K
Kilian Q. Weinberger
Researcher at Cornell University
Publications - 241
Citations - 71535
Kilian Q. Weinberger is an academic researcher from Cornell University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 76, co-authored 222 publications receiving 49707 citations. Previous affiliations of Kilian Q. Weinberger include University of Washington & Washington University in St. Louis.
Papers
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Proceedings ArticleDOI
Fast solvers and efficient implementations for distance metric learning
TL;DR: A highly efficient solver for the particular instance of semidefinite programming that arises in LMNN classification is described; this solver can handle problems with billions of large margin constraints in a few hours.
Proceedings Article
Large Margin Multi-Task Metric Learning
TL;DR: This paper proposes an alternative formulation for multi-task learning by extending the recently published large margin nearest neighbor (1mnn) algorithm to the MTL paradigm and shows that it consistently outperforms single-task kNN under several metrics and state-of-the-art MTL classifiers.
Spectral Methods for Dimensionality Reduction.
TL;DR: This chapter provides an overview of unsupervised learning algorithms that can be viewed as spectral methods for linear and nonlinear dimensionality reduction and manifold learning.
Proceedings ArticleDOI
Deep Feature Interpolation for Image Content Changes
Paul Upchurch,Jacob R. Gardner,Geoff Pleiss,Robert Pless,Noah Snavely,Kavita Bala,Kilian Q. Weinberger +6 more
TL;DR: Deep Feature Interpolation (DFI) as mentioned in this paper is a data-driven baseline for automatic high-resolution image transformation, which relies only on simple linear interpolation of deep convolutional features from pre-trained convnets.
Proceedings ArticleDOI
Unsupervised learning of image manifolds by semidefinite programming
TL;DR: The proposed algorithm can be used to analyze high dimensional data that lies on or near a low dimensional manifold, and overcomes certain limitations of previous work in manifold learning, such as Isomap and locally linear embedding.