Daniel Soudry

According to our database1, Daniel Soudry authored at least 31 papers between 2010 and 2018.

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Bibliography

2018
ACIQ: Analytical Clipping for Integer Quantization of neural networks.
CoRR, 2018

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate.
CoRR, 2018

Implicit Bias of Gradient Descent on Linear Convolutional Networks.
CoRR, 2018

Scalable Methods for 8-bit Training of Neural Networks.
CoRR, 2018

The Global Optimization Geometry of Shallow Linear Neural Networks.
CoRR, 2018

Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning.
CoRR, 2018

Convergence of Gradient Descent on Separable Data.
CoRR, 2018

Norm matters: efficient and accurate normalization schemes in deep networks.
CoRR, 2018

Characterizing Implicit Bias in Terms of Optimization Geometry.
CoRR, 2018

On the Blindspots of Convolutional Networks.
CoRR, 2018

Fix your classifier: the marginal value of training the last weight layer.
CoRR, 2018

Characterizing Implicit Bias in Terms of Optimization Geometry.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Multi-scale approaches for high-speed imaging and analysis of large neural populations.
PLoS Computational Biology, 2017

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations.
Journal of Machine Learning Research, 2017

The Implicit Bias of Gradient Descent on Separable Data.
CoRR, 2017

Train longer, generalize better: closing the generalization gap in large batch training of neural networks.
CoRR, 2017

Train longer, generalize better: closing the generalization gap in large batch training of neural networks.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
No bad local minima: Data independent training error guarantees for multilayer neural networks.
CoRR, 2016

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations.
CoRR, 2016

Binarized Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A fully analog memristor-based neural network with online gradient training.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2016

2015
Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training.
IEEE Trans. Neural Netw. Learning Syst., 2015

Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data.
PLoS Computational Biology, 2015

Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation.
CoRR, 2015

2014
The neuronal response at extended timescales: a linearized spiking input-output relation.
Front. Comput. Neurosci., 2014

The neuronal response at extended timescales: long-term correlations without long-term memory.
Front. Comput. Neurosci., 2014

Diffusion approximation-based simulation of stochastic ion channels: which method to use?
Front. Comput. Neurosci., 2014

Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2012
Conductance-Based Neuron Models and the Slow Dynamics of Excitability.
Front. Comput. Neurosci., 2012

"Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter".
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2010
History-Dependent Dynamics in a Generic Model of Ion Channels - An Analytic Study.
Front. Comput. Neurosci., 2010


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