1 citations · 1 across the 5 of their papers we have counts for
5 papers
Building Interpretable Models for Moral Decision-Making
Mayank Goel, Aritra Das, Paras Chopra
We build a custom transformer model to study how neural networks make moral decisions on trolley-style dilemmas. The model processes structured scenarios using embeddings that enco…
HomID : Benchmarking Intrinsic Dimension Estimators on Homogenous Manifolds with Anisotropic Embeddings
Aritra Das, Joseph T. Iosue, Victor V. Albert
The manifold hypothesis suggests that data lies on manifolds with smaller intrinsic dimension (ID) than their ambient dimension. However there is no empirical agreement on the esti…
Grokking Modular Polynomials
Darshil Doshi, Tianyu He, Aritra Das +1
Neural networks readily learn a subset of the modular arithmetic tasks, while failing to generalize on the rest. This limitation remains unmoved by the choice of architecture and t…
Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks
Tianyu He, Darshil Doshi, Aritra Das +1
Large language models can solve tasks that were not present in the training set. This capability is believed to be due to in-context learning and skill composition. In this work, w…
To grok or not to grok: Disentangling generalization and memorization on corrupted algorithmic datasets
Darshil Doshi, Aritra Das, Tianyu He +1
Robust generalization is a major challenge in deep learning, particularly when the number of trainable parameters is very large. In general, it is very difficult to know if the net…