David Rolnick - RankMe

123 papers A* 25B 1Journal 87Unranked 10

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models. AAAI Catherine Villeneuve, Benjamin Akera, Mélisande Teng, David Rolnick
2026	J	`jnl`	Estimating Individual Tree Height and Species from UAV Imagery. CoRR Jannik Endres, Etienne Laliberté, David Rolnick, Arthur Ouaknine
2026	J	`jnl`	In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior. CoRR Anaïs Berkes, Vincent Taboga, Donna Vakalis, David Rolnick, Yoshua Bengio
2026	J	`jnl`	Localized, High-resolution Geographic Representations with Slepian Functions. CoRR Arjun Rao, Ruth Crasto, Tessa Ooms, David Rolnick, Konstantin Klemmer, Marc Rußwurm
2025	—	`conf`	A HOT Dataset: 150, 000 Buildings for HVAC Operations Transfer Research. BuildSys Anaïs Berkes, Yoshua Bengio, David Rolnick, Donna Vakalis
2025	A*	`conf`	Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery. ICML Pratinav Seth, Michelle P. Lin, Brefo Yaw Dwamena, Jade Boutot, Mary Kang, David Rolnick
2025	J	`jnl`	Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery. CoRR Mélisande Teng, Arthur Ouaknine, Etienne Laliberté, Yoshua Bengio, David Rolnick, Hugo Larochelle
2025	J	`jnl`	BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models. CoRR Catherine Villeneuve, Benjamin Akera, Mélisande Teng, David Rolnick
2025	J	`jnl`	Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery. CoRR Mélisande Teng, Arthur Ouaknine, Etienne Laliberté, Yoshua Bengio, David Rolnick, Hugo Larochelle
2025	J	`jnl`	CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations. CoRR Hager Radi Abdelwahed, Mélisande Teng, Robin Zbinden, Laura Pollock, Hugo Larochelle, Devis Tuia, David Rolnick
2025	J	`jnl`	Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study. CoRR Lena Podina, Christina Humer, Alexandre Duval, Victor Schmidt, Ali Ramlaoui, Shahana Chatterjee, Yoshua Bengio, Alex Hernández-García, David Rolnick, Félix Therrien
2025	J	`jnl`	Causal Climate Emulation with Bayesian Filtering. CoRR Sebastian Hickman, Ilija Trajkovic, Julia Kaltenborn, Francis Pelletier, Alex Archibald, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard
2025	—	`conf`	Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal. TerraBytes Christina Butsko, Gabriel Tseng, Kristof Van Tricht, Giorgia Milli, David Rolnick, Ruben Cartuyvels, Inbal Becker-Reshef, Zoltan Szantoi, Hannah Kerner
2025	J	`jnl`	Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal. CoRR Christina Butsko, Kristof Van Tricht, Gabriel Tseng, Giorgia Milli, David Rolnick, Ruben Cartuyvels, Inbal Becker-Reshef, Zoltan Szantoi, Hannah Kerner
2025	A*	`conf`	FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring. AAAI Nikolaos-Ioannis Bountos, Arthur Ouaknine, Ioannis Papoutsis, David Rolnick
2025	A*	`conf`	Galileo: Learning Global & Local Features of Many Remote Sensing Modalities. ICML Gabriel Tseng, Anthony Fuller, Marlena Reil, Henry Herzog, Patrick Beukema, Favyen Bastani, James R. Green, Evan Shelhamer, Hannah Kerner, David Rolnick
2025	J	`jnl`	Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models. CoRR Gabriel Tseng, Anthony Fuller, Marlena Reil, Henry Herzog, Patrick Beukema, Favyen Bastani, James R. Green, Evan Shelhamer, Hannah Kerner, David Rolnick
2025	J	`jnl`	GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction. CoRR Eya Cherif, Arthur Ouaknine, Luke A. Brown, Phuong D. Dao, Kyle R. Kovach, Bing Lu, Daniel Mederer, Hannes Feilhauer, Teja Kattenborn, David Rolnick
2025	J	`jnl`	Identifying birdsong syllables without labelled data. CoRR Mélisande Teng, Julien Boussard, David Rolnick, Hugo Larochelle
2025	J	`jnl`	On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling. CoRR Paula Harder, Christian Lessig, Matthew Chantry, Francis Pelletier, David Rolnick
