Speedy Q-Learning
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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. -
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 -
Q-Learning in Continuous State and Action Spaces
-Learning in Continuous Q State and Action Spaces Chris Gaskett, David Wettergreen, and Alexander Zelinsky Robotic Systems Laboratory Department of Systems Engineering Research School of Information Sciences and Engineering The Australian National University Canberra, ACT 0200 Australia [cg dsw alex]@syseng.anu.edu.au j j Abstract. -learning can be used to learn a control policy that max- imises a scalarQ reward through interaction with the environment. - learning is commonly applied to problems with discrete states and ac-Q tions. We describe a method suitable for control tasks which require con- tinuous actions, in response to continuous states. The system consists of a neural network coupled with a novel interpolator. Simulation results are presented for a non-holonomic control task. Advantage Learning, a variation of -learning, is shown enhance learning speed and reliability for this task.Q 1 Introduction Reinforcement learning systems learn by trial-and-error which actions are most valuable in which situations (states) [1]. Feedback is provided in the form of a scalar reward signal which may be delayed. The reward signal is defined in relation to the task to be achieved; reward is given when the system is successfully achieving the task. The value is updated incrementally with experience and is defined as a discounted sum of expected future reward. The learning systems choice of actions in response to states is called its policy. Reinforcement learning lies between the extremes of supervised learning, where the policy is taught by an expert, and unsupervised learning, where no feedback is given and the task is to find structure in data. -
Introduction to Machine Learning
Introduction to Machine Learning Perceptron Barnabás Póczos Contents History of Artificial Neural Networks Definitions: Perceptron, Multi-Layer Perceptron Perceptron algorithm 2 Short History of Artificial Neural Networks 3 Short History Progression (1943-1960) • First mathematical model of neurons ▪ Pitts & McCulloch (1943) • Beginning of artificial neural networks • Perceptron, Rosenblatt (1958) ▪ A single neuron for classification ▪ Perceptron learning rule ▪ Perceptron convergence theorem Degression (1960-1980) • Perceptron can’t even learn the XOR function • We don’t know how to train MLP • 1963 Backpropagation… but not much attention… Bryson, A.E.; W.F. Denham; S.E. Dreyfus. Optimal programming problems with inequality constraints. I: Necessary conditions for extremal solutions. AIAA J. 1, 11 (1963) 2544-2550 4 Short History Progression (1980-) • 1986 Backpropagation reinvented: ▪ Rumelhart, Hinton, Williams: Learning representations by back-propagating errors. Nature, 323, 533—536, 1986 • Successful applications: ▪ Character recognition, autonomous cars,… • Open questions: Overfitting? Network structure? Neuron number? Layer number? Bad local minimum points? When to stop training? • Hopfield nets (1982), Boltzmann machines,… 5 Short History Degression (1993-) • SVM: Vapnik and his co-workers developed the Support Vector Machine (1993). It is a shallow architecture. • SVM and Graphical models almost kill the ANN research. • Training deeper networks consistently yields poor results. • Exception: deep convolutional neural networks, Yann LeCun 1998. (discriminative model) 6 Short History Progression (2006-) Deep Belief Networks (DBN) • Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554. • Generative graphical model • Based on restrictive Boltzmann machines • Can be trained efficiently Deep Autoencoder based networks Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. -
Lecture 11 Recurrent Neural Networks I CMSC 35246: Deep Learning
Lecture 11 Recurrent Neural Networks I CMSC 35246: Deep Learning Shubhendu Trivedi & Risi Kondor University of Chicago May 01, 2017 Lecture 11 Recurrent Neural Networks I CMSC 35246 Introduction Sequence Learning with Neural Networks Lecture 11 Recurrent Neural Networks I CMSC 35246 Some Sequence Tasks Figure credit: Andrej Karpathy Lecture 11 Recurrent Neural Networks I CMSC 35246 MLPs only accept an input of fixed dimensionality and map it to an output of fixed dimensionality Great e.g.: Inputs - Images, Output - Categories Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently. How is this problematic? Need to re-learn the rules of language from scratch each time Another example: Classify events after a fixed number of frames in a movie Need to resuse knowledge about the previous events to help in classifying the current. Problems with MLPs for Sequence Tasks The "API" is too limited. Lecture 11 Recurrent Neural Networks I CMSC 35246 Great e.g.: Inputs - Images, Output - Categories Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently. How is this problematic? Need to re-learn the rules of language from scratch each time Another example: Classify events after a fixed number of frames in a movie Need to resuse knowledge about the previous events to help in classifying the current. Problems with MLPs for Sequence Tasks The "API" is too limited. MLPs only accept an input of fixed dimensionality and map it to an output of fixed dimensionality Lecture 11 Recurrent Neural Networks I CMSC 35246 Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently. -
Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting
water Article Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting Halit Apaydin 1 , Hajar Feizi 2 , Mohammad Taghi Sattari 1,2,* , Muslume Sevba Colak 1 , Shahaboddin Shamshirband 3,4,* and Kwok-Wing Chau 5 1 Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey; [email protected] (H.A.); [email protected] (M.S.C.) 2 Department of Water Engineering, Agriculture Faculty, University of Tabriz, Tabriz 51666, Iran; [email protected] 3 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam 4 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam 5 Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China; [email protected] * Correspondence: [email protected] or [email protected] (M.T.S.); [email protected] (S.S.) Received: 1 April 2020; Accepted: 21 May 2020; Published: 24 May 2020 Abstract: Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. -
