40 citations · 44 across the 6 of their papers we have counts for
7 papers
Network of Theseus (like the ship)
Vighnesh Subramaniam, Colin Conwell, Boris Katz +2
A standard assumption in deep learning is that the inductive bias introduced by a neural network architecture must persist from training through inference. The architecture you tra…
Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models
Colin Conwell, Rupert Tawiah-Quashie, Tomer Ullman
Despite remarkable progress in multi-modal AI research, there is a salient domain in which modern AI continues to lag considerably behind even human children: the reliable deployme…
Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics
Colin Conwell, Christopher Hamblin, Chelsea Boccagno +4
When we experience a visual stimulus as beautiful, how much of that experience derives from perceptual computations we cannot describe versus conceptual knowledge we can readily tr…
Training the Untrainable: Introducing Inductive Bias via Representational Alignment
Vighnesh Subramaniam, David Mayo, Colin Conwell +4
We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. We call a network…
Revealing Vision-Language Integration in the Brain with Multimodal Networks
Vighnesh Subramaniam, Colin Conwell, Christopher Wang +4
We use (multi)modal deep neural networks (DNNs) to probe for sites of multimodal integration in the human brain by predicting stereoencephalography (SEEG) recordings taken while hu…
Testing Relational Understanding in Text-Guided Image Generation
Colin Conwell, Tomer Ullman
Relations are basic building blocks of human cognition. Classic and recent work suggests that many relations are early developing, and quickly perceived. Machine models that aspire…
On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation
Binxu Wang, David Mayo, Arturo Deza +2
Self-supervised learning is a powerful way to learn useful representations from natural data. It has also been suggested as one possible means of building visual representation in…