4 papers · 1 filter
Quantum-inspired Benchmark for Estimating Intrinsic Dimension
Aritra Das, Joseph T. Iosue, Victor V. Albert
Machine learning models can generalize well on real-world datasets. According to the manifold hypothesis, this is possible because datasets lie on a latent manifold with small intr…
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…