11 citations · 13 across the 5 of their papers we have counts for
5 papers
Cost-Effective, Low Latency Vector Search with Azure Cosmos DB
Nitish Upreti, Harsha Vardhan Simhadri, Hari Sudan Sundar +33
Vector indexing enables semantic search over diverse corpora and has become an important interface to databases for both users and AI agents. Efficient vector search requires deep…
Global Convergence and Rich Feature Learning in -Layer Infinite-Width Neural Networks under P Parametrization
Zixiang Chen, Greg Yang, Qingyue Zhao +1
Despite deep neural networks' powerful representation learning capabilities, theoretical understanding of how networks can simultaneously achieve meaningful feature learning and gl…
Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks
Greg Yang, Dingli Yu, Chen Zhu +1
By classifying infinite-width neural networks and identifying the *optimal* limit, Tensor Programs IV and V demonstrated a universal way, called P, for *widthwise hyperparameter…
A Spectral Condition for Feature Learning
Greg Yang, James B. Simon, Jeremy Bernstein
The push to train ever larger neural networks has motivated the study of initialization and training at large network width. A key challenge is to scale training so that a network'…
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki +3
We study the first gradient descent step on the first-layer parameters in a two-layer neural network: $f(\boldsymbol{x}) = \frac{1}{\sqrt{N}}\boldsymbol{a}^\topσ(\…