eLife, vol. 5, e10094, 2016

Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

*E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau,T. Reardon,Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. M. Jessell, D. S. Peterka, R. Yuste, L. Paninski, *

Neuron, vol. 89, no. 2, 2016

Efficient ‘Shotgun’ Inference of Neural Connectivity from Highly Sub-sampled Activity Data

*D. Soudry, S. Keshri, P. Stinson, M.H. Oh, G. Iyengar, L. Paninski *

PLoS Comput Biol, vol. 11, no. 10, 2015

Memristor-based multilayer neural networks with online gradient descent training

** D. Soudry **, D. Di Castro, A. Gal, A. Kolodny, and S. Kvatinsky

IEEE TNNLS, vol. 26, no. 10, 2015

Diffusion approximation-based simulation of stochastic ion channels: which method to use?

*D. Pezo, D. Soudry, P. Orio *

Front. Comput. Neurosci., vol. 8, no. 139, 2014

The neuronal response at extended timescales: a linearized spiking input-output relation

** D. Soudry ** and R. Meir

Front. Comput. Neurosci., vol. 8, no. 29, 2014

The neuronal response at extended timescales: long term correlations without long memory

** D. Soudry ** and R. Meir

Front. Comput. Neurosci., vol. 8, no. 35, 2014

Simple, fast and accurate implementation of the diffusion approximation algorithm for stochastic ion channels with multiple states

*P. Orio and D. Soudry *

PLoS ONE, vol. 7, no. 5 p. e36670, 2012

Conductance-based neuron models and the slow dynamics of excitability

** D. Soudry ** and R. Meir

Front. Comput. Neurosci., vol. 6, no. 4, 2012

History-Dependent Dynamics in a Generic Model of Ion Channels–An Analytic Study

** D. Soudry ** and R. Meir

Front. Comput. Neurosci., vol. 4, Jan. 2010

*I. Hubara, D. Soudry, and R. El-Yaniv,*

Binarized Neural Networks

US Patent 10,831,444 (2020)

* D. Soudry, D. Di Castro, A. Gal, A. Kolodny, and S. Kvatinsky*

Analog Multiplier Using Memristor a Memristive Device and Methods for Implementing Hebbian Learning Rules Using Memristor Arrays

US Patent US9754203 B2 (2016)

Why Cold Posteriors? On the Suboptimal Generalization of Optimal Bayes Estimates

*C. Zeno, I. Golan, A. Pakman, D. Soudry *

Third Symposium on Advances in Approximate Bayesian Inference, contributed talk (2021).

How Learning Rate and Delay Affect Minima Selection in Asynchronous Training of Neural Networks: Toward Closing the Generalization Gap (Oral)

*N. Giladi * , Mor Shpigel * , E. Hoffer, D. Soudry *

ICML ‘Understanding and Improving Generalization in Deep Learning’ workshop (2019)

A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off (Oral)

*Y. Blumenfeld, D. Gilboa, D. Soudry *

ICML ‘Physics for deep learning’ workshop (2019)

Increasing batch size through instance repetition improves generalization (Poster)

*E. Hoffer, T. Ben-Nun, N. Giladi, I. Hubara, T. Hoefler, D. Soudry *

ICML ‘Understanding and Improving Generalization in Deep Learning’ workshop, poster (2019)

Task Agnostic Continual Learning Using Online Variational Bayes (Poster)

*C. Zeno *, I. Golan *, E. Hoffer, D. Soudry *

NIPS Deep Bayesian learning workshop, 2018

Infer2Train: leveraging inference for better training of deep networks (Poster)

*E Hoffer, B Weinstein, I Hubara, S Gofman, D Soudry *

NIPS Deep Bayesian learning workshop, 2018

Exponentially vanishing sub-optimal local minima in multilayer neural networks (Poster)

*D. Soudry, E. Hoffer *

ICLR workshop, 2018

Quantized Neural Networks (Poster)

*I. Hubara * , M. Courbariaux *, D. Soudry, R. El-Yaniv, Y. Bengio *

NIPS workshop on Efficient Methods for Deep Neural Networks (2016)

Quantized Neural Networks (Poster)

*I. Hubara * , M. Courbariaux *, D. Soudry, R. El-Yaniv, Y. Bengio *

NIPS workshop on Efficient Methods for Deep Neural Networks (2016)

Binarized neural networks (Poster)

*I. Hubara * , M. Courbariaux *, D. Soudry, R. El-Yaniv, Y. Bengio *

Machine Learning seminar, IBM research center, Haifa (2016)

