activity
20222026
most citedNo Free Prune: Information-Theoretic Barriers to Pruning at Initialization

2 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Speculative Speculative Decoding

Tanishq Kumar, Tri Dao, Avner May

Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a fast draft model to predict upco…

cs.CL2025★ 2 cited

Overtrained Language Models Are Harder to Fine-Tune

Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen +5

Large language models are pre-trained on ever-growing token budgets under the assumption that better pre-training performance translates to improved downstream models. In this work…

cs.LG2024

Scaling Laws for Precision

Tanishq Kumar, Zachary Ankner, Benjamin F. Spector +6

Low precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. In this work, we devise "precision-aware…

cs.LG2024★ 1 cited

Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex

Tanishq Kumar, Blake Bordelon, Cengiz Pehlevan +2

Does learning of task-relevant representations stop when behavior stops changing? Motivated by recent theoretical advances in machine learning and the intuitive observation that hu…

stat.ML2024★ 2 cited

No Free Prune: Information-Theoretic Barriers to Pruning at Initialization

Tanishq Kumar, Kevin Luo, Mark Sellke

The existence of "lottery tickets" arXiv:1803.03635 at or near initialization raises the tantalizing question of whether large models are necessary in deep learning, or whether spa…

stat.ML2023

Grokking as the Transition from Lazy to Rich Training Dynamics

Tanishq Kumar, Blake Bordelon, Samuel J. Gershman +1

We propose that the grokking phenomenon, where the train loss of a neural network decreases much earlier than its test loss, can arise due to a neural network transitioning from la…

cs.CV2022

Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap

Mengmi Zhang, Elisa Pavarino, Xiao Liu +20

As AI becomes increasingly embedded in daily life, ascertaining whether an agent is human is critical. We systematically benchmark AI's ability to imitate humans in three language…