Search Results
Adversarial Training Methods for Semi-Supervised Text Classification
We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself.
Ranked #13 on
Sentiment Analysis
on IMDb
General Classification
Semi Supervised Text Classification
+3
MobileNetV2: Inverted Residuals and Linear Bottlenecks
tensorflow/models
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CVPR 2018
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
Ranked #6 on
Retinal OCT Disease Classification
on Srinivasan2014
AutoAugment: Learning Augmentation Policies from Data
In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
Ranked #7 on
Fine-Grained Image Classification
on Caltech-101
Identity Mappings in Deep Residual Networks
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors.
Ranked #15 on
Image Classification
on Kuzushiji-MNIST
Cognitive Mapping and Planning for Visual Navigation
tensorflow/models
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CVPR 2017
The accumulated belief of the world enables the agent to track visited regions of the environment.
Ensemble Adversarial Training: Attacks and Defenses
tensorflow/models
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ICLR 2018
We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss.
Very Deep Convolutional Networks for Large-Scale Image Recognition
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
Ranked #4 on
Activity Recognition In Videos
on DogCentric
Weight Uncertainty in Neural Networks
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.
Swivel: Improving Embeddings by Noticing What's Missing
We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix.
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
tensorflow/models
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ICLR 2018
At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
Ranked #1 on
Multi-Armed Bandits
on Mushroom
