Recurrent Neural Network
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Backpropagation and Deep Learning in the Brain
Backpropagation and Deep Learning in the Brain Simons Institute -- Computational Theories of the Brain 2018 Timothy Lillicrap DeepMind, UCL With: Sergey Bartunov, Adam Santoro, Jordan Guerguiev, Blake Richards, Luke Marris, Daniel Cownden, Colin Akerman, Douglas Tweed, Geoffrey Hinton The “credit assignment” problem The solution in artificial networks: backprop Credit assignment by backprop works well in practice and shows up in virtually all of the state-of-the-art supervised, unsupervised, and reinforcement learning algorithms. Why Isn’t Backprop “Biologically Plausible”? Why Isn’t Backprop “Biologically Plausible”? Neuroscience Evidence for Backprop in the Brain? A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: How to convince a neuroscientist that the cortex is learning via [something like] backprop - To convince a machine learning researcher, an appeal to variance in gradient estimates might be enough. - But this is rarely enough to convince a neuroscientist. - So what lines of argument help? How to convince a neuroscientist that the cortex is learning via [something like] backprop - What do I mean by “something like backprop”?: - That learning is achieved across multiple layers by sending information from neurons closer to the output back to “earlier” layers to help compute their synaptic updates. How to convince a neuroscientist that the cortex is learning via [something like] backprop 1. Feedback connections in cortex are ubiquitous and modify the -
Implementing Machine Learning & Neural Network Chip
Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP Implementing Machine Learning & Neural Network Chip Architectures USING NETWORK-ON-CHIP INTERCONNECT IP TY GARIBAY Chief Technology Officer [email protected] Title: Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP Primary author: Ty Garibay, Chief Technology Officer, ArterisIP, [email protected] Secondary author: Kurt Shuler, Vice President, Marketing, ArterisIP, [email protected] Abstract: A short tutorial and overview of machine learning and neural network chip architectures, with emphasis on how network-on-chip interconnect IP implements these architectures. Time to read: 10 minutes Time to present: 30 minutes Copyright © 2017 Arteris www.arteris.com 1 Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP What is Machine Learning? “ Field of study that gives computers the ability to learn without being explicitly programmed.” Arthur Samuel, IBM, 1959 • Machine learning is a subset of Artificial Intelligence Copyright © 2017 Arteris 2 • Machine learning is a subset of artificial intelligence, and explicitly relies upon experiential learning rather than programming to make decisions. • The advantage to machine learning for tasks like automated driving or speech translation is that complex tasks like these are nearly impossible to explicitly program using rule-based if…then…else statements because of the large solution space. • However, the “answers” given by a machine learning algorithm have a probability associated with them and can be non-deterministic (meaning you can get different “answers” given the same inputs during different runs) • Neural networks have become the most common way to implement machine learning. -
Chombining Recurrent Neural Networks and Adversarial Training
Combining Recurrent Neural Networks and Adversarial Training for Human Motion Modelling, Synthesis and Control † ‡ Zhiyong Wang∗ Jinxiang Chai Shihong Xia Institute of Computing Texas A&M University Institute of Computing Technology CAS Technology CAS University of Chinese Academy of Sciences ABSTRACT motions from the generator using recurrent neural networks (RNNs) This paper introduces a new generative deep learning network for and refines the generated motion using an adversarial neural network human motion synthesis and control. Our key idea is to combine re- which we call the “refiner network”. Fig 2 gives an overview of current neural networks (RNNs) and adversarial training for human our method: a motion sequence XRNN is generated with the gener- motion modeling. We first describe an efficient method for training ator G and is refined using the refiner network R. To add realism, a RNNs model from prerecorded motion data. We implement recur- we train our refiner network using an adversarial loss, similar to rent neural networks with long short-term memory (LSTM) cells Generative Adversarial Networks (GANs) [9] such that the refined because they are capable of handling nonlinear dynamics and long motion sequences Xre fine are indistinguishable from real motion cap- term temporal dependencies present in human motions. Next, we ture sequences Xreal using a discriminative network D. In addition, train a refiner network using an adversarial loss, similar to Gener- we embed contact information into the generative model to further ative Adversarial Networks (GANs), such that the refined motion improve the quality of the generated motions. sequences are indistinguishable from real motion capture data using We construct the generator G based on recurrent neural network- a discriminative network. -
