Jascha Sohl‐Dickstein
Impact in
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- Online Learning and Analytics
- Astronomy and Astrophysics top 5%
- Planetary Science and Exploration
- Astro and Planetary Science
Papers in
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- Neural Networks and Applications 15
- Machine Learning and Data Classification 8
- Stochastic Gradient Optimization Techniques 7
- Adversarial Robustness in Machine Learning 7
- Gaussian Processes and Bayesian Inference 7
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- Generative Adversarial Networks and Image Synthesis 8
- Advanced Neural Network Applications 7
- Co-authors
- Surya GanguliBen PooleLuke MetzChris PiechJonathan HuangLeonidas GuibasMehran SahamiJeffrey Pennington
- Journals
- Journal of Geophysical Research Atmospheres (4 papers)Medical Physics (1 paper)Journal of Vision (1 paper)Nature Communications (1 paper)Physical Review Letters (1 paper)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Jascha Sohl‐Dickstein
57 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Computer Science Applications 247
- Astronomy and Astrophysics 620
- Artificial Intelligence 1.0k
- Computer Vision and Pattern Recognition 375
- Computational Mathematics 8
Countries citing papers authored by Jascha Sohl‐Dickstein
This map shows the geographic impact of Jascha Sohl‐Dickstein'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 Jascha Sohl‐Dickstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jascha Sohl‐Dickstein more than expected).
Fields of papers citing papers by Jascha Sohl‐Dickstein
This network shows the impact of papers produced by Jascha Sohl‐Dickstein. 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 Jascha Sohl‐Dickstein. The network helps show where Jascha Sohl‐Dickstein may publish in the future.
Co-authors
The 25 scholars most cited alongside Jascha Sohl‐Dickstein, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Score-Based Generative Modeling through Stochastic Differential Equations | 2021 | 6 |
| 2 | Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling | 2020 | 3 |
| 3 | Finite Versus Infinite Neural Networks: an Empirical Study | 2020 | 5 |
| 4 | Neural Tangents: Fast and Easy Infinite Neural Networks in Python | 2020 | 9 |
| 5 | A Mean Field Theory of Batch Normalization | 2019 | 13 |
| 6 | Meta-Learning Update Rules for Unsupervised Representation Learning | 2018 | 4 |
| 7 | Sensitivity and Generalization in Neural Networks: an Empirical Study | 2018 | 18 |
| 8 | Learning Unsupervised Learning Rules | 2018 | 9 |
| 9 | Deep Neural Networks as Gaussian Processes | 2018 | 76 |
| 10 | Learned optimizers that outperform SGD on wall-clock and validation loss | 2018 | 3 |
| 11 | Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes. | 2018 | 5 |
| 12 | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability | 2017 | 6 |
| 13 | Explaining the Learning Dynamics of Direct Feedback Alignment | 2017 | 3 |
| 14 | Exponential expressivity in deep neural networks through transient chaos | 2016 | 36 |
| 15 | Measurably Increasing Motivation in MOOCs. | 2013 | 5 |
| 16 | An adaptive low dimensional quasi-Newton sum of functions optimizer. | 2013 | 3 |
| 17 | Minimum Probability Flow Learning | 2011 | 15 |
| 18 | A Martian Year of High Resolution Multispectral Imaging from the Pancam Instruments on the Mars Exploration Rovers Spirit and Opportunity | 2006 | 0 |
| 19 | Modeling Visible/Near-Infrared Photometric Properties of Dustfall on a Known Substrate | 2005 | 16 |
| 20 | The Single Scattering Albedo of Martian Atmospheric Dust in the 290-500 nm Region | 2002 | 1 |
About Jascha Sohl‐Dickstein
Jascha Sohl‐Dickstein is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Health Informatics and Computer Graphics and Computer-Aided Design, having authored 60 papers that have together received 2.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (15 papers), Planetary Science and Exploration (9 papers), Machine Learning and Data Classification (8 papers), Generative Adversarial Networks and Image Synthesis (8 papers), Stochastic Gradient Optimization Techniques (7 papers), Adversarial Robustness in Machine Learning (7 papers), Advanced Neural Network Applications (7 papers) and Gaussian Processes and Bayesian Inference (7 papers). The work is most often cited by research in Computer Science Applications (247 citations), Astronomy and Astrophysics (620 citations), Artificial Intelligence (1.0k citations), Computer Vision and Pattern Recognition (375 citations) and Computational Mathematics (8 citations). Jascha Sohl‐Dickstein has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Surya Ganguli, Ben Poole, Luke Metz, Chris Piech, Jonathan Huang, Leonidas Guibas, Mehran Sahami, Jeffrey Pennington, Yasaman Bahri and David Pfau. Their work appears in journals such as Journal of Geophysical Research Atmospheres, Medical Physics, Journal of Vision, Nature Communications and Physical Review Letters.
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.