Data-driven neural models part II: connectivity patterns of human seizures (Best student poster)

*P. J. Karoly, D. R. Freestone, D. Soudry, L. Kuhlmann, L. Paninski, M. Cook *

CNS (2016)

Data-driven neural models part I: state and parameter estimation (Poster)

*D. R. Freestone, P. J. Karoly, D. Soudry, L. Kuhlmann, M.Cook *

CNS (2016)

Extracting grid characteristics from spatially distributed place cell inputs using non-negative PCA (Poster)

*Y. Dordek * , D. Soudry * , R. Meir, D. Derdikman *

SFN (2015)

Fast Constrained Non-negative Matrix Factorization for Whole-Brain Calcium Imaging Data (Poster)

*J. Friedrich, D. Soudry, Y. Mu, J. Freeman, M. Ahrens, and L. Paninski *

NIPS workshop on Statistical Methods for Understanding Neural Systems (2015)

Implementing efficient ‘shotgun’ inference of neural connectivity from highly sub-sampled activity data (Spotlight Presentation and poster)

** D. Soudry **, S. Keshri, P. Stinson, M.H. Oh, G. Iyengar, L. Paninski

NIPS workshop on Modelling and Inference for Dynamics on Complex Interaction Networks (2015)

Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous Or Discrete Weights (Poster)

** D. Soudry **,I. Hubara and R. Meir

Machine Learning seminar (IBM research center, Haifa 2015)

Efficient “shotgun” inference of neural connectivity from highly sub-sampled activity data (Oral)”

** D. Soudry **, S. Keshri, P. Stinson, M.H. Oh, G. Iyengar, L. Paninski

Swartz Annual Meeting at Janelia Research Campus (2015)

A shotgun sampling solution for the common input problem in neural connectivity inference (Poster)

** D. Soudry **, S. Keshri, P. Stinson, M.H. Oh, G. Iyengar, L. Paninski

COSYNE (2015)

Whole Brain Region of Interest Detection (Poster)

*D. Pfau *, D. Soudry *, Y. Gao, Y. Mu, J. Freeman, M. Ahrens, L. Paninski *

NIPS workshop on Large scale optical physiology -From data-acquisition to models of neural coding (2014)

Whole Brain Region of Interest Detection (Poster)

*D. Pfau , D. Soudry , Y. Gao, Y. Mu, J. Freeman, M. Ahrens, L. Paninski *

AREADNE (2014)

Mean Field Bayes Backpropagation: scalable training of multilayer neural networks with discrete weights (Poster)

** D. Soudry **and R. Meir

Machine Learning seminar (IBM research center, Haifa 2013)

Implementing Hebbian Learning Rules with Memristors (Poster)

** D. Soudr **y, D. Di Castro, A. Gal, A. Kolodny, and S. Kvatinsky

Memristor-based Systems for Neuromorphic Applications (Torino University 2013)

A spiking input-output relation for general biophysical neuron models explains observed 1/f response (Poster)

** D. Soudry ** and R. Meir

COSYNE (2013)

Spiking input-output relation of general biophysical neuron models – exact analytic solutions and comparisons with experiment (Poster)

** D. Soudry ** and R. Meir

Variants and invariants in brain and behavior (Technion 2012)

The slow dynamics of neuronal excitability - exact analytic solutions for the response of general biophysical neuron models at long times, and comparisons with experiment (Poster)

** D. Soudry ** and R. Meir

Brain Plasticity Symposium (Tel Aviv university 2012)

The slow dynamics of neuronal excitability (Poster)

** D. Soudry ** and R. Meir

ISFN (2012)

The neuron as a population of ion channels: The emergence of stochastic and history dependent behavior (Poster)

** D. Soudry ** and R. Meir

COSYNE (2011)

The neuron as a population of ion channels: The emergence of stochastic and history dependent behavior (Oral)

** D. Soudry ** and R. Meir

ISFN (2010)

History dependent dynamics in ion channels - an analytic study (Poster)

** D. Soudry ** and R. Meir

COSYNE (2010)

Adapting Timescales: From Channel to Neuron” (Oral)

** D. Soudry ** and R. Meir

ISFN (2009)

Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency

*E. Hoffer, B. Weinstein, I. Hubara, T. Ben-Nun, T. Hoefler, D. Soudry *

See Here

On the Blindspots of Convolutional Networks

*E. Hoffer, S. Fine, D. Soudry *

See Here

No bad local minima: Data independent training error guarantees for multilayer neural networks

** D. Soudry **, E. Hoffer

See Here