2025	J	`jnl`	Open-Set Recognition of Novel Species in Biodiversity Monitoring. CoRR Yuyan Chen, Nico Lang, B. Christian Schmidt, Aditya Jain, Yves Basset, Sara Beery, Maxim Larrivée, David Rolnick
2025	J	`jnl`	RainShift: A Benchmark for Precipitation Downscaling Across Geographies. CoRR Paula Harder, Luca Schmidt, Francis Pelletier, Nicole Ludwig, Matthew Chantry, Christian Lessig, Alex Hernández-García, David Rolnick
2025	—	`conf`	Task-Informed Meta-Learning for Remote Sensing. CVPR Workshops Gabriel Tseng, Hannah Kerner, David Rolnick
2025	A*	`conf`	The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions. ICML Gül Sena Altintas, Devin Kwok, Colin Raffel, David Rolnick
2025	J	`jnl`	The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions. CoRR Devin Kwok, Gül Sena Altintas, Colin Raffel, David Rolnick
2024	J	`jnl`	A machine learning pipeline for automated insect monitoring. CoRR Aditya Jain, Fagner Cunha, Michael James Bunsen, Léonard Pasi, Anna Viklund, Maxim Larrivée, David Rolnick
2024	J	`jnl`	Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery. CoRR Pratinav Seth, Michelle P. Lin, Brefo Yaw Dwamena, Jade Boutot, Mary Kang, David Rolnick
2024	J	`jnl`	Application-Driven Innovation in Machine Learning. CoRR David Rolnick, Alán Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
2024	J	`jnl`	Causal Representation Learning in Temporal Data via Single-Parent Decoding. CoRR Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick
2024	J	`jnl`	Climate Variable Downscaling with Conditional Normalizing Flows. CoRR Christina Winkler, Paula Harder, David Rolnick
2024	J	`jnl`	Dataset Difficulty and the Role of Inductive Bias. CoRR Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, David Rolnick
2024	J	`jnl`	Evaluating the transferability potential of deep learning models for climate downscaling. CoRR Ayush Prasad, Paula Harder, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
2024	J	`jnl`	Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling. J. Mach. Learn. Res. Qidong Yang, Alex Hernández-García, Paula Harder, Venkatesh Ramesh, Prasanna Sattigeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
2024	J	`jnl`	Improving Molecular Modeling with Geometric GNNs: an Empirical Study. CoRR Ali Ramlaoui, Théo Saulus, Basile Terver, Victor Schmidt, David Rolnick, Fragkiskos D. Malliaros, Alexandre Duval
2024	—	`conf`	Insect Identification in the Wild: The AMI Dataset. ECCV (37) 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 A. August, Kim Bjerge, Alba Gomez Segura, Marc Bélisle, Yves Basset, Kent P. McFarland, David Roy, Toke Thomas Høye, Maxim Larrivée, David Rolnick
2024	J	`jnl`	Insect Identification in the Wild: The AMI Dataset. CoRR 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 A. August, Kim Bjerge, Alba Gomez Segura, Marc Bélisle, Yves Basset, Kent P. McFarland, David Roy, Toke Thomas Høye, Maxim Larrivée, David Rolnick
2024	J	`jnl`	Linear Weight Interpolation Leads to Transient Performance Gains. Trans. Mach. Learn. Res. Gaurav Iyer, Gintare Karolina Dziugaite, David Rolnick
2024	J	`jnl`	PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design. J. Mach. Learn. Res. Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick
2024	A*	`conf`	Position: Application-Driven Innovation in Machine Learning. ICML David Rolnick, Alán Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
2024	J	`jnl`	Predicting Species Occurrence Patterns from Partial Observations. CoRR Hager Radi Abdelwahed, Mélisande Teng, David Rolnick
2024	—	`conf`	Simultaneous Linear Connectivity of Neural Networks Modulo Permutation. ECML/PKDD (7) Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite
