Daniel Soudry
About
In The Last Decade
Daniel Soudry
39 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Computer Vision and Pattern Recognition 744
- Artificial Intelligence 744
- Cognitive Neuroscience 678
- Cellular and Molecular Neuroscience 576
- Electrical and Electronic Engineering 550
Countries citing papers authored by Daniel Soudry
This map shows the geographic impact of Daniel Soudry's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Soudry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Soudry more than expected).
Fields of papers citing papers by Daniel Soudry
This network shows the impact of papers produced by Daniel Soudry. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Soudry. The network helps show where Daniel Soudry may publish in the future.
Co-authorship network of co-authors of Daniel Soudry
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Soudry. A scholar is included among the top collaborators of Daniel Soudry based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Soudry. Daniel Soudry is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | The Implicit Bias of Minima Stability: A View from Function Space | 5 |
| 2 | At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks? | 1 |
| 3 | Neural gradients are lognormally distributed: understanding sparse and quantized training. | 1 |
| 4 | A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off | 1 |
| 5 | Post training 4-bit quantization of convolutional networks for rapid-deployment | 175 |
| 6 | 27 | |
| 7 | Scalable methods for 8-bit training of neural networks | 52 |
| 8 | ACIQ: Analytical Clipping for Integer Quantization of neural networks | 40 |
| 9 | Norm matters: efficient and accurate normalization schemes in deep networks | 16 |
| 10 | Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning. | 1 |
| 11 | Characterizing Implicit Bias in Terms of Optimization Geometry | 29 |
| 12 | 41 | |
| 13 | Train longer, generalize better: closing the generalization gap in large batch training of neural networks | 48 |
| 14 | 32 | |
| 15 | Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data breakdown → | 608 |
| 16 | 2 | |
| 17 | 5 | |
| 18 | Neuronal Spike Generation Mechanism as an Oversampling, Noise-shaping A-to-D converter | 5 |
| 19 | 9 | |
| 20 | 30 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.