Character-level Convolutional Networks for Text Classification
interest. We also insert 2 dropout [10] modules in between the 3 fully-connected layers to regularize. They have dropout probability of 0.5. Table 1 lists the configurations for convolutional layers, and table 2 lists the configurations for fully-connected (linear) layers. Table 1: Convolutional layers used in our experiments.
Download Character-level Convolutional Networks for Text Classification
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Advertisement
Documents from same domain
On Discriminative vs. Generative Classifiers: A …
papers.nips.ccOn Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes Andrew Y. Ng Computer Science Division University of California, Berkeley
SAGA: A Fast Incremental Gradient Method With Support for ...
papers.nips.ccSAGA is preferred over SVRG both theoretically and in practice. For neural networks, where no theory is available for either method, the storage of gradients is generally more expensive than the
With, Methods, Support, Fast, Saga, Derating, Incremental, A fast incremental gradient method with support
Thinking Fast and Slow with Deep Learning and Tree Search
papers.nips.ccSystem 1 is a fast, unconscious and automatic mode of thought, also known as intuition or heuristic process. System 2, an evolutionarily recent process unique to humans, is a slow, conscious, explicit
With, Learning, Search, Tree, Thinking, Deep, Fast, Slow, Thinking fast and slow with deep learning and tree search
A Growing Neural Gas Network Learns Topologies
papers.nips.ccA Growing Neural Gas Network Learns Topologies 627 a) Delaunay triangulation b) induced Delaunay triangulation Figure 1: Two ways of defining closeness among a set of points.
Attention is All you Need - Neural Information Processing ...
papers.nips.ccAttention Is All You Need Ashish Vaswani Google Brain avaswani@google.com Noam Shazeer Google Brain noam@google.com Niki Parmar Google Research nikip@google.com
ImageNet Classification with Deep Convolutional Neural ...
papers.nips.ccChallenge, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images, and 150,000 testing images. ILSVRC-2010 is the only version ...
Challenges, Scale, Visual, Recognition, Ilsvrc, Scale visual recognition challenge
Generative Adversarial Nets - NIPS
papers.nips.ccGenerative adversarial networks has been sometimes confused with the related concept of “adversar-ial examples” [28]. Adversarial examples are examples found by using gradient-based optimization directly on the input to a classification network, in order to find examples that are similar to the data yet misclassified.
Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks, Adversar ial, Adversar
Time-series Generative Adversarial Networks
papers.nips.ccA good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time ...
Network, Adversarial, Generative, Generative adversarial networks
Hidden Technical Debt in Machine Learning Systems
papers.nips.ccaccount for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns. 1 Introduction As the machine learning (ML) community continues to accumulate years of experience with live
System, Design, Machine, Technical, Learning, Debt, Hidden, Hidden technical debt in machine learning systems
InfoGAN: Interpretable Representation Learning by ...
papers.nips.cc30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. ... a higher-order extension of the spike-and-slab restricted Boltzmann machine that can disentangle emotion from identity on the Toronto Face Dataset ... we want PG(cjx) to have a small entropy. In other words, the information in the latent code cshould not ...
Related documents
1 DeepLab: Semantic Image Segmentation with Deep ...
arxiv.orgA fully connected CRF is then applied to refine the segmentation result and better capture the object boundaries. segmentation-free approaches of [14], [52] directly apply DCNNs to the whole image in a fully convolutional fashion, transforming the last fully connected layers of the DCNN into convolutional layers. In order to deal with the ...
With, Image, Deep, Fully, Segmentation, Convolutional, Semantics, Deeplab, Semantic image segmentation with deep, Fully convolutional
Convolutional Neural Networks for Sentence Classification
emnlp2014.orgConvolutional layer with multiple filter widths and feature maps Max -over -time pooling Fully connected layer with dropout and softmax output Figure 1: Model architecture with two channels for an example sentence. necessary) is represented as x 1: n = x 1 x 2::: x n; (1) is the concatenation operator. In gen-
Network, Classification, Sentences, Fully, Neural, Convolutional, Convolutional neural networks for sentence classification
Iasonas Kokkinos arXiv:1412.7062v4 [cs.CV] 7 Jun 2016 ...
arxiv.orga first step to implement this, we convert the fully-connected layers of VGG-16 into convolutional ones and run the network in a convolutional fashion on the image at its original resolution. However this is not enough as it yields very sparsely computed detection scores (with a stride of 32 pixels). To
Convolutional Neural Networks for Facial Expression ...
cs231n.stanford.eduon Convolutional Neural Networks (CNN) for facial expression recognition. The input into our system is an image; then, we use CNN to predict the facial ex- ... and fully connected layers, as well as the existance of batch normalization, dropout and max-pooling lay-ers. Along with dropout and batch normalization tech-
Network, Expression, Fully, Neural, Convolutional, Facial, Convolutional neural networks for facial expression
ImageNet Classification with Deep Convolutional Neural …
www.nvidia.cnof five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make train-ing faster, we used non-saturating neurons and a very efficient GPU implemen-tation of the convolution operation. To reduce overfitting in the fully-connected
Spatial Pyramid Pooling in Deep Convolutional Networks …
tinman.cs.gsu.edu1 Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun Abstract—Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 224) input image.This require-
Network, Pyramid, Spatial, Convolutional, Convolutional networks, Pooling, Spatial pyramid pooling
ImageNet Classification with Deep Convolutional Neural …
vision.stanford.eduThe output from the last 4096 fully-connected layer : 4096 dimensional feature. Discussion " Depth is really important. removing a single convolutional layer degrades the performance. ... Two-Stream Convolutional Networks for Action Recognition in Videos. NIPS 2014.
Deformable Convolutional Networks
openaccess.thecvf.comDeformable Convolutional Networks ... It follows the “fully convolutional” spirit in [6], as illustrated in Figure 4. In the top branch, a conv layer generates the full spatial resolution offset fields. For each RoI (also for each class), PS RoI pooling is applied
Network, Fully, Convolutional, Convolutional networks, Fully convolutional