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2020 – today
- 2022
[i39]Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry:
Optimal Fine-Grained N: M sparsity for Activations and Neural Gradients. CoRR abs/2203.10991 (2022)- [i38]Itay Evron, Edward Moroshko, Rachel Ward, Nati Srebro, Daniel Soudry:
How catastrophic can catastrophic forgetting be in linear regression? CoRR abs/2205.09588 (2022) - 2021
- [j13]Chen Zeno, Itay Golan, Elad Hoffer, Daniel Soudry:
Task-Agnostic Continual Learning Using Online Variational Bayes With Fixed-Point Updates. Neural Comput. 33(11): 3139-3177 (2021) - [c31]Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry:
Neural gradients are near-lognormal: improved quantized and sparse training. ICLR 2021 - [c30]Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry:
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. ICML 2021: 468-477 - [c29]Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, Daniel Soudry:
Accurate Post Training Quantization With Small Calibration Sets. ICML 2021: 4466-4475 - [c28]Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry:
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling. NeurIPS 2021: 1378-1389 - [c27]Rotem Mulayoff, Tomer Michaeli, Daniel Soudry:
The Implicit Bias of Minima Stability: A View from Function Space. NeurIPS 2021: 17749-17761 - [c26]Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Joseph Naor, Daniel Soudry:
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. NeurIPS 2021: 21099-21111 - [i37]Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Seffi Naor, Daniel Soudry:
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. CoRR abs/2102.08124 (2021) - [i36]Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry:
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. CoRR abs/2102.09769 (2021) - [i35]Matan Haroush, Tzivel Frostig, Ruth Heller, Daniel Soudry:
Statistical Testing for Efficient Out of Distribution Detection in Deep Neural Networks. CoRR abs/2102.12967 (2021) - [i34]Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry:
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling. CoRR abs/2106.07218 (2021) - [i33]Aviv Tamar, Daniel Soudry, Ev Zisselman:
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability. CoRR abs/2109.11792 (2021) - [i32]Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry:
Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning. CoRR abs/2112.10769 (2021) - 2020
- [j12]Zhihui Zhu
, Daniel Soudry, Yonina C. Eldar, Michael B. Wakin:
The Global Optimization Geometry of Shallow Linear Neural Networks. J. Math. Imaging Vis. 62(3): 279-292 (2020) - [c25]Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro:
Kernel and Rich Regimes in Overparametrized Models. COLT 2020: 3635-3673 - [c24]Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry:
Augment Your Batch: Improving Generalization Through Instance Repetition. CVPR 2020: 8126-8135 - [c23]Matan Haroush, Itay Hubara, Elad Hoffer, Daniel Soudry:
The Knowledge Within: Methods for Data-Free Model Compression. CVPR 2020: 8491-8499 - [c22]Niv Giladi, Mor Shpigel Nacson, Elad Hoffer, Daniel Soudry:
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks? ICLR 2020 - [c21]Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro:
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. ICLR 2020 - [c20]Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry:
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? ICML 2020: 960-969 - [c19]Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry:
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. NeurIPS 2020 - [i31]Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro:
Kernel and Rich Regimes in Overparametrized Models. CoRR abs/2002.09277 (2020) - [i30]Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry:
Neural gradients are lognormally distributed: understanding sparse and quantized training. CoRR abs/2006.08173 (2020) - [i29]Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, Daniel Soudry:
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming. CoRR abs/2006.10518 (2020) - [i28]Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry:
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? CoRR abs/2007.01038 (2020) - [i27]Edward Moroshko, Suriya Gunasekar, Blake E. Woodworth, Jason D. Lee, Nathan Srebro, Daniel Soudry:
