Brown Bag Talk: Jane Li
111 Krieger Hall 3400 N Charles St, Baltimore, MD, United StatesWhat do recurrent neural networks learn and use from morpho-phonological alternations?: a case study of Turkish vowel harmony
What do recurrent neural networks learn and use from morpho-phonological alternations?: a case study of Turkish vowel harmony
Speech production and its neural substrates: evidence from sound-based errors in Primary Progressive Aphasia
Speech production and its neural substrates: evidence from sound-based errors in Primary Progressive Aphasia
Dynamic, social vision highlights gaps between deep learning and humans
Social visual and language processing during a naturalistic movie
Differential effects of syntactic complexity in congenitally blind and sighted individuals: evidence from self-paced listening and reading
RNNs too expressive to properly learn local phonological patterns? Although recurrent neural networks with long short-term memory mechanisms (LSTMs) can learn many existing phonological patterns, they vastly overgenerate. This raises questions about whether LSTMs are suitable models for understanding how humans learn phonology, similar to phonological theories that overgenerate. In this work, we examine whether […]
presented by PhD Student Manasi Malik Title and abstract to come.
Generative (Mental) World Explorer Abstract: Understanding, navigating, and exploring the 3D physical real world has long been a central challenge in the development of artificial intelligence. In this work, we take a step toward this goal by introducing GenEx, a system capable of planning complex embodied world exploration, guided by its generative imagination that forms priors […]
This talk is rescheduled from April 11 to April 18. High-dimensional structure underlying individual differences in naturalistic visual experience.
This talk is rescheduled from April 18 to April 25. Neural computations underlying human social evaluations from visual stimuli Abstract: Humans easily make social evaluations from visual scenes, but the computational mechanisms in the brain that support this ability remain unknown. Here, we test two hypotheses raised by prior work: one proposes that people recognize […]
Rapid unsupervised alignment with the natural image manifold Abstract:There is a stark contrast between the nature of feature learning in biological and artificial vision. While brains learn without explicit supervision and with little data, deep neural networks require supervised feedback and massive training sets. Here we show that a surprisingly simple unsupervised learning algorithm can […]