Deep Learning in Bioinformatics
Deep Learning in Bioinformatics Seonwoo Min1, Byunghan Lee1, and Sungroh Yoon1,2* 1Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea 2Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea Abstract In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. *Corresponding author. Mailing address: 301-908, Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea. E-mail: [email protected]. Phone: +82-2-880-1401. Keywords Deep learning, neural network, machine learning, bioinformatics, omics, biomedical imaging, biomedical signal processing Key Points As a great deal of biomedical data have been accumulated, various machine algorithms are now being widely applied in bioinformatics to extract knowledge from big data. -
Training Deep Networks Without Learning Rates Through Coin Betting
Training Deep Networks without Learning Rates Through Coin Betting Francesco Orabona∗ Tatiana Tommasi∗ Department of Computer Science Department of Computer, Control, and Stony Brook University Management Engineering Stony Brook, NY Sapienza, Rome University, Italy [email protected] [email protected] Abstract Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. In this paper, we propose a new stochastic gradient descent procedure for deep networks that does not require any learning rate setting. Contrary to previous methods, we do not adapt the learning rates nor we make use of the assumed curvature of the objective function. Instead, we reduce the optimization process to a game of betting on a coin and propose a learning-rate-free optimal algorithm for this scenario. Theoretical convergence is proven for convex and quasi-convex functions and empirical evidence shows the advantage of our algorithm over popular stochastic gradient algorithms. 1 Introduction In the last years deep learning has demonstrated a great success in a large number of fields and has attracted the attention of various research communities with the consequent development of multiple coding frameworks (e.g., Caffe [Jia et al., 2014], TensorFlow [Abadi et al., 2015]), the diffusion of blogs, online tutorials, books, and dedicated courses. Besides reaching out scientists with different backgrounds, the need of all these supportive tools originates also from the nature of deep learning: it is a methodology that involves many structural details as well as several hyperparameters whose importance has been growing with the recent trend of designing deeper and multi-branches networks. -
Machine Learning and Data Mining Machine Learning Algorithms Enable Discovery of Important “Regularities” in Large Data Sets
General Domains Machine Learning and Data Mining Machine learning algorithms enable discovery of important “regularities” in large data sets. Over the past Tom M. Mitchell decade, many organizations have begun to routinely cap- ture huge volumes of historical data describing their operations, products, and customers. At the same time, scientists and engineers in many fields have been captur- ing increasingly complex experimental data sets, such as gigabytes of functional mag- netic resonance imaging (MRI) data describing brain activity in humans. The field of data mining addresses the question of how best to use this historical data to discover general regularities and improve PHILIP NORTHOVER/PNORTHOV.FUTURE.EASYSPACE.COM/INDEX.HTML the process of making decisions. COMMUNICATIONS OF THE ACM November 1999/Vol. 42, No. 11 31 he increasing interest in Figure 1. Data mining application. A historical set of 9,714 medical data mining, or the use of records describes pregnant women over time. The top portion is a historical data to discover typical patient record (“?” indicates the feature value is unknown). regularities and improve The task for the algorithm is to discover rules that predict which T future patients will be at high risk of requiring an emergency future decisions, follows from the confluence of several recent trends: C-section delivery. The bottom portion shows one of many rules the falling cost of large data storage discovered from this data. Whereas 7% of all pregnant women in devices and the increasing ease of the data set received emergency C-sections, the rule identifies a collecting data over networks; the subclass at 60% at risk for needing C-sections. -
Sparse Cooperative Q-Learning
Sparse Cooperative Q-learning Jelle R. Kok [email protected] Nikos Vlassis [email protected] Informatics Institute, Faculty of Science, University of Amsterdam, The Netherlands Abstract in uncertain environments. In principle, it is pos- sible to treat a multiagent system as a `big' single Learning in multiagent systems suffers from agent and learn the optimal joint policy using stan- the fact that both the state and the action dard single-agent reinforcement learning techniques. space scale exponentially with the number of However, both the state and action space scale ex- agents. In this paper we are interested in ponentially with the number of agents, rendering this using Q-learning to learn the coordinated ac- approach infeasible for most problems. Alternatively, tions of a group of cooperative agents, us- we can let each agent learn its policy independently ing a sparse representation of the joint state- of the other agents, but then the transition model de- action space of the agents. We first examine pends on the policy of the other learning agents, which a compact representation in which the agents may result in oscillatory behavior. need to explicitly coordinate their actions only in a predefined set of states. Next, we On the other hand, in many problems the agents only use a coordination-graph approach in which need to coordinate their actions in few states (e.g., two we represent the Q-values by value rules that cleaning robots that want to clean the same room), specify the coordination dependencies of the while in the rest of the states the agents can act in- agents at particular states.