20 citations · 30 across the 2 of their papers we have counts for
6 papers
Self-supervised models of audio effectively explain human cortical responses to speech
Aditya R. Vaidya, Shailee Jain, Alexander G. Huth
Self-supervised language models are very effective at predicting high-level cortical responses during language comprehension. However, the best current models of lower-level audito…
Physically Plausible Pose Refinement using Fully Differentiable Forces
Akarsh Kumar, Aditya R. Vaidya, Alexander G. Huth
All hand-object interaction is controlled by forces that the two bodies exert on each other, but little work has been done in modeling these underlying forces when doing pose and c…
Multi-timescale Representation Learning in LSTM Language Models
Shivangi Mahto, Vy A. Vo, Javier S. Turek +1
Language models must capture statistical dependencies between words at timescales ranging from very short to very long. Earlier work has demonstrated that dependencies in natural l…
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
Javier S. Turek, Shailee Jain, Vy Vo +3
Recent work has shown that topological enhancements to recurrent neural networks (RNNs) can increase their expressiveness and representational capacity. Two popular enhancements ar…
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
Zaiwei Zhang, Zhenpei Yang, Chongyang Ma +4
We present a deep generative scene modeling technique for indoor environments. Our goal is to train a generative model using a feed-forward neural network that maps a prior distrib…
PrAGMATiC: a Probabilistic and Generative Model of Areas Tiling the Cortex
Alexander G. Huth, Thomas L. Griffiths, Frederic E. Theunissen +1
Much of the human cortex seems to be organized into topographic cortical maps. Yet few quantitative methods exist for characterizing these maps. To address this issue we developed…