Kilian Q. Weinberger

Distance Metric Learning for Large Margin Nearest Neighbor Classification

Kilian Q. Weinberger, Lawrence K. Saul
Journal of Machine Learning Research
2009

Densely Connected Convolutional Networks

Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
2017 IEEE Conference on Computer Vision and…
2017

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those… (More)

Marginalized Denoising Autoencoders for Domain Adaptation

Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha
ICML
2012

Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of… (More)

From Word Embeddings To Document Distances

Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger
ICML
2015

References 1. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. Distributed representations of words and phrases and their compositionality. In NIPS, pp. 3111– 3119, 2013 2.… (More)

Unsupervised Learning of Image Manifolds by Semidefinite Programming

Deep Networks with Stochastic Depth

Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Q. Weinberger
ECCV
2016

Very deep convolutional networks with hundreds or more layers have lead to significant reductions in error on competitive benchmarks like the ImageNet or COCO tasks. Although the unmatched… (More)

Fast solvers and efficient implementations for distance metric learning

In this paper we study how to improve nearest neighbor classification by learning a Mahalanobis distance metric. We build on a recently proposed framework for distance metric learning known as large… (More)

Learning a kernel matrix for nonlinear dimensionality reduction

Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
ICML
2004

We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear feature… (More)

Fast Image Tagging

Minmin Chen, Alice X. Zheng, Kilian Q. Weinberger
ICML
2013

Automatic image annotation is a difficult and highly relevant machine learning task. Recent advances have significantly improved the state-of-the-art in retrieval accuracy with algorithms based on… (More)

Feature Hashing for Large Scale Multitask Learning

Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alexander J. Smola
ICML
2009

Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature… (More)

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