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Found 9 papers, 5 papers with code
Understanding Decoupled and Early Weight Decay
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
Non-negative matrix factorization (NMF) is a highly celebrated algorithm for matrix decomposition that guarantees strictly non-negative factors.
An empirical study on evaluation metrics of generative adversarial networks
Evaluating generative adversarial networks (GANs) is inherently challenging.
Fast Reading Comprehension with ConvNets
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets.
Deep Feature Interpolation for Image Content Changes
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
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.
Deep Networks with Stochastic Depth
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).
Ranked #20 on
Image Classification
on SVHN
Marginalizing Corrupted Features
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.
Feature Hashing for Large Scale Multitask Learning
Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation.