Training Autoencoders by Alternating Minimization
Under review as a conference paper at ICLR 2018 TRAINING AUTOENCODERS BY ALTERNATING MINI- MIZATION Anonymous authors Paper under double-blind review ABSTRACT We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle. DANTE provides a distinct perspective in lieu of traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convex optimization techniques to cast autoencoder training as a bi-quasi-convex optimiza- tion problem. We show that for autoencoder configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations very effectively. DANTE effortlessly extends to networks with multiple hidden layers and varying network configurations. In experiments on standard datasets, autoencoders trained using the proposed method were found to be very promising and competitive to traditional backpropagation techniques, both in terms of quality of solution, as well as training speed. 1 INTRODUCTION For much of the recent march of deep learning, gradient-based backpropagation methods, e.g. Stochastic Gradient Descent (SGD) and its variants, have been the mainstay of practitioners. The use of these methods, especially on vast amounts of data, has led to unprecedented progress in several areas of artificial intelligence. On one hand, the intense focus on these techniques has led to an intimate understanding of hardware requirements and code optimizations needed to execute these routines on large datasets in a scalable manner. Today, myriad off-the-shelf and highly optimized packages exist that can churn reasonably large datasets on GPU architectures with relatively mild human involvement and little bootstrap effort. -
Predrnn: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal Lstms
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang Mingsheng Long∗ School of Software School of Software Tsinghua University Tsinghua University [email protected] [email protected] Jianmin Wang Zhifeng Gao Philip S. Yu School of Software School of Software School of Software Tsinghua University Tsinghua University Tsinghua University [email protected] [email protected] [email protected] Abstract The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal vari- ations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial ap- pearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures. 1 Introduction -
Learning to Learn by Gradient Descent by Gradient Descent
Learning to learn by gradient descent by gradient descent Marcin Andrychowicz1, Misha Denil1, Sergio Gómez Colmenarejo1, Matthew W. Hoffman1, David Pfau1, Tom Schaul1, Brendan Shillingford1,2, Nando de Freitas1,2,3 1Google DeepMind 2University of Oxford 3Canadian Institute for Advanced Research [email protected] {mdenil,sergomez,mwhoffman,pfau,schaul}@google.com [email protected], [email protected] Abstract The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure. We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art. 1 Introduction Frequently, tasks in machine learning can be expressed as the problem of optimizing an objective function f(✓) defined over some domain ✓ ⇥. The goal in this case is to find the minimizer 2 ✓⇤ = arg min✓ ⇥ f(✓). While any method capable of minimizing this objective function can be applied, the standard2 approach for differentiable functions is some form of gradient descent, resulting in a sequence of updates ✓ = ✓ ↵ f(✓ ) . t+1 t − tr t The performance of vanilla gradient descent, however, is hampered by the fact that it only makes use of gradients and ignores second-order information. -
Recurrent Neural Network for Text Classification with Multi-Task
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) Recurrent Neural Network for Text Classification with Multi-Task Learning Pengfei Liu Xipeng Qiu⇤ Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing, Fudan University School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, China pfliu14,xpqiu,xjhuang @fudan.edu.cn { } Abstract are based on unsupervised objectives such as word predic- tion for training [Collobert et al., 2011; Turian et al., 2010; Neural network based methods have obtained great Mikolov et al., 2013]. This unsupervised pre-training is effec- progress on a variety of natural language process- tive to improve the final performance, but it does not directly ing tasks. However, in most previous works, the optimize the desired task. models are learned based on single-task