David Rolnick

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 85

Top Topics

Hard Constraints

Machine Learning

Top Venues

Publications

Ayush Prasad, Paula Harder, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
Evaluating the transferability potential of deep learning models for climate downscaling. CoRR (2024)
Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite
Simultaneous linear connectivity of neural networks modulo permutation. CoRR (2024)
Aditya Jain, Fagner Cunha, Michael James Bunsen, Léonard Pasi, Anna Viklund, Maxim Larrivée, David Rolnick
A machine learning pipeline for automated insect monitoring. CoRR (2024)
Nicholas Carlini, Daniel Paleka, Krishnamurthy (Dj) Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr
Stealing Part of a Production Language Model. CoRR (2024)
Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, David Rolnick
Dataset Difficulty and the Role of Inductive Bias. CoRR (2024)
Aditya Jain, Fagner Cunha, Michael James Bunsen, Juan Sebastián Cañas, Léonard Pasi, Nathan Pinoy, Flemming Helsing, JoAnne Russo, Marc Botham, Michael Sabourin, Jonathan Fréchette, Alexandre Anctil, Yacksecari Lopez, Eduardo Navarro, Filonila Perez Pimentel, Ana Cecilia Zamora, José Alejandro Ramirez Silva, Jonathan Gagnon, Tom August, Kim Bjerge, Alba Gomez Segura, Marc Bélisle, Yves Basset, Kent P. McFarland, David Roy, Toke Thomas Høye, Maxim Larrivée, David Rolnick
Insect Identification in the Wild: The AMI Dataset. CoRR (2024)
Christina Winkler, Paula Harder, David Rolnick
Climate Variable Downscaling with Conditional Normalizing Flows. CoRR (2024)
David Rolnick, Alán Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
Application-Driven Innovation in Machine Learning. CoRR (2024)
Ali Ramlaoui, Théo Saulus, Basile Terver, Victor Schmidt, David Rolnick, Fragkiskos D. Malliaros, Alexandre Duval
Improving Molecular Modeling with Geometric GNNs: an Empirical Study. CoRR (2024)
Venkatesh Ramesh, Arthur Ouaknine, David Rolnick
Tree semantic segmentation from aerial image time series. CoRR (2024)
Hager Radi Abdelwahed, Mélisande Teng, David Rolnick
Predicting Species Occurrence Patterns from Partial Observations. CoRR (2024)
Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi Abdelwahed, Hugo Larochelle, David Rolnick
SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data. NeurIPS (2023)
Qidong Yang, Alex Hernández-García, Paula Harder, Venkatesh Ramesh, Prasanna Sattigeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling. CoRR (2023)
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Sasha Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio
Tackling Climate Change with Machine Learning. ACM Comput. Surv. 55 (2) (2023)
Alvaro Carbonero, Alexandre Duval, Victor Schmidt, Santiago Miret, Alex Hernández-García, Yoshua Bengio, David Rolnick
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions. CoRR (2023)
Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. CoRR (2023)
J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick
Hidden Symmetries of ReLU Networks. ICML (2023)
Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick
FAENet: Frame Averaging Equivariant GNN for Materials Modeling. CoRR (2023)
Richard D. Lange, Devin Kwok, Jordan Kyle Matelsky, Xinyue Wang, David Rolnick, Konrad Kording
Deep Networks as Paths on the Manifold of Neural Representations. TAG-ML (2023)
Jose González-Abad, Alex Hernández-García, Paula Harder, David Rolnick, José Manuel Gutiérrez Llorente
Multi-variable Hard Physical Constraints for Climate Model Downscaling. CoRR (2023)
Mélisande Teng, Amna Elmustafa, Benjamin Akera, Hugo Larochelle, David Rolnick
Bird Distribution Modelling using Remote Sensing and Citizen Science data. CoRR (2023)
J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick
Hidden symmetries of ReLU networks. CoRR (2023)
Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. NeurIPS (2023)
Maximilian Müller, Tiffany Vlaar, David Rolnick, Matthias Hein
Normalization Layers Are All That Sharpness-Aware Minimization Needs. NeurIPS (2023)
Maximilian Müller, Tiffany Vlaar, David Rolnick, Matthias Hein