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. CoRR abs/2007.06738 (2020) - [i26]Chen Zeno, Itay Golan, Elad Hoffer, Daniel Soudry:
Task Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updates. CoRR abs/2010.00373 (2020)
2010 – 2019
- 2019
- [c18]Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry:
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. AISTATS 2019: 3051-3059 - [c17]Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry:
Convergence of Gradient Descent on Separable Data. AISTATS 2019: 3420-3428 - [c16]Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro:
How do infinite width bounded norm networks look in function space? COLT 2019: 2667-2690 - [c15]Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry:
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. ICML 2019: 4683-4692 - [c14]Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry:
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off. NeurIPS 2019: 7036-7046 - [c13]Ron Banner, Yury Nahshan, Daniel Soudry:
Post training 4-bit quantization of convolutional networks for rapid-deployment. NeurIPS 2019: 7948-7956 - [i25]Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry:
Augment your batch: better training with larger batches. CoRR abs/1901.09335 (2019) - [i24]Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro:
How do infinite width bounded norm networks look in function space? CoRR abs/1902.05040 (2019) - [i23]Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry:
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. CoRR abs/1905.07325 (2019) - [i22]Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry:
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off. CoRR abs/1906.00771 (2019) - [i21]Elad Hoffer, Berry Weinstein, Itay Hubara, Tal Ben-Nun, Torsten Hoefler, Daniel Soudry:
Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency. CoRR abs/1908.08986 (2019) - [i20]Niv Giladi, Mor Shpigel Nacson, Elad Hoffer, Daniel Soudry:
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks? CoRR abs/1909.12340 (2019) - [i19]Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro:
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. CoRR abs/1910.01635 (2019) - [i18]Matan Haroush, Itay Hubara, Elad Hoffer, Daniel Soudry:
The Knowledge Within: Methods for Data-Free Model Compression. CoRR abs/1912.01274 (2019) - [i17]Tzofnat Greenberg-Toledo, Ben Perach, Daniel Soudry, Shahar Kvatinsky:
MTJ-Based Hardware Synapse Design for Quantized Deep Neural Networks. CoRR abs/1912.12636 (2019) - 2018
- [j11]Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro:
The Implicit Bias of Gradient Descent on Separable Data. J. Mach. Learn. Res. 19: 70:1-70:57 (2018) - [j10]Philippa J. Karoly, Levin Kuhlmann, Daniel Soudry, David B. Grayden, Mark J. Cook, Dean R. Freestone:
Seizure pathways: A model-based investigation. PLoS Comput. Biol. 14(10) (2018) - [c12]Elad Hoffer, Itay Hubara, Daniel Soudry:
Fix your classifier: the marginal value of training the last weight layer. ICLR (Poster) 2018 - [c11]Daniel Soudry, Elad Hoffer:
Exponentially vanishing sub-optimal local minima in multilayer neural networks. ICLR (Workshop) 2018 - [c10]Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro:
The Implicit Bias of Gradient Descent on Separable Data. ICLR (Poster) 2018 - [c9]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro:
Characterizing Implicit Bias in Terms of Optimization Geometry. ICML 2018: 1827-1836 - [c8]Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry:
Norm matters: efficient and accurate normalization schemes in deep networks. NeurIPS 2018: 2164-2174 - [c7]Ron Banner, Itay Hubara, Elad Hoffer, Daniel Soudry:
Scalable methods for 8-bit training of neural networks. NeurIPS 2018: 5151-5159 - [c6]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nati Srebro:
Implicit Bias of Gradient Descent on Linear Convolutional Networks. NeurIPS 2018: 9482-9491 - [i16]Elad Hoffer, Itay Hubara, Daniel Soudry:
Fix your classifier: the marginal value of training the last weight layer. CoRR abs/1801.04540 (2018) - [i15]Elad Hoffer, Shai Fine, Daniel Soudry:
On the Blindspots of Convolutional Networks. CoRR abs/1802.05187 (2018) - [i14]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro:
Characterizing Implicit Bias in Terms of Optimization Geometry. CoRR abs/1802.08246 (2018) - [i13]Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry:
