Search Results for author: Kilian Weinberger

Found 9 papers, 5 papers with code

Understanding Decoupled and Early Weight Decay

no code implementations27 Dec 2020 Johan Bjorck, Kilian Weinberger, Carla Gomes

We also show how the growth of network weights is heavily influenced by the dataset and its generalization properties.

Star-Convexity in Non-Negative Matrix Factorization

no code implementations25 Sep 2019 Johan Bjorck, Carla Gomes, Kilian Weinberger

Non-negative matrix factorization (NMF) is a highly celebrated algorithm for matrix decomposition that guarantees strictly non-negative factors.

Deep Feature Interpolation for Image Content Changes

2 code implementations CVPR 2017 Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Weinberger

We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation.

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

2 code implementations TACL 2018 Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, Kilian Weinberger

To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exists.

Classification Cross-Lingual Document Classification +5

Deep Networks with Stochastic Depth

17 code implementations30 Mar 2016 Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger

With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4. 91% on CIFAR-10).

Image Classification Test

Marginalizing Corrupted Features

no code implementations27 Feb 2014 Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Weinberger

In this paper, we propose a third, alternative approach to combat overfitting: we extend the training set with infinitely many artificial training examples that are obtained by corrupting the original training data.

Bayesian Inference Test

Feature Hashing for Large Scale Multitask Learning

no code implementations12 Feb 2009 Kilian Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex Smola

Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation.

Dimensionality Reduction

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