Normalization Layers Are All That Sharpness-Aware Minimization Needs. CoRR (2023)
Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick
FAENet: Frame Averaging Equivariant GNN for Materials Modeling. ICML (2023)
Arthur Ouaknine, Teja Kattenborn, Etienne Laliberté, David Rolnick
OpenForest: A data catalogue for machine learning in forest monitoring. CoRR (2023)
Alexandra Sasha Luccioni, David Rolnick
Bugs in the Data: How ImageNet Misrepresents Biodiversity. AAAI (2023)
Gabriel Tseng, Ivan Zvonkov, Mirali Purohit, David Rolnick, Hannah Kerner
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries. CoRR (2023)
Julien Boussard, Chandni Nagda, Julia Kaltenborn, Charlotte Emilie Elektra Lange, Philippe Brouillard, Yaniv Gurwicz, Peer Nowack, David Rolnick
Towards Causal Representations of Climate Model Data. CoRR (2023)
Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi Abdelwahed, Hugo Larochelle, David Rolnick
SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data. CoRR (2023)
Gaurav Iyer, Boris Hanin, David Rolnick
Maximal Initial Learning Rates in Deep ReLU Networks. ICML (2023)
Nikolaos-Ioannis Bountos, Arthur Ouaknine, David Rolnick
FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models. CoRR (2023)
Richard D. Lange, David Rolnick, Konrad P. Kording
Clustering units in neural networks: upstream vs downstream information. Trans. Mach. Learn. Res. 2022 (2022)
Gabriel Tseng, Hannah Kerner, David Rolnick
TIML: Task-Informed Meta-Learning for Agriculture. CoRR (2022)
Setareh Cohen, Nam Hee Kim, David Rolnick, Michiel van de Panne
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. CoRR (2022)
Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design. CoRR (2022)
Alexandra Sasha Luccioni, David Rolnick
Bugs in the Data: How ImageNet Misrepresents Biodiversity. CoRR (2022)
Gaurav Iyer, Boris Hanin, David Rolnick
Maximal Initial Learning Rates in Deep ReLU Networks. CoRR (2022)
Setareh Cohan, Nam Hee Kim, David Rolnick, Michiel van de Panne
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. NeurIPS (2022)
Boris Hanin, Ryan S. Jeong, David Rolnick
Deep ReLU Networks Preserve Expected Length. ICLR (2022)
Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake A. Richards, Guillaume Lajoie
On Neural Architecture Inductive Biases for Relational Tasks. CoRR (2022)
Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, David Rolnick
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models. CoRR (2021)
Sever Topan, David Rolnick, Xujie Si
Techniques for Symbol Grounding with SATNet. NeurIPS (2021)
Boris Hanin, Ryan Jeong, David Rolnick
Deep ReLU Networks Preserve Expected Length. CoRR (2021)
Priya L. Donti, David Rolnick, J. Zico Kolter
DC3: A learning method for optimization with hard constraints. ICLR (2021)
Christopher Hillar, Tenzin Chan, Rachel Taubman, David Rolnick
Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks. Entropy 23 (11) (2021)
Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, David Rolnick
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models. NeurIPS Datasets and Benchmarks (2021)
Sever Topan, David Rolnick, Xujie Si
Techniques for Symbol Grounding with SATNet. CoRR (2021)
Charles A. Kantor, Marta Skreta, Brice Rauby, Léonard Boussioux, Emmanuel Jehanno, Alexandra Luccioni, David Rolnick, Hugues Talbot
Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification. CoRR (2021)
Priya L. Donti, David Rolnick, J. Zico Kolter
DC3: A learning method for optimization with hard constraints. CoRR (2021)
David Rolnick, Konrad P. Kording
Reverse-engineering deep ReLU networks. ICML (2020)
David Rolnick, Kevin Aydin, Jean Pouget-Abadie, Shahab Kamali, Vahab S. Mirrokni, Amir Najmi
Randomized Experimental Design via Geographic Clustering. KDD (2019)
David Rolnick, Konrad P. Körding
Identifying Weights and Architectures of Unknown ReLU Networks. CoRR (2019)
Boris Hanin, David Rolnick
Deep ReLU Networks Have Surprisingly Few Activation Patterns. CoRR (2019)
Ari S. Benjamin, David Rolnick, Konrad P. Körding
Measuring and regularizing networks in function space. ICLR (Poster) (2019)
Yaron Meirovitch, Lu Mi, Hayk Saribekyan, Alexander Matveev, David Rolnick, Nir Shavit