2024	J	`jnl`	Simultaneous linear connectivity of neural networks modulo permutation. CoRR Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite
2024	J	`jnl`	Stealing Part of a Production Language Model. CoRR 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
2024	A*	`conf`	Stealing part of a production language model. ICML 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
2024	J	`jnl`	Tree semantic segmentation from aerial image time series. CoRR Venkatesh Ramesh, Arthur Ouaknine, David Rolnick
2023	J	`jnl`	Bird Distribution Modelling using Remote Sensing and Citizen Science data. CoRR Mélisande Teng, Amna Elmustafa, Benjamin Akera, Hugo Larochelle, David Rolnick
2023	A*	`conf`	Bugs in the Data: How ImageNet Misrepresents Biodiversity. AAAI Alexandra Sasha Luccioni, David Rolnick
2023	A*	`conf`	ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. NeurIPS Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick
2023	J	`jnl`	ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. CoRR Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick
2023	—	`conf`	Deep Networks as Paths on the Manifold of Neural Representations. TAG-ML Richard D. Lange, Devin Kwok, Jordan Kyle Matelsky, Xinyue Wang, David Rolnick, Konrad P. Kording
2023	A*	`conf`	FAENet: Frame Averaging Equivariant GNN for Materials Modeling. ICML Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick
2023	J	`jnl`	FAENet: Frame Averaging Equivariant GNN for Materials Modeling. CoRR Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick
2023	J	`jnl`	FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models. CoRR Nikolaos-Ioannis Bountos, Arthur Ouaknine, David Rolnick
2023	J	`jnl`	Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling. CoRR Qidong Yang, Alex Hernández-García, Paula Harder, Venkatesh Ramesh, Prasanna Sattigeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
2023	J	`jnl`	Hard-Constrained Deep Learning for Climate Downscaling. J. Mach. Learn. Res. Paula Harder, Alex Hernández-García, Venkatesh Ramesh, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
2023	A*	`conf`	Hidden Symmetries of ReLU Networks. ICML J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick
2023	J	`jnl`	Hidden symmetries of ReLU networks. CoRR J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick
2023	J	`jnl`	Lightweight, Pre-trained Transformers for Remote Sensing Timeseries. CoRR Gabriel Tseng, Ivan Zvonkov, Mirali Purohit, David Rolnick, Hannah Kerner
2023	A*	`conf`	Maximal Initial Learning Rates in Deep ReLU Networks. ICML Gaurav Iyer, Boris Hanin, David Rolnick
2023	J	`jnl`	Multi-variable Hard Physical Constraints for Climate Model Downscaling. CoRR Jose González-Abad, Alex Hernández-García, Paula Harder, David Rolnick, José Manuel Gutiérrez Llorente
2023	A*	`conf`	Normalization Layers Are All That Sharpness-Aware Minimization Needs. NeurIPS Maximilian Müller, Tiffany Vlaar, David Rolnick, Matthias Hein
2023	J	`jnl`	Normalization Layers Are All That Sharpness-Aware Minimization Needs. CoRR Maximilian Müller, Tiffany Vlaar, David Rolnick, Matthias Hein
2023	J	`jnl`	On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions. CoRR Alvaro Carbonero, Alexandre Duval, Victor Schmidt, Santiago Miret, Alex Hernández-García, Yoshua Bengio, David Rolnick
2023	J	`jnl`	OpenForest: A data catalogue for machine learning in forest monitoring. CoRR Arthur Ouaknine, Teja Kattenborn, Etienne Laliberté, David Rolnick
2023	J	`jnl`	SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data. CoRR Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi Abdelwahed, Hugo Larochelle, David Rolnick
2023	A*	`conf`	SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data. NeurIPS Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi Abdelwahed, Hugo Larochelle, David Rolnick
