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David S. Rolnick
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2020 – today
- 2024
[j14]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. J. Mach. Learn. Res. 25: 106:1-106:26 (2024)- [c27]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. ICML 2024 - [c26]David Rolnick, Alán Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White:
Position: Application-Driven Innovation in Machine Learning. ICML 2024 - [c25]Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite:
Simultaneous Linear Connectivity of Neural Networks Modulo Permutation. ECML/PKDD (7) 2024: 262-279 - [i55]Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, David Rolnick:
Dataset Difficulty and the Role of Inductive Bias. CoRR abs/2401.01867 (2024) - [i54]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 abs/2403.06634 (2024) - [i53]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 abs/2403.17381 (2024) - [i52]Hager Radi Abdelwahed, Mélisande Teng, David Rolnick:
Predicting Species Occurrence Patterns from Partial Observations. CoRR abs/2403.18028 (2024) - [i51]Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite:
Simultaneous linear connectivity of neural networks modulo permutation. CoRR abs/2404.06498 (2024) - [i50]Christina Winkler, Paula Harder, David Rolnick:
Climate Variable Downscaling with Conditional Normalizing Flows. CoRR abs/2405.20719 (2024) - [i49]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 abs/2406.12452 (2024) - [i48]Aditya Jain, Fagner Cunha, Michael James Bunsen, Léonard Pasi, Anna Viklund, Maxim Larrivée, David Rolnick:
A machine learning pipeline for automated insect monitoring. CoRR abs/2406.13031 (2024) - [i47]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 abs/2407.08313 (2024) - [i46]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 abs/2407.12517 (2024) - [i45]Venkatesh Ramesh, Arthur Ouaknine, David Rolnick:
Tree semantic segmentation from aerial image time series. CoRR abs/2407.13102 (2024) - 2023
- [j13]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): 42:1-42:96 (2023) - [j12]Paula Harder, Alex Hernández-García, Venkatesh Ramesh, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick:
Hard-Constrained Deep Learning for Climate Downscaling. J. Mach. Learn. Res. 24: 365:1-365:40 (2023) - [c24]Alexandra Sasha Luccioni, David Rolnick:
Bugs in the Data: How ImageNet Misrepresents Biodiversity. AAAI 2023: 14382-14390 - [c23]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: 9013-9033 - [c22]J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick:
Hidden Symmetries of ReLU Networks. ICML 2023: 11734-11760 - [c21]Gaurav Iyer, Boris Hanin, David Rolnick:
Maximal Initial Learning Rates in Deep ReLU Networks. ICML 2023: 14500-14530 - [c20]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 - [c19]Maximilian Müller, Tiffany Vlaar, David Rolnick, Matthias Hein:
Normalization Layers Are All That Sharpness-Aware Minimization Needs. NeurIPS 2023 - [c18]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 - [c17]Richard D. Lange, Devin Kwok, Jordan Kyle Matelsky, Xinyue Wang, David Rolnick, Konrad P. Kording:
Deep Networks as Paths on the Manifold of Neural Representations. TAG-ML 2023: 102-133 - [i44]Gabriel Tseng, Ivan Zvonkov, Mirali Purohit, David Rolnick, Hannah Kerner:
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries. CoRR abs/2304.14065 (2023) - [i43]Mélisande Teng, Amna Elmustafa, Benjamin Akera, Hugo Larochelle, David Rolnick:
Bird Distribution Modelling using Remote Sensing and Citizen Science data. CoRR abs/2305.01079 (2023) - [i42]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 abs/2305.05577 (2023) - [i41]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 abs/2305.14452 (2023) - [i40]Maximilian Müller, Tiffany Vlaar, David Rolnick, Matthias Hein:
Normalization Layers Are All That Sharpness-Aware Minimization Needs. CoRR abs/2306.04226 (2023) - [i39]J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick:
