4 citations · 7 across the 4 of their papers we have counts for
6 papers
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…