Norm matters: efficient and accurate normalization schemes in deep networks. CoRR abs/1803.01814 (2018) - [i12]Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Nathan Srebro, Daniel Soudry:
Convergence of Gradient Descent on Separable Data. CoRR abs/1803.01905 (2018) - [i11]Chen Zeno, Itay Golan, Elad Hoffer, Daniel Soudry:
Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning. CoRR abs/1803.10123 (2018) - [i10]Zhihui Zhu, Daniel Soudry, Yonina C. Eldar, Michael B. Wakin:
The Global Optimization Geometry of Shallow Linear Neural Networks. CoRR abs/1805.04938 (2018) - [i9]Ron Banner, Itay Hubara, Elad Hoffer, Daniel Soudry:
Scalable Methods for 8-bit Training of Neural Networks. CoRR abs/1805.11046 (2018) - [i8]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro:
Implicit Bias of Gradient Descent on Linear Convolutional Networks. CoRR abs/1806.00468 (2018) - [i7]Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry:
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. CoRR abs/1806.01796 (2018) - [i6]Ron Banner, Yury Nahshan, Elad Hoffer, Daniel Soudry:
ACIQ: Analytical Clipping for Integer Quantization of neural networks. CoRR abs/1810.05723 (2018) - 2017
- [j9]Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio:
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. J. Mach. Learn. Res. 18: 187:1-187:30 (2017) - [j8]Johannes Friedrich, Weijian Yang, Daniel Soudry, Yu Mu, Misha Ahrens, Rafael Yuste, Darcy S. Peterka, Liam Paninski:
Multi-scale approaches for high-speed imaging and analysis of large neural populations. PLoS Comput. Biol. 13(8) (2017) - [c5]Elad Hoffer, Itay Hubara, Daniel Soudry:
Train longer, generalize better: closing the generalization gap in large batch training of neural networks. NIPS 2017: 1731-1741 - [i5]Elad Hoffer, Itay Hubara, Daniel Soudry:
Train longer, generalize better: closing the generalization gap in large batch training of neural networks. CoRR abs/1705.08741 (2017) - [i4]Daniel Soudry, Elad Hoffer, Nathan Srebro:
The Implicit Bias of Gradient Descent on Separable Data. CoRR abs/1710.10345 (2017) - 2016
- [c4]Eyal Rosenthal, Sergey Greshnikov, Daniel Soudry, Shahar Kvatinsky:
A fully analog memristor-based neural network with online gradient training. ISCAS 2016: 1394-1397 - [c3]Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio:
Binarized Neural Networks. NIPS 2016: 4107-4115 - [i3]Daniel Soudry, Yair Carmon:
No bad local minima: Data independent training error guarantees for multilayer neural networks. CoRR abs/1605.08361 (2016) - [i2]Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio:
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. CoRR abs/1609.07061 (2016) - 2015
- [j7]Daniel Soudry, Suraj Keshri, Patrick Stinson, Min-hwan Oh, Garud Iyengar, Liam Paninski:
Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data. PLoS Comput. Biol. 11(10) (2015) - [j6]Daniel Soudry, Dotan Di Castro, Asaf Gal, Avinoam Kolodny, Shahar Kvatinsky:
Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training. IEEE Trans. Neural Networks Learn. Syst. 26(10): 2408-2421 (2015) - [i1]Zhiyong Cheng, Daniel Soudry, Zexi Mao, Zhen-zhong Lan:
Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation. CoRR abs/1503.03562 (2015) - 2014
- [j5]Daniel Soudry, Ron Meir:
The neuronal response at extended timescales: a linearized spiking input-output relation. Frontiers Comput. Neurosci. 8: 29 (2014) - [j4]Daniel Soudry, Ron Meir:
The neuronal response at extended timescales: long-term correlations without long-term memory. Frontiers Comput. Neurosci. 8: 35 (2014) - [j3]Danilo Pezo, Daniel Soudry, Patricio Orio:
Diffusion approximation-based simulation of stochastic ion channels: which method to use? Frontiers Comput. Neurosci. 8: 139 (2014) - [c2]Daniel Soudry, Itay Hubara, Ron Meir:
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights. NIPS 2014: 963-971 - 2012
- [j2]Daniel Soudry, Ron Meir:
Conductance-Based Neuron Models and the Slow Dynamics of Excitability. Frontiers Comput. Neurosci. 6: 4 (2012) - [c1]Dmitri B. Chklovskii, Daniel Soudry:
"Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter". NIPS 2012: 512-520 - 2010
- [j1]Daniel Soudry, Ron Meir:
History-Dependent Dynamics in a Generic Model of Ion Channels - An Analytic Study. Frontiers Comput. Neurosci. 4: 3 (2010)
Coauthor Index
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