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Kilian Q. Weinberger

Cornell University

118 papers hereh-index 79103.8k citations226 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author55
  • last author56

Across the 112 of 118 papers where every author was matched, so the position is known.

fields
  • cs.LG43
  • cs.CV34
  • cs.CL16
  • stat.ML6
  • cs.AI4
  • eess.IV3
affiliations
  • Cornell University
Homepage
same name
  • Kilian Q. Weinberger — 14 papers, h 3
  • Kilian Q. Weinberger — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20092025
most citedBERTScore: Evaluating Text Generation with BERT

2.4k citations · 12.3k across the 115 of their papers we have counts for

collaborators
Showing 2015 · cs.LGShow all

4 papers · 2 filters

cs.LG2015★ 61 cited

Deep Manifold Traversal: Changing Labels with Convolutional Features

Jacob R. Gardner, Paul Upchurch, Matt J. Kusner +4

Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in or…

cs.LG2015★ 32 cited

Compressing Convolutional Neural Networks

Wenlin Chen, James T. Wilson, Stephen Tyree +2

Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities o…

cs.LG2015★ 563 cited

Compressing Neural Networks with the Hashing Trick

Wenlin Chen, James T. Wilson, Stephen Tyree +2

As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-in…

cs.LG2015★ 7 cited

Compressed Support Vector Machines

Zhixiang Xu, Jacob R. Gardner, Stephen Tyree +1

Support vector machines (SVM) can classify data sets along highly non-linear decision boundaries because of the kernel-trick. This expressiveness comes at a price: During test-time…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.