2023	J	`jnl`	Tackling Climate Change with Machine Learning. ACM Comput. Surv. 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
2023	J	`jnl`	Towards Causal Representations of Climate Model Data. CoRR Julien Boussard, Chandni Nagda, Julia Kaltenborn, Charlotte Emilie Elektra Lange, Philippe Brouillard, Yaniv Gurwicz, Peer Nowack, David Rolnick
2022	J	`jnl`	Bugs in the Data: How ImageNet Misrepresents Biodiversity. CoRR Alexandra Sasha Luccioni, David Rolnick
2022	J	`jnl`	Clustering units in neural networks: upstream vs downstream information. Trans. Mach. Learn. Res. Richard D. Lange, David Rolnick, Konrad P. Kording
2022	A*	`conf`	Deep ReLU Networks Preserve Expected Length. ICLR Boris Hanin, Ryan S. Jeong, David Rolnick
2022	J	`jnl`	Maximal Initial Learning Rates in Deep ReLU Networks. CoRR Gaurav Iyer, Boris Hanin, David Rolnick
2022	J	`jnl`	On Neural Architecture Inductive Biases for Relational Tasks. CoRR Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake A. Richards, Guillaume Lajoie
2022	J	`jnl`	PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design. CoRR Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick
2022	J	`jnl`	TIML: Task-Informed Meta-Learning for Agriculture. CoRR Gabriel Tseng, Hannah Kerner, David Rolnick
2022	A*	`conf`	Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. NeurIPS Setareh Cohan, Nam Hee Kim, David Rolnick, Michiel van de Panne
2022	J	`jnl`	Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. CoRR Setareh Cohan, Nam Hee Kim, David Rolnick, Michiel van de Panne
2021	—	`conf`	ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models. NeurIPS Datasets and Benchmarks Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, David Rolnick
2021	J	`jnl`	ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models. CoRR Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, David Rolnick
2021	A*	`conf`	DC3: A learning method for optimization with hard constraints. ICLR Priya L. Donti, David Rolnick, J. Zico Kolter
2021	J	`jnl`	DC3: A learning method for optimization with hard constraints. CoRR Priya L. Donti, David Rolnick, J. Zico Kolter
2021	J	`jnl`	Deep ReLU Networks Preserve Expected Length. CoRR Boris Hanin, Ryan Jeong, David Rolnick
2021	J	`jnl`	Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification. CoRR Charles A. Kantor, Marta Skreta, Brice Rauby, Léonard Boussioux, Emmanuel Jehanno, Alexandra Luccioni, David Rolnick, Hugues Talbot
2021	J	`jnl`	Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks. Entropy Christopher Hillar, Tenzin Chan, Rachel Taubman, David Rolnick
2021	A*	`conf`	Techniques for Symbol Grounding with SATNet. NeurIPS Sever Topan, David Rolnick, Xujie Si
2021	J	`jnl`	Techniques for Symbol Grounding with SATNet. CoRR Sever Topan, David Rolnick, Xujie Si
2020	A*	`conf`	Reverse-engineering deep ReLU networks. ICML David Rolnick, Konrad P. Kording
2019	A*	`conf`	Complexity of Linear Regions in Deep Networks. ICML Boris Hanin, David Rolnick
2019	J	`jnl`	Complexity of Linear Regions in Deep Networks. CoRR Boris Hanin, David Rolnick
2019	A*	`conf`	Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. CVPR Yaron Meirovitch, Lu Mi, Hayk Saribekyan, Alexander Matveev, David Rolnick, Nir Shavit
2019	A*	`conf`	Deep ReLU Networks Have Surprisingly Few Activation Patterns. NeurIPS Boris Hanin, David Rolnick
2019	J	`jnl`	Deep ReLU Networks Have Surprisingly Few Activation Patterns. CoRR Boris Hanin, David Rolnick
2019	A*	`conf`	Experience Replay for Continual Learning. NeurIPS David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne
2019	J	`jnl`	Identifying Weights and Architectures of Unknown ReLU Networks. CoRR David Rolnick, Konrad P. Körding
2019	—	`conf`	Measuring and regularizing networks in function space. ICLR (Poster) Ari S. Benjamin, David Rolnick, Konrad P. Körding
