Jascha Sohl-Dickstein
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
42
Venues
6
Active years
2011–2024
Best venue rank
A*
Where they publish
Papers
42 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2024 | ICLR | Small-scale proxies for large-scale Transformer training instabilities. | Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie E. Everett, Alexander A. Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith |
| 2024 | ICML | Scaling Exponents Across Parameterizations and Optimizers. | Katie E. Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington |
| 2024 | ICML | Position: Levels of AGI for Operationalizing Progress on the Path to AGI. | Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clment Farabet, Shane Legg |
| 2023 | ICML | Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. | Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl |
| 2022 | ICML | Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling. | Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2022 | ICML | Fast Finite Width Neural Tangent Kernel. | Roman Novak, Jascha Sohl-Dickstein, Samuel S. Schoenholz |
| 2022 | IJCAI | Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies (Extended Abstract). | Paul Vicol, Luke Metz, Jascha Sohl-Dickstein |
| 2021 | ICLR | Score-Based Generative Modeling through Stochastic Differential Equations. | Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole |
| 2021 | ICML | Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies. | Paul Vicol, Luke Metz, Jascha Sohl-Dickstein |
| 2021 | ICML | Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization. | Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein |
| 2020 | ICLR | Neural Tangents: Fast and Easy Infinite Neural Networks in Python. | Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz |
| 2020 | ICML | Infinite attention: NNGP and NTK for deep attention networks. | Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak |
| 2019 | ICLR | A RAD approach to deep mixture models. | Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle |
| 2019 | ICLR | Adversarial Reprogramming of Neural Networks. | Gamaleldin F. Elsayed, Ian J. Goodfellow, Jascha Sohl-Dickstein |
| 2019 | ICLR | Meta-Learning Update Rules for Unsupervised Representation Learning. | Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein |
| 2019 | ICLR | Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes. | Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2019 | ICLR | A Mean Field Theory of Batch Normalization. | Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz |
| 2019 | ICML | Guided evolutionary strategies: augmenting random search with surrogate gradients. | Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein |
| 2019 | ICML | Understanding and correcting pathologies in the training of learned optimizers. | Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein |
| 2019 | ICML | The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. | Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith |
| 2018 | ICLR | Deep Neural Networks as Gaussian Processes. | Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2018 | ICLR | Generalizing Hamiltonian Monte Carlo with Neural Networks. | Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein |
| 2018 | ICLR | Learning to Learn Without Labels. | Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein |
| 2018 | ICLR | Sensitivity and Generalization in Neural Networks: an Empirical Study. | Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2018 | ICML | Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. | Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington |
| 2017 | ICLR | Capacity and Trainability in Recurrent Neural Networks. | Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo |
| 2017 | ICLR | Density estimation using Real NVP. | Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio |
| 2017 | ICLR | Explaining the Learning Dynamics of Direct Feedback Alignment. | Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein |
| 2017 | ICLR | Unrolled Generative Adversarial Networks. | Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein |
| 2017 | ICLR | Deep Information Propagation. | Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein |
| 2017 | ICLR | REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. | George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein |
| 2017 | ICML | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. | Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo |
| 2017 | ICML | On the Expressive Power of Deep Neural Networks. | Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein |
| 2017 | ICML | Learned Optimizers that Scale and Generalize. | Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein |
| 2015 | ICML | Deep Unsupervised Learning using Nonequilibrium Thermodynamics. | Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli |
| 2014 | ICML | Hamiltonian Monte Carlo Without Detailed Balance. | Jascha Sohl-Dickstein, Mayur Mudigonda, Michael Robert DeWeese |
| 2014 | ICML | Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods. | Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli |
| 2013 | AIED | Controlled experiments on millions of students to personalize learning. | Eliana Feasley, Chris Klaiber, James Irwin, Jace Kohlmeier, Jascha Sohl-Dickstein |
| 2013 | AIED | Measurably Increasing Motivation in MOOCs. | Joseph Jay Williams, Dave Paunesku, Benjamin Heley, Jascha Sohl-Dickstein |
| 2011 | DCC | Lie Group Transformation Models for Predictive Video Coding. | Ching Ming Wang, Jascha Sohl-Dickstein, Ivana Tosic, Bruno A. Olshausen |
| 2011 | ICCV | Building a better probabilistic model of images by factorization. | Benjamin J. Culpepper, Jascha Sohl-Dickstein, Bruno A. Olshausen |
| 2011 | ICML | Minimum Probability Flow Learning. | Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese |