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09/30/2022
FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective
A foundational set of findable, accessible, interoperable, and reusable ...
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03/23/2020
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Despite the fact that the loss functions of deep neural networks are hig...
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08/29/2019
Sparse, Low-bias, and Scalable Estimation of High Dimensional Vector Autoregressive Models via Union of Intersections
Vector autoregressive (VAR) models are widely used for causal discovery ...
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05/23/2019
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Linear dimensionality reduction methods are commonly used to extract low...
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05/23/2019
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Interrogating Learned Representations
Interpretable representations of data are useful for testing a hypothesi...
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01/29/2019
Numerically Recovering the Critical Points of a Deep Linear Autoencoder
Numerically locating the critical points of non-convex surfaces is a lon...
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05/22/2018
Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces
Neuromorphic architectures achieve low-power operation by using many sim...
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03/26/2018
Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex
A fundamental challenge in neuroscience is to understand what structure ...
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05/22/2017
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
The increasing size and complexity of scientific data could dramatically...
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05/13/2015