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David Rolnick
David S. Rolnick
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2020 – today
- 2025
- [c30]Nikolaos-Ioannis Bountos, Arthur Ouaknine, Ioannis Papoutsis, David Rolnick:
FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring. AAAI 2025: 27858-27868 - [c29]Gabriel Tseng, Hannah Kerner, David Rolnick:
Task-Informed Meta-Learning for Remote Sensing. CVPR Workshops 2025: 2244-2253 - [i63]Gabriel Tseng, Anthony Fuller, Marlena Reil, Henry Herzog, Patrick Beukema, Favyen Bastani, James R. Green, Evan Shelhamer, Hannah Kerner, David Rolnick:
Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models. CoRR abs/2502.09356 (2025) - [i62]Yuyan Chen, Nico Lang, B. Christian Schmidt, Aditya Jain, Yves Basset, Sara Beery, Maxim Larrivée
, David Rolnick:
Open-Set Recognition of Novel Species in Biodiversity Monitoring. CoRR abs/2503.01691 (2025) - [i61]Mélisande Teng, Arthur Ouaknine, Etienne Laliberté, Yoshua Bengio, David Rolnick, Hugo Larochelle:
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery. CoRR abs/2503.20199 (2025) - [i60]Mélisande Teng, Arthur Ouaknine, Etienne Laliberté, Yoshua Bengio, David Rolnick, Hugo Larochelle:
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery. CoRR abs/2506.04970 (2025) - [i59]Sebastian Hickman, Ilija Trajkovic, Julia Kaltenborn, Francis Pelletier, Alex Archibald, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard:
Causal Climate Emulation with Bayesian Filtering. CoRR abs/2506.09891 (2025) - [i58]Devin Kwok, Gül Sena Altintas, Colin Raffel, David Rolnick:
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions. CoRR abs/2506.13234 (2025) - 2024
- [j15]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) - [j14]Gaurav Iyer, Gintare Karolina Dziugaite, David Rolnick:
Linear Weight Interpolation Leads to Transient Performance Gains. Trans. Mach. Learn. Res. 2024 (2024) - [c28]Aditya Jain, Fagner Cunha, Michael James Bunsen
Insect Identification in the Wild: The AMI Dataset. ECCV (37) 2024: 55-73 - [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 - [i57]Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, David Rolnick:
Dataset Difficulty and the Role of Inductive Bias. CoRR abs/2401.01867 (2024) - [i56]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) - [i55]David Rolnick, Alán Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni
Application-Driven Innovation in Machine Learning. CoRR abs/2403.17381 (2024) - [i54]Hager Radi Abdelwahed, Mélisande Teng, David Rolnick:
Predicting Species Occurrence Patterns from Partial Observations. CoRR abs/2403.18028 (2024) - [i53]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) - [i52]Christina Winkler, Paula Harder, David Rolnick:
Climate Variable Downscaling with Conditional Normalizing Flows. CoRR abs/2405.20719 (2024) - [i51]Aditya Jain, Fagner Cunha, Michael James Bunsen
Insect Identification in the Wild: The AMI Dataset. CoRR abs/2406.12452 (2024) - [i50]Aditya Jain, Fagner Cunha, Michael James Bunsen
A machine learning pipeline for automated insect monitoring. CoRR abs/2406.13031 (2024) - [i49]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) - [i48]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) - [i47]Venkatesh Ramesh, Arthur Ouaknine, David Rolnick:
Tree semantic segmentation from aerial image time series. CoRR abs/2407.13102 (2024) - [i46]Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick:
Causal Representation Learning in Temporal Data via Single-Parent Decoding. CoRR abs/2410.07013 (2024) - [i45]Pratinav Seth
Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery. CoRR abs/2410.09032 (2024) - 2023
- [j13]David Rolnick
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
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
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
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
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
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
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
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
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
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
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
Quantitative Tverberg Theorems Over Lattices and Other Discrete Sets. Discret. Comput. Geom. 58(2): 435-448 (2017) - [j7]David Rolnick
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
On the growth of Stanley sequences. Discret. Math. 338(11): 1928-1937 (2015) - [c1]Jesús A. De Loera, Susan Margulies, Michael Pernpeintner
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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