super- vised objectives, which often suffer from insuffi- Multi-task learning utilizes the correlation between related cient training data. In this paper, we use the multi- tasks to improve classification by learning tasks in parallel. [ task learning framework to jointly learn across mul- Motivated by the success of multi-task learning Caruana, ] tiple related tasks. Based on recurrent neural net- 1997 , there are several neural network based NLP models [ ] work, we propose three different mechanisms of Collobert and Weston, 2008; Liu et al., 2015b utilize multi- sharing information to model text with task-specific task learning to jointly learn several tasks with the aim of and shared layers. The entire network is trained mutual benefit. The basic multi-task architectures of these jointly on all these tasks. Experiments on four models are to share some lower layers to determine common benchmark text classification tasks show that our features. -
Training Neural Networks Without Gradients: a Scalable ADMM Approach
Training Neural Networks Without Gradients: A Scalable ADMM Approach Gavin Taylor1 [email protected] Ryan Burmeister1 Zheng Xu2 [email protected] Bharat Singh2 [email protected] Ankit Patel3 [email protected] Tom Goldstein2 [email protected] 1United States Naval Academy, Annapolis, MD USA 2University of Maryland, College Park, MD USA 3Rice University, Houston, TX USA Abstract many parameters. Because big datasets provide results that With the growing importance of large network (often dramatically) outperform the prior state-of-the-art in models and enormous training datasets, GPUs many machine learning tasks, researchers are willing to have become increasingly necessary to train neu- purchase specialized hardware such as GPUs, and commit ral networks. This is largely because conven- large amounts of time to training models and tuning hyper- tional optimization algorithms rely on stochastic parameters. gradient methods that don’t scale well to large Gradient-based training methods have several properties numbers of cores in a cluster setting. Further- that contribute to this need for specialized hardware. First, more, the convergence of all gradient methods, while large amounts of data can be shared amongst many including batch methods, suffers from common cores, existing optimization methods suffer when paral- problems like saturation effects, poor condition- lelized. Second, training neural nets requires optimizing ing, and saddle points. This paper explores an highly non-convex objectives that exhibit saddle points, unconventional training method that uses alter- poor conditioning, and vanishing gradients, all of which nating direction methods and Bregman iteration slow down gradient-based methods such as stochastic gra- to train networks without gradient descent steps. -
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments Boris Ginsburg 1 Patrice Castonguay 1 Oleksii Hrinchuk 1 Oleksii Kuchaiev 1 Ryan Leary 1 Vitaly Lavrukhin 1 Jason Li 1 Huyen Nguyen 1 Yang Zhang 1 Jonathan M. Cohen 1 Abstract We started with Adam, and then (1) replaced the element- wise second moment with the layer-wise moment, (2) com- We propose NovoGrad, an adaptive stochastic puted the first moment using gradients normalized by layer- gradient descent method with layer-wise gradient wise second moment, (3) decoupled weight decay (WD) normalization and decoupled weight decay. In from normalized gradients (similar to AdamW). our experiments on neural networks for image classification, speech recognition, machine trans- The resulting algorithm, NovoGrad, combines SGD’s and lation, and language modeling, it performs on par Adam’s strengths. We applied NovoGrad to a variety of or better than well-tuned SGD with momentum, large scale problems — image classification, neural machine Adam, and AdamW. Additionally, NovoGrad (1) translation, language modeling, and speech recognition — is robust to the choice of learning rate and weight and found that in all cases, it performs as well or better than initialization, (2) works well in a large batch set- Adam/AdamW and SGD with momentum. ting, and (3) has half the memory footprint of Adam. 2. Related Work NovoGrad belongs to the family of Stochastic Normalized 1. Introduction Gradient Descent (SNGD) optimizers (Nesterov, 1984; Hazan et al., 2015). SNGD uses only the direction of the The most popular algorithms for training Neural Networks stochastic gradient gt to update the weights wt: (NNs) are Stochastic Gradient Descent (SGD) with mo- gt mentum (Polyak, 1964; Sutskever et al., 2013) and Adam wt+1 = wt − λt · (Kingma & Ba, 2015). -
Face Recognition: a Convolutional Neural-Network Approach
