23 citations · 37 across the 5 of their papers we have counts for
6 papers
Learning temporal relationships between symbols with Laplace Neural Manifolds
Marc W. Howard, Zahra G. Esfahani, Bao Le +1
Firing across populations of neurons in many regions of the mammalian brain maintains a temporal memory, a neural timeline of the recent past. Behavioral results demonstrate that p…
A deep convolutional neural network that is invariant to time rescaling
Brandon G. Jacques, Zoran Tiganj, Aakash Sarkar +2
Human learners can readily understand speech, or a melody, when it is presented slower or faster than usual. Although deep convolutional neural networks (CNNs) are extremely powerf…
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales
Brandon Jacques, Zoran Tiganj, Marc W. Howard +1
Extracting temporal relationships over a range of scales is a hallmark of human perception and cognition -- and thus it is a critical feature of machine learning applied to real-wo…
Estimating scale-invariant future in continuous time
Zoran Tiganj, Samuel J. Gershman, Per B. Sederberg +1
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous tim…
Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world
Tyler A. Spears, Brandon G. Jacques, Marc W. Howard +1
In both the human brain and any general artificial intelligence (AI), a representation of the past is necessary to predict the future. However, perfect storage of all experiences i…
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing
Justin Ziniel, Philip Schniter, Per Sederberg
For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (…