Convolutional LSTM Network: A Machine Learning Approach ...
Convolutional LSTM Network: A Machine Learning ... According to the philosophy underlying the deep learning approach, if we have a reasonable end-to-end model and sufficient data for training it, we are close to solving the ... The pioneering LSTM encoder-decoder framework proposed in [23] provides a
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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 ...
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Character-level Convolutional Networks for Text Classification
papers.nips.ccApplying convolutional networks to text classification or natural language processing at large was explored in literature. It has been shown that ConvNets can be directly applied to distributed [6] [16] or discrete [13] embedding of words, without any knowledge on the syntactic or semantic structures of a language.
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
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
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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
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 ...
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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
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Image Restoration Using Very Deep Convolutional Encoder ...
proceedings.neurips.ccThe proposed framework mainly contains a chain of convolutional layers and symmetric decon-volutional layers, as shown in Figure 1. We term our method “RED-Net”—very deep Residual Encoder-Decoder Networks. 2.1 Architecture The framework is fully convolutional and deconvolutional. Rectification layers are added after each
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Graph Convolutional Matrix Completion
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Abstract - arXiv
arxiv.orgConvolutional LSTM Network: A Machine Learning ... According to the philosophy underlying the deep learning approach, if we have a reasonable end-to-end model and sufficient data for training it, we are close to solving the ... The pioneering LSTM encoder-decoder framework proposed in [23] provides a
Stacked Convolutional Auto-Encoders for Hierarchical ...
people.idsia.chDeep architectures can be trained in an unsupervised layer-wise fashion, and later fine-tuned by back-propagationto be-come classifiers [9]. Unsupervised initializations tend to avoid local minima and increase the network’s performance stability [6]. Most methods are based on the encoder-decoder paradigm, e.g., [20]. The in-
Image Colorization with Deep Convolutional Neural …
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Convolutional Neural Networks
proceedings.mlr.pressConvolutional Neural Networks Lingxiao Yang 1 2 3Ru-Yuan Zhang4 5 Lida Li6 Xiaohua Xie Abstract ... s are based on a hand-crafted encoder-decoder structure. Compared to that study, our work provides an alternative ... Network Architectures. In 2012, a modern deep ConvNet, AlexNet (Krizhevsky et al.,2012), was released for large-scale image ...
Long-Term Recurrent Convolutional Networks for Visual ...
openaccess.thecvf.comdeep”, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image de-scription and retrieval problems, and video narration ...
Deep Convolutional Dictionary Learning for Image Denoising
openaccess.thecvf.comDeep Convolutional Dictionary Learning for Image Denoising Hongyi Zhenga,b,* Hongwei Yonga,b,* Lei Zhanga,b,† aThe Hong Kong Polytechnic University bDAMO Academy, Alibaba Group {cshzheng,cshyong,cslzhang}@comp.polyu.edu.hk Abstract Inspired by the great success of deep neural net-
arXiv:1706.02216v4 [cs.SI] 10 Sep 2018
arxiv.orgGraph convolutional networks. In recent years, several convolutional neural network architectures for learning over graphs have been proposed (e.g., [4, 9, 8, 17, 24]). The majority of these methods do not scale to large graphs or are designed for whole-graph classification (or both) [4, 9, 8, 24].