20 citations · 45 across the 5 of their papers we have counts for
5 papers
Generative causal testing to bridge data-driven models and scientific theories in language neuroscience
Richard Antonello, Chandan Singh, Shailee Jain +5
Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is uncl…
Explaining black box text modules in natural language with language models
Chandan Singh, Aliyah R. Hsu, Richard Antonello +4
Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have cre…
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…
Spatial Language Representation with Multi-Level Geocoding
Sayali Kulkarni, Shailee Jain, Mohammad Javad Hosseini +3
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations. The Earth's surface is represented using space-filling curves that decompose…
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…