2019	A*	`conf`	Randomized Experimental Design via Geographic Clustering. KDD David Rolnick, Kevin Aydin, Jean Pouget-Abadie, Shahab Kamali, Vahab S. Mirrokni, Amir Najmi
2019	J	`jnl`	Tackling Climate Change with Machine Learning. CoRR 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
2018	J	`jnl`	Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. CoRR Yaron Meirovitch, Lu Mi, Hayk Saribekyan, Alexander Matveev, David Rolnick, Casimir Wierzynski, Nir Shavit
2018	J	`jnl`	Experience Replay for Continual Learning. CoRR David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne
2018	A*	`conf`	How to Start Training: The Effect of Initialization and Architecture. NeurIPS Boris Hanin, David Rolnick
2018	J	`jnl`	How to Start Training: The Effect of Initialization and Architecture. CoRR Boris Hanin, David Rolnick
2018	J	`jnl`	Measuring and regularizing networks in function space. CoRR Ari S. Benjamin, David Rolnick, Konrad P. Körding
2018	—	`conf`	The power of deeper networks for expressing natural functions. ICLR (Poster) David Rolnick, Max Tegmark
2017	J	`jnl`	Deep Learning is Robust to Massive Label Noise. CoRR David Rolnick, Andreas Veit, Serge J. Belongie, Nir Shavit
2017	—	`conf`	Markov Transitions between Attractor States in a Recurrent Neural Network. AAAI Spring Symposia Jeremy Bernstein, Ishita Dasgupta, David Rolnick, Haim Sompolinsky
2017	J	`jnl`	Morphological Error Detection in 3D Segmentations. CoRR David Rolnick, Yaron Meirovitch, Toufiq Parag, Hanspeter Pfister, Viren Jain, Jeff W. Lichtman, Edward S. Boyden, Nir Shavit
2017	J	`jnl`	On the classification of Stanley sequences. Eur. J. Comb. David Rolnick
2017	J	`jnl`	Quantitative (p, q) theorems in combinatorial geometry. Discret. Math. David Rolnick, Pablo Soberón
2017	J	`jnl`	Quantitative Combinatorial Geometry for Continuous Parameters. Discret. Comput. Geom. Jesús A. De Loera, Reuben N. La Haye, David Rolnick, Pablo Soberón
2017	J	`jnl`	Quantitative Tverberg Theorems Over Lattices and Other Discrete Sets. Discret. Comput. Geom. Jesús A. De Loera, Reuben N. La Haye, David Rolnick, Pablo Soberón
2017	J	`jnl`	The power of deeper networks for expressing natural functions. CoRR David Rolnick, Max Tegmark
2016	J	`jnl`	A Multi-Pass Approach to Large-Scale Connectomics. CoRR 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
2016	J	`jnl`	Algorithmic aspects of Tverberg's Theorem. CoRR David Rolnick, Pablo Soberón
2016	J	`jnl`	GeoCUTS: Geographic Clustering Using Travel Statistics. CoRR David Rolnick, Kevin Aydin, Shahab Kamali, Vahab S. Mirrokni, Amir Najmi
2016	J	`jnl`	Novel structures in Stanley sequences. Discret. Math. Richard A. Moy, David Rolnick
2015	J	`jnl`	Acyclic Subgraphs of Planar Digraphs. Electron. J. Comb. Noah Golowich, David Rolnick
2015	B	`conf`	Graph-Coloring Ideals: Nullstellensatz Certificates, Gröbner Bases for Chordal Graphs, and Hardness of Gröbner Bases. ISSAC Jesús A. De Loera, Susan Margulies, Michael Pernpeintner, Eric Riedl, David Rolnick, Gwen Spencer, Despina Stasi, Jon Swenson
2015	J	`jnl`	Novel structures in Stanley sequences. CoRR Richard A. Moy, David Rolnick
2015	J	`jnl`	On the growth of Stanley sequences. Discret. Math. David Rolnick, Praveen S. Venkataramana
2015	J	`jnl`	On the robust hardness of Gröbner basis computation. CoRR Gwen Spencer, David Rolnick
2014	J	`jnl`	Gröbner Bases and Nullstellensätze for Graph-Coloring Ideals. CoRR Jesús A. De Loera, Susan Margulies, Michael Pernpeintner, Eric Riedl, David Rolnick, Gwen Spencer, Despina Stasi, Jon Swenson
2013	J	`jnl`	The on-line degree Ramsey number of cycles. Discret. Math. David Rolnick
2011	J	`jnl`	Trees with an On-Line Degree Ramsey Number of Four. Electron. J. Comb. David Rolnick