5 citations · 10 across the 10 of their papers we have counts for
4 papers · 1 filter
Predicting kernel regression learning curves from only raw data statistics
Dhruva Karkada, Joseph Turnbull, Yuxi Liu +1
We study kernel regression with common rotation-invariant kernels on real datasets including CIFAR-5m, SVHN, and ImageNet. We give a theoretical framework that predicts learning cu…
Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks
Daniel Kunin, Giovanni Luca Marchetti, Feng Chen +5
What features neural networks learn, and how, remains an open question. In this paper, we introduce Alternating Gradient Flows (AGF), an algorithmic framework that describes the dy…
On the Emergence of Linear Analogies in Word Embeddings
Daniel J. Korchinski, Dhruva Karkada, Yasaman Bahri +1
Models such as Word2Vec and GloVe construct word embeddings based on the co-occurrence probability of words and in text corpora. The resulting vectors not on…
Closed-Form Training Dynamics Reveal Learned Features and Linear Structure in Word2Vec-like Models
Dhruva Karkada, James B. Simon, Yasaman Bahri +1
Self-supervised word embedding algorithms such as word2vec provide a minimal setting for studying representation learning in language modeling. We examine the quartic Taylor approx…