Search Results for author:
Found 8 papers, 2 papers with code
Voice EHR: Introducing Multimodal Audio Data for Health
This report introduces a consortium of partners for global work, presents the application used for data collection, and showcases the potential of informative voice EHR to advance the scalability and diversity of audio AI.
Critical Learning Periods Emerge Even in Deep Linear Networks
Critical learning periods are periods early in development where temporary sensory deficits can have a permanent effect on behavior and learned representations.
Critical Learning Periods for Multisensory Integration in Deep Networks
We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early phases of training.
Gacs-Korner Common Information Variational Autoencoder
We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique to each.
Learning rule influences recurrent network representations but not attractor structure in decision-making tasks
For relatively complex tasks, we find that attractor topology is invariant to the choice of learning rule, but representational geometry is not.
A mechanistic multi-area recurrent network model of decision-making
Recurrent neural networks (RNNs) trained on neuroscience-based tasks have been widely used as models for cortical areas performing analogous tasks.
Redundant Information Neural Estimation
We introduce the Redundant Information Neural Estimator (RINE), a method that allows efficient estimation for the component of information about a target variable that is common to a set of sources, previously referred to as the “redundant information.” We show that existing definitions of the redundant information can be recast in terms of an optimization over a family of deterministic or stochastic functions.
Usable Information and Evolution of Optimal Representations During Training
We introduce a notion of usable information contained in the representation learned by a deep network, and use it to study how optimal representations for the task emerge during training.