Hidden symmetries of ReLU networks. CoRR abs/2306.06179 (2023) - [i38]Jose González-Abad, Alex Hernández-García, Paula Harder, David Rolnick, José Manuel Gutiérrez Llorente:
Multi-variable Hard Physical Constraints for Climate Model Downscaling. CoRR abs/2308.01868 (2023) - [i37]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 abs/2310.06682 (2023) - [i36]Arthur Ouaknine, Teja Kattenborn, Etienne Laliberté, David Rolnick:
OpenForest: A data catalogue for machine learning in forest monitoring. CoRR abs/2311.00277 (2023) - [i35]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 abs/2311.00936 (2023) - [i34]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 abs/2311.03721 (2023) - [i33]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 abs/2312.02858 (2023) - [i32]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 abs/2312.10114 (2023) - 2022
- [j11]Richard D. Lange, David Rolnick, Konrad P. Kording:
Clustering units in neural networks: upstream vs downstream information. Trans. Mach. Learn. Res. 2022 (2022) - [c16]Boris Hanin, Ryan S. Jeong, David Rolnick:
Deep ReLU Networks Preserve Expected Length. ICLR 2022 - [c15]Setareh Cohan, Nam Hee Kim, David Rolnick, Michiel van de Panne:
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. NeurIPS 2022 - [i31]Gabriel Tseng, Hannah Kerner, David Rolnick:
TIML: Task-Informed Meta-Learning for Agriculture. CoRR abs/2202.02124 (2022) - [i30]Richard D. Lange, David S. Rolnick, Konrad P. Kording:
Clustering units in neural networks: upstream vs downstream information. CoRR abs/2203.11815 (2022) - [i29]Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake A. Richards, Guillaume Lajoie:
On Neural Architecture Inductive Biases for Relational Tasks. CoRR abs/2206.05056 (2022) - [i28]Richard D. Lange, Jordan Matelsky, Xinyue Wang, Devin Kwok, David S. Rolnick, Konrad P. Kording:
Neural Networks as Paths through the Space of Representations. CoRR abs/2206.10999 (2022) - [i27]Paula Harder, Qidong Yang, Venkatesh Ramesh, Prasanna Sattigeri, Alex Hernández-García, Campbell D. Watson, Daniela Szwarcman, David S. Rolnick:
Generating physically-consistent high-resolution climate data with hard-constrained neural networks. CoRR abs/2208.05424 (2022) - [i26]Alexandra Sasha Luccioni, David Rolnick:
Bugs in the Data: How ImageNet Misrepresents Biodiversity. CoRR abs/2208.11695 (2022) - [i25]Setareh Cohan, Nam Hee Kim, David Rolnick, Michiel van de Panne:
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. CoRR abs/2210.13611 (2022) - [i24]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 abs/2211.12020 (2022) - [i23]Gaurav Iyer, Boris Hanin, David Rolnick:
Maximal Initial Learning Rates in Deep ReLU Networks. CoRR abs/2212.07295 (2022) - 2021
- [j10]Christopher Hillar, Tenzin Chan, Rachel Taubman, David Rolnick:
Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks. Entropy 23(11): 1494 (2021) - [c14]Priya L. Donti, David Rolnick, J. Zico Kolter:
DC3: A learning method for optimization with hard constraints. ICLR 2021 - [c13]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 - [c12]Sever Topan, David Rolnick, Xujie Si:
Techniques for Symbol Grounding with SATNet. NeurIPS 2021: 20733-20744 - [i22]Boris Hanin, Ryan Jeong, David Rolnick:
Deep ReLU Networks Preserve Expected Length. CoRR abs/2102.10492 (2021) - [i21]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 abs/2103.11285 (2021) - [i20]Priya L. Donti, David Rolnick, J. Zico Kolter:
DC3: A learning method for optimization with hard constraints. CoRR abs/2104.12225 (2021) - [i19]Sever Topan, David Rolnick, Xujie Si:
Techniques for Symbol Grounding with SATNet. CoRR abs/2106.11072 (2021) - [i18]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 abs/2111.14671 (2021) - 2020
- [c11]David Rolnick, Konrad P. Kording:
Reverse-engineering deep ReLU networks. ICML 2020: 8178-8187
2010 – 2019
- 2019
- [c10]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: 8425-8435 - [c9]Ari S. Benjamin, David Rolnick, Konrad P. Körding:
Measuring and regularizing networks in function space. ICLR (Poster) 2019 - [c8]Boris Hanin, David Rolnick:
Complexity of Linear Regions in Deep Networks. ICML 2019: 2596-2604 - [c7]David Rolnick, Kevin Aydin, Jean Pouget-Abadie, Shahab Kamali, Vahab S. Mirrokni, Amir Najmi:
Randomized Experimental Design via Geographic Clustering. KDD 2019: 2745-2753 - [c6]David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne:
Experience Replay for Continual Learning. NeurIPS 2019: 348-358 - [c5]Boris Hanin, David Rolnick:
Deep ReLU Networks Have Surprisingly Few Activation Patterns. NeurIPS 2019: 359-368 - [i17]Boris Hanin, David Rolnick:
Complexity of Linear Regions in Deep Networks. CoRR abs/1901.09021 (2019) - [i16]Boris Hanin, David Rolnick:
Deep ReLU Networks Have Surprisingly Few Activation Patterns. CoRR abs/1906.00904 (2019) - [i15]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 abs/1906.05433 (2019) - [i14]David Rolnick, Konrad P. Körding:
Identifying Weights and Architectures of Unknown ReLU Networks. CoRR abs/1910.00744 (2019) - 2018
- [c4]David Rolnick, Max Tegmark:
The power of deeper networks for expressing natural functions. ICLR (Poster) 2018 - [c3]Boris Hanin, David Rolnick:
How to Start Training: The Effect of Initialization and Architecture. NeurIPS 2018: 569-579 - [i13]Boris Hanin, David Rolnick:
How to Start Training: The Effect of Initialization and Architecture. CoRR abs/1803.01719 (2018) - [i12]Ari S. Benjamin, David Rolnick, Konrad P. Körding:
Measuring and regularizing networks in function space. CoRR abs/1805.08289 (2018) - [i11]David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne:
Experience Replay for Continual Learning. CoRR abs/1811.11682 (2018) - [i10]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 abs/1812.01157 (2018) - 2017
- [j9]Jesús A. De Loera, Reuben N. La Haye, David Rolnick, Pablo Soberón:
Quantitative Combinatorial Geometry for Continuous Parameters. Discret. Comput. Geom. 57(2): 318-334 (2017) - [j8]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): 435-448 (2017) - [j7]David Rolnick, Pablo Soberón:
Quantitative (p, q) theorems in combinatorial geometry. Discret. Math. 340(10): 2516-2527 (2017) - [j6]David Rolnick:
On the classification of Stanley sequences. Eur. J. Comb. 59: 51-70 (2017) - [c2]Jeremy Bernstein, Ishita Dasgupta, David Rolnick, Haim Sompolinsky:
Markov Transitions between Attractor States in a Recurrent Neural Network. AAAI Spring Symposia 2017 - [i9]David Rolnick, Max Tegmark:
The power of deeper networks for expressing natural functions. CoRR abs/1705.05502 (2017) - [i8]David Rolnick, Andreas Veit, Serge J. Belongie, Nir Shavit:
Deep Learning is Robust to Massive Label Noise. CoRR abs/1705.10694 (2017) - [i7]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 abs/1705.10882 (2017) - 2016
- [j5]Richard A. Moy, David Rolnick:
Novel structures in Stanley sequences. Discret. Math. 339(2): 689-698 (2016) - [i6]David Rolnick, Pablo Soberón:
Algorithmic aspects of Tverberg's Theorem. CoRR abs/1601.03083 (2016) - [i5]David Rolnick, Kevin Aydin, Shahab Kamali, Vahab S. Mirrokni, Amir Najmi:
GeoCUTS: Geographic Clustering Using Travel Statistics. CoRR abs/1611.03780 (2016) - [i4]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 abs/1612.02120 (2016) - 2015
- [j4]Noah Golowich, David Rolnick:
Acyclic Subgraphs of Planar Digraphs. Electron. J. Comb. 22(3): 3 (2015) - [j3]David Rolnick, Praveen S. Venkataramana:
On the growth of Stanley sequences. Discret. Math. 338(11): 1928-1937 (2015) - [c1]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: 133-140 - [i3]Richard A. Moy, David Rolnick:
Novel structures in Stanley sequences. CoRR abs/1502.06013 (2015) - [i2]Gwen Spencer, David Rolnick:
On the robust hardness of Gröbner basis computation. CoRR abs/1511.06436 (2015) - 2014
- [i1]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 abs/1410.6806 (2014) - 2013
- [j2]David Rolnick:
The on-line degree Ramsey number of cycles. Discret. Math. 313(20): 2084-2093 (2013) - 2011
- [j1]David Rolnick:
Trees with an On-Line Degree Ramsey Number of Four. Electron. J. Comb. 18(1) (2011)
Coauthor Index
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