21 citations · 28 across the 5 of their papers we have counts for
8 papers
The Male Abstraction Bias in Internet Culture and Large Language Models
Douglas Guilbeault, Bhargav Srinivasa Desikan
Conceptual abstraction and gender are two equally central aspects of the human mind, yet they have traditionally been studied in isolation from one another, as though they were fun…
Artificial Intelligence and the New Science of Culture
Douglas R. Guilbeault, Bhargav Srinivasa Desikan, James A. Evans
Cognition and culture have long been treated as parallel objects of study, joined more by metaphor than by mechanism. We argue they are co-constituted in a manner now empirically t…
iLLuMinaTE: An LLM-XAI Framework Leveraging Social Science Explanation Theories Towards Actionable Student Performance Feedback
Vinitra Swamy, Davide Romano, Bhargav Srinivasa Desikan +2
Recent advances in eXplainable AI (XAI) for education have highlighted a critical challenge: ensuring that explanations for state-of-the-art AI models are understandable for non-te…
Divergences in Color Perception between Deep Neural Networks and Humans
Ethan O. Nadler, Elise Darragh-Ford, Bhargav Srinivasa Desikan +4
Deep neural networks (DNNs) are increasingly proposed as models of human vision, bolstered by their impressive performance on image classification and object recognition tasks. Yet…
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models
Mark Chu, Bhargav Srinivasa Desikan, Ethan O. Nadler +3
Natural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e.g., co-occurrence) correlates with meaning.…
comp-syn: Perceptually Grounded Word Embeddings with Color
Bhargav Srinivasa Desikan, Tasker Hull, Ethan O. Nadler +4
Popular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language. Here, we i…
Kernel-Based Ensemble Learning in Python
Benjamin Guedj, Bhargav Srinivasa Desikan
We propose a new supervised learning algorithm, for classification and regression problems where two or more preliminary predictors are available. We introduce \texttt{KernelCobra}…
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
Benjamin Guedj, Bhargav Srinivasa Desikan
We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several en…