David Rolnick

Krishnamurthy (Dj) Dvijotham

CoRR, 2024

Application-Driven Innovation in Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Stealing Part of a Production Language Model.

[BibT_eX]

[DOI]

Nicholas Carlini

Daniel Paleka

CoRR, 2024

Dataset Difficulty and the Role of Inductive Bias.

[BibT_eX]

[DOI]

Devin Kwok

Nikhil Anand

Jonathan Frankle

Gintare Karolina Dziugaite

CoRR, 2024

2023

Tackling Climate Change with Machine Learning.

[BibT_eX]

[DOI]

ACM Comput. Surv., 2023

FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models.

[BibT_eX]

[DOI]

Nikolaos-Ioannis Bountos

Arthur Ouaknine

Charlotte Emilie Elektra Lange

CoRR, 2023

Towards Causal Representations of Climate Model Data.

[BibT_eX]

[DOI]

Julien Boussard

Chandni Nagda

Julia Kaltenborn

CoRR, 2023

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data.

[BibT_eX]

[DOI]

Hager Radi Abdelwahed

Hugo Larochelle

CoRR, 2023

OpenForest: A data catalogue for machine learning in forest monitoring.

[BibT_eX]

[DOI]

CoRR, 2023

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions.

[BibT_eX]

[DOI]

Yoshua Bengio

CoRR, 2023

Multi-variable Hard Physical Constraints for Climate Model Downscaling.

[BibT_eX]

[DOI]

Jose González-Abad

Paula Harder

José Manuel Gutiérrez Llorente

CoRR, 2023

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling.

[BibT_eX]

[DOI]

Qidong Yang

CoRR, 2023

Bird Distribution Modelling using Remote Sensing and Citizen Science data.

[BibT_eX]

[DOI]

CoRR, 2023

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries.

[BibT_eX]

[DOI]

CoRR, 2023

Deep Networks as Paths on the Manifold of Neural Representations.

[BibT_eX]

[DOI]

Proceedings of the Topological, 2023

SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data.

[BibT_eX]

[DOI]

Hager Radi Abdelwahed

Hugo Larochelle

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Normalization Layers Are All That Sharpness-Aware Minimization Needs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning.

[BibT_eX]

[DOI]

Julia Kaltenborn

Charlotte E. E. Lange

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Maximal Initial Learning Rates in Deep ReLU Networks.

[BibT_eX]

[DOI]

Gaurav Iyer

Proceedings of the International Conference on Machine Learning, 2023

Hidden Symmetries of ReLU Networks.

[BibT_eX]

[DOI]

J. Elisenda Grigsby

Kathryn Lindsey

Proceedings of the International Conference on Machine Learning, 2023

FAENet: Frame Averaging Equivariant GNN for Materials Modeling.

[BibT_eX]

[DOI]

Alexandre Duval

Victor Schmidt

Santiago Miret

Fragkiskos D. Malliaros

Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2023

Bugs in the Data: How ImageNet Misrepresents Biodiversity.

[BibT_eX]

[DOI]

Alexandra Sasha Luccioni

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Clustering units in neural networks: upstream vs downstream information.

[BibT_eX]

[DOI]

Richard D. Lange

Konrad P. Kording

Trans. Mach. Learn. Res., 2022

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design.

[BibT_eX]

[DOI]

CoRR, 2022

Generating physically-consistent high-resolution climate data with hard-constrained neural networks.

[BibT_eX]

[DOI]

Campbell D. Watson

Daniela Szwarcman

David S. Rolnick

CoRR, 2022

Neural Networks as Paths through the Space of Representations.

[BibT_eX]

[DOI]

CoRR, 2022

On Neural Architecture Inductive Biases for Relational Tasks.

[BibT_eX]

[DOI]

CoRR, 2022

TIML: Task-Informed Meta-Learning for Agriculture.

[BibT_eX]

[DOI]

Gabriel Tseng

Hannah Kerner

CoRR, 2022

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Deep ReLU Networks Preserve Expected Length.

[BibT_eX]

[DOI]

Ryan S. Jeong

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks.