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. CVPR (2019)
Boris Hanin, David Rolnick
Complexity of Linear Regions in Deep Networks. CoRR (2019)
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Körding, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio
Tackling Climate Change with Machine Learning. CoRR (2019)
Boris Hanin, David Rolnick
Deep ReLU Networks Have Surprisingly Few Activation Patterns. NeurIPS (2019)
Boris Hanin, David Rolnick
Complexity of Linear Regions in Deep Networks. ICML (2019)
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne
Experience Replay for Continual Learning. NeurIPS (2019)
Yaron Meirovitch, Lu Mi, Hayk Saribekyan, Alexander Matveev, David Rolnick, Casimir Wierzynski, Nir Shavit
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. CoRR (2018)
Boris Hanin, David Rolnick
How to Start Training: The Effect of Initialization and Architecture. NeurIPS (2018)
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne
Experience Replay for Continual Learning. CoRR (2018)
Ari S. Benjamin, David Rolnick, Konrad P. Körding
Measuring and regularizing networks in function space. CoRR (2018)
David Rolnick, Max Tegmark
The power of deeper networks for expressing natural functions. ICLR (Poster) (2018)
Boris Hanin, David Rolnick
How to Start Training: The Effect of Initialization and Architecture. CoRR (2018)
David Rolnick, Andreas Veit, Serge J. Belongie, Nir Shavit
Deep Learning is Robust to Massive Label Noise. CoRR (2017)
Jeremy Bernstein, Ishita Dasgupta, David Rolnick, Haim Sompolinsky
Markov Transitions between Attractor States in a Recurrent Neural Network. AAAI Spring Symposia (2017)
David Rolnick
On the classification of Stanley sequences. Eur. J. Comb. 59 (2017)
David Rolnick, Max Tegmark
The power of deeper networks for expressing natural functions. CoRR (2017)
David Rolnick, Pablo Soberón
Quantitative (p, q) theorems in combinatorial geometry. Discret. Math. 340 (10) (2017)
David Rolnick, Yaron Meirovitch, Toufiq Parag, Hanspeter Pfister, Viren Jain, Jeff W. Lichtman, Edward S. Boyden, Nir Shavit
Morphological Error Detection in 3D Segmentations. CoRR (2017)
Jesús A. De Loera, Reuben N. La Haye, David Rolnick, Pablo Soberón
Quantitative Combinatorial Geometry for Continuous Parameters. Discret. Comput. Geom. 57 (2) (2017)
Jesús A. De Loera, Reuben N. La Haye, David Rolnick, Pablo Soberón
Quantitative Tverberg Theorems Over Lattices and Other Discrete Sets. Discret. Comput. Geom. 58 (2) (2017)
David Rolnick, Kevin Aydin, Shahab Kamali, Vahab S. Mirrokni, Amir Najmi
GeoCUTS: Geographic Clustering Using Travel Statistics. CoRR (2016)
Richard A. Moy, David Rolnick
Novel structures in Stanley sequences. Discret. Math. 339 (2) (2016)
David Rolnick, Pablo Soberón
Algorithmic aspects of Tverberg's Theorem. CoRR (2016)
Yaron Meirovitch, Alexander Matveev, Hayk Saribekyan, David M. Budden, David Rolnick, Gergely Ódor, Seymour Knowles-Barley, Thouis Raymond Jones, Hanspeter Pfister, Jeff William Lichtman, Nir Shavit
A Multi-Pass Approach to Large-Scale Connectomics. CoRR (2016)
Richard A. Moy, David Rolnick
Novel structures in Stanley sequences. CoRR (2015)
Noah Golowich, David Rolnick
Acyclic Subgraphs of Planar Digraphs. Electron. J. Comb. 22 (3) (2015)
David Rolnick, Praveen S. Venkataramana
On the growth of Stanley sequences. Discret. Math. 338 (11) (2015)
Gwen Spencer, David Rolnick
On the robust hardness of Gröbner basis computation. CoRR (2015)
Jesús A. De Loera, Susan Margulies, Michael Pernpeintner, Eric Riedl, David Rolnick, Gwen Spencer, Despina Stasi, Jon Swenson
Graph-Coloring Ideals: Nullstellensatz Certificates, Gröbner Bases for Chordal Graphs, and Hardness of Gröbner Bases. ISSAC (2015)
Jesús A. De Loera, Susan Margulies, Michael Pernpeintner, Eric Riedl, David Rolnick, Gwen Spencer, Despina Stasi, Jon Swenson
Gröbner Bases and Nullstellensätze for Graph-Coloring Ideals. CoRR (2014)
David Rolnick
The on-line degree Ramsey number of cycles. Discret. Math. 313 (20) (2013)
David Rolnick
Trees with an On-Line Degree Ramsey Number of Four. Electron. J. Comb. 18 (1) (2011)