b'David Rolnick'

research

∙ 08/02/2023

Multi-variable Hard Physical Constraints for Climate Model Downscaling

Global Climate Models (GCMs) are the primary tool to simulate climate ev...

0 Jose González-Abad, et al. ∙

research

∙ 06/09/2023

Hidden symmetries of ReLU networks

The parameter space for any fixed architecture of feedforward ReLU neura...

0 J. Elisenda Grigsby, et al. ∙

research

∙ 06/07/2023

Normalization Layers Are All That Sharpness-Aware Minimization Needs

Sharpness-aware minimization (SAM) was proposed to reduce sharpness of m...

0 Maximilian Mueller, et al. ∙

research

∙ 05/23/2023

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Climate simulations are essential in guiding our understanding of climat...

0 Qidong Yang, et al. ∙

research

∙ 05/01/2023

Bird Distribution Modelling using Remote Sensing and Citizen Science data

Climate change is a major driver of biodiversity loss, changing the geog...

0 Mélisande Teng, et al. ∙

research

∙ 04/27/2023

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Machine learning algorithms for parsing remote sensing data have a wide ...

0 Gabriel Tseng, et al. ∙

research

∙ 12/14/2022

Maximal Initial Learning Rates in Deep ReLU Networks

Training a neural network requires choosing a suitable learning rate, in...

0 Gaurav Iyer, et al. ∙

research

∙ 11/22/2022

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

Mitigating the climate crisis requires a rapid transition towards lower ...

0 Alexandre Duval, et al. ∙

research

∙ 10/24/2022

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

Policies produced by deep reinforcement learning are typically character...

0 Setareh Cohen, et al. ∙

research

∙ 08/24/2022

Bugs in the Data: How ImageNet Misrepresents Biodiversity

ImageNet-1k is a dataset often used for benchmarking machine learning (M...

0 Alexandra Sasha Luccioni, et al. ∙

research

∙ 08/08/2022

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

The availability of reliable, high-resolution climate and weather data i...

15 Paula Harder, et al. ∙

research

∙ 06/09/2022

On Neural Architecture Inductive Biases for Relational Tasks

Current deep learning approaches have shown good in-distribution general...

20 Giancarlo Kerg, et al. ∙

research

∙ 02/04/2022

TIML: Task-Informed Meta-Learning for Agriculture

Labeled datasets for agriculture are extremely spatially imbalanced. Whe...

0 Gabriel Tseng, et al. ∙

research

∙ 11/29/2021

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

Numerical simulations of Earth's weather and climate require substantial...

0 Salva Rühling Cachay, et al. ∙

research

∙ 06/16/2021

Techniques for Symbol Grounding with SATNet

Many experts argue that the future of artificial intelligence is limited...

0 Sever Topan, et al. ∙

research

∙ 04/25/2021

DC3: A learning method for optimization with hard constraints

Large optimization problems with hard constraints arise in many settings...

0 Priya L. Donti, et al. ∙

research

∙ 02/21/2021

Deep ReLU Networks Preserve Expected Length

Assessing the complexity of functions computed by a neural network helps...

0 Boris Hanin, et al. ∙

research

∙ 10/02/2019

Identifying Weights and Architectures of Unknown ReLU Networks

The output of a neural network depends on its parameters in a highly non...

0 David Rolnick, et al. ∙

research

∙ 06/10/2019

Tackling Climate Change with Machine Learning

Climate change is one of the greatest challenges facing humanity, and we...

1 David Rolnick, et al. ∙

research

∙ 06/03/2019

Deep ReLU Networks Have Surprisingly Few Activation Patterns

The success of deep networks has been attributed in part to their expres...

0 Boris Hanin, et al. ∙

research

∙ 01/25/2019

Complexity of Linear Regions in Deep Networks

It is well-known that the expressivity of a neural network depends on it...

0 Boris Hanin, et al. ∙

research

∙ 12/04/2018

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

Pixel-accurate tracking of objects is a key element in many computer vis...

2 Yaron Meirovitch, et al. ∙

research

∙ 11/28/2018

Experience Replay for Continual Learning

Continual learning is the problem of learning new tasks or knowledge whi...

0 David Rolnick, et al. ∙

research

∙ 05/21/2018

Measuring and regularizing networks in function space

Neural network optimization is often conceptualized as optimizing parame...

0 Ari S. Benjamin, et al. ∙

research

∙ 03/05/2018

How to Start Training: The Effect of Initialization and Architecture

We investigate the effects of initialization and architecture on the sta...

0 Boris Hanin, et al. ∙

research

∙ 05/30/2017

Morphological Error Detection in 3D Segmentations

Deep learning algorithms for connectomics rely upon localized classifica...

0 David Rolnick, et al. ∙

research

∙ 05/30/2017

Deep Learning is Robust to Massive Label Noise

Deep neural networks trained on large supervised datasets have led to im...

0 David Rolnick, et al. ∙

research

∙ 05/16/2017

The power of deeper networks for expressing natural functions

It is well-known that neural networks are universal approximators, but t...

0 David Rolnick, et al. ∙

research

∙ 12/07/2016

A Multi-Pass Approach to Large-Scale Connectomics

The field of connectomics faces unprecedented "big data" challenges. To ...

0 Yaron Meirovitch, et al. ∙

research

∙ 08/29/2016

Why does deep and cheap learning work so well?

We show how the success of deep learning could depend not only on mathem...

0 Henry W. Lin, et al. ∙

research

∙ 11/19/2015

On the robust hardness of Gröbner basis computation

We introduce a new problem in the approximate computation of Gröbner bas...

0 Gwen Spencer, et al. ∙

David Rolnick

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