[BibT_eX]

[DOI]

Entropy, 2021

Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification.

[BibT_eX]

[DOI]

CoRR, 2021

Techniques for Symbol Grounding with SATNet.

[BibT_eX]

[DOI]

Sever Topan

Xujie Si

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

DC3: A learning method for optimization with hard constraints.

[BibT_eX]

[DOI]

Priya L. Donti

J. Zico Kolter

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Reverse-engineering deep ReLU networks.

[BibT_eX]

[DOI]

Konrad P. Kording

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Identifying Weights and Architectures of Unknown ReLU Networks.

[BibT_eX]

[DOI]

Konrad P. Körding

CoRR, 2019

Experience Replay for Continual Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deep ReLU Networks Have Surprisingly Few Activation Patterns.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Randomized Experimental Design via Geographic Clustering.

[BibT_eX]

[DOI]

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Complexity of Linear Regions in Deep Networks.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Measuring and regularizing networks in function space.

[BibT_eX]

[DOI]

Ari S. Benjamin

Konrad P. Körding

Proceedings of the 7th International Conference on Learning Representations, 2019

Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics.

[BibT_eX]

[DOI]

CoRR, 2018

How to Start Training: The Effect of Initialization and Architecture.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The power of deeper networks for expressing natural functions.

[BibT_eX]

[DOI]

Max Tegmark

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

On the classification of Stanley sequences.

[BibT_eX]

[DOI]

Eur. J. Comb., 2017

Quantitative (p, q) theorems in combinatorial geometry.

[BibT_eX]

[DOI]

Pablo Soberón

Discret. Math., 2017

Quantitative Tverberg Theorems Over Lattices and Other Discrete Sets.

[BibT_eX]

[DOI]

Discret. Comput. Geom., 2017

Quantitative Combinatorial Geometry for Continuous Parameters.

[BibT_eX]

[DOI]

Discret. Comput. Geom., 2017

Deep Learning is Robust to Massive Label Noise.

[BibT_eX]

[DOI]

CoRR, 2017

Morphological Error Detection in 3D Segmentations.

[BibT_eX]

[DOI]

CoRR, 2017

Markov Transitions between Attractor States in a Recurrent Neural Network.

[BibT_eX]

[DOI]

Proceedings of the 2017 AAAI Spring Symposia, 2017

2016

Novel structures in Stanley sequences.

[BibT_eX]

[DOI]

Richard A. Moy

Discret. Math., 2016

Algorithmic aspects of Tverberg's Theorem.

[BibT_eX]

[DOI]

Pablo Soberón

CoRR, 2016

GeoCUTS: Geographic Clustering Using Travel Statistics.

[BibT_eX]

[DOI]

CoRR, 2016

A Multi-Pass Approach to Large-Scale Connectomics.

[BibT_eX]

[DOI]

Seymour Knowles-Barley

Thouis Raymond Jones

Hanspeter Pfister

Jeff William Lichtman

Nir Shavit

CoRR, 2016

2015

On the growth of Stanley sequences.

[BibT_eX]

[DOI]

Praveen S. Venkataramana

Discret. Math., 2015

On the robust hardness of Gröbner basis computation.

[BibT_eX]

[DOI]

Gwen Spencer

CoRR, 2015

Acyclic Subgraphs of Planar Digraphs.

[BibT_eX]

[DOI]

Noah Golowich

Electron. J. Comb., 2015

Graph-Coloring Ideals: Nullstellensatz Certificates, Gröbner Bases for Chordal Graphs, and Hardness of Gröbner Bases.

[BibT_eX]

[DOI]

Proceedings of the 2015 ACM on International Symposium on Symbolic and Algebraic Computation, 2015

2014

Gröbner Bases and Nullstellensätze for Graph-Coloring Ideals.

[BibT_eX]

[DOI]

CoRR, 2014

2013

The on-line degree Ramsey number of cycles.

[BibT_eX]

[DOI]

Discret. Math., 2013

2011

Trees with an On-Line Degree Ramsey Number of Four.

[BibT_eX]

[DOI]