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02/27/2023
Analyzing Populations of Neural Networks via Dynamical Model Embedding
A core challenge in the interpretation of deep neural networks is identi...
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10/15/2022
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an important driver of progress in artificial...
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11/01/2021
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Recurrent neural networks (RNNs) are powerful models for processing time...
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11/04/2020
Reverse engineering learned optimizers reveals known and novel mechanisms
Learned optimizers are algorithms that can themselves be trained to solv...
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10/28/2020
The geometry of integration in text classification RNNs
Despite the widespread application of recurrent neural networks (RNNs) a...
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04/17/2020
How recurrent networks implement contextual processing in sentiment analysis
Neural networks have a remarkable capacity for contextual processing–usi...
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07/19/2019
Universality and individuality in neural dynamics across large populations of recurrent networks
Task-based modeling with recurrent neural networks (RNNs) has emerged as...
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06/25/2019
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
Recurrent neural networks (RNNs) are a widely used tool for modeling seq...
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06/20/2018
Task-Driven Convolutional Recurrent Models of the Visual System
Feed-forward convolutional neural networks (CNNs) are currently state-of...
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03/16/2018
A dataset and architecture for visual reasoning with a working memory
A vexing problem in artificial intelligence is reasoning about events th...
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11/28/2017
Recurrent Segmentation for Variable Computational Budgets
State-of-the-art systems for semantic image segmentation utilize feed-fo...
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11/29/2016
Capacity and Trainability in Recurrent Neural Networks
Two potential bottlenecks on the expressiveness of recurrent neural netw...
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11/28/2016
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
There exist many problem domains where the interpretability of neural ne...
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10/19/2016
Making brain-machine interfaces robust to future neural variability
A major hurdle to clinical translation of brain-machine interfaces (BMIs...
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08/22/2016
LFADS - Latent Factor Analysis via Dynamical Systems
Neuroscience is experiencing a data revolution in which many hundreds or...
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11/16/2015
A Neural Transducer
Sequence-to-sequence models have achieved impressive results on various ...
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12/19/2014