98 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 1, JANUARY 1997 Face Recognition: A Convolutional Neural-Network Approach Steve Lawrence, Member, IEEE, C. Lee Giles, Senior Member, IEEE, Ah Chung Tsoi, Senior Member, IEEE, and Andrew D. Back, Member, IEEE Abstract— Faces represent complex multidimensional mean- include fingerprints [4], speech [7], signature dynamics [36], ingful visual stimuli and developing a computational model for and face recognition [8]. Sales of identity verification products face recognition is difficult. We present a hybrid neural-network exceed $100 million [29]. Face recognition has the benefit of solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map being a passive, nonintrusive system for verifying personal (SOM) neural network, and a convolutional neural network. identity. The techniques used in the best face recognition The SOM provides a quantization of the image samples into a systems may depend on the application of the system. We topological space where inputs that are nearby in the original can identify at least two broad categories of face recognition space are also nearby in the output space, thereby providing systems. dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides for 1) We want to find a person within a large database of partial invariance to translation, rotation, scale, and deformation. faces (e.g., in a police database). These systems typically The convolutional network extracts successively larger features return a list of the most likely people in the database in a hierarchical set of layers. We present results using the [34]. -
CSE 152: Computer Vision Manmohan Chandraker
CSE 152: Computer Vision Manmohan Chandraker Lecture 15: Optimization in CNNs Recap Engineered against learned features Label Convolutional filters are trained in a Dense supervised manner by back-propagating classification error Dense Dense Convolution + pool Label Convolution + pool Classifier Convolution + pool Pooling Convolution + pool Feature extraction Convolution + pool Image Image Jia-Bin Huang and Derek Hoiem, UIUC Two-layer perceptron network Slide credit: Pieter Abeel and Dan Klein Neural networks Non-linearity Activation functions Multi-layer neural network From fully connected to convolutional networks next layer image Convolutional layer Slide: Lazebnik Spatial filtering is convolution Convolutional Neural Networks [Slides credit: Efstratios Gavves] 2D spatial filters Filters over the whole image Weight sharing Insight: Images have similar features at various spatial locations! Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. Fergus, Y. LeCun Slide: Lazebnik Convolution as a feature extractor Key operations in a CNN Feature maps Rectified Linear Unit (ReLU) Spatial pooling Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Key operations in a CNN Feature maps Spatial pooling Max Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Pooling operations • Aggregate multiple values into a single value • Invariance to small transformations • Keep only most important information for next layer • Reduces the size of the next layer • Fewer parameters, faster computations • Observe larger receptive field in next layer • Hierarchically extract more abstract features Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. -
CNN Architectures
Lecture 9: CNN Architectures Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 1 May 2, 2017 Administrative A2 due Thu May 4 Midterm: In-class Tue May 9. Covers material through Thu May 4 lecture. Poster session: Tue June 6, 12-3pm Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 2 May 2, 2017 Last time: Deep learning frameworks Paddle (Baidu) Caffe Caffe2 (UC Berkeley) (Facebook) CNTK (Microsoft) Torch PyTorch (NYU / Facebook) (Facebook) MXNet (Amazon) Developed by U Washington, CMU, MIT, Hong Kong U, etc but main framework of Theano TensorFlow choice at AWS (U Montreal) (Google) And others... Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 3 May 2, 2017 Last time: Deep learning frameworks (1) Easily build big computational graphs (2) Easily compute gradients in computational graphs (3) Run it all efficiently on GPU (wrap cuDNN, cuBLAS, etc) Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 4 May 2, 2017 Last time: Deep learning frameworks Modularized layers that define forward and backward pass Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 5 May 2, 2017 Last time: Deep learning frameworks Define model architecture as a sequence of layers Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 6 May 2, 2017 Today: CNN Architectures Case Studies - AlexNet - VGG - GoogLeNet - ResNet Also.... - NiN (Network in Network) - DenseNet - Wide ResNet - FractalNet - ResNeXT - SqueezeNet - Stochastic Depth Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 7 May 2, 2017 Review: LeNet-5 [LeCun et al., 1998] Conv filters were 5x5, applied at stride 1 Subsampling (Pooling) layers were 2x2 applied at stride 2 i.e.