Recent Trends in Stochastic Gradient Descent for Machine Learning and Big Data
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Stochastic Optimization from Distributed, Streaming Data in Rate-Limited Networks Matthew Nokleby, Member, IEEE, and Waheed U
1 Stochastic Optimization from Distributed, Streaming Data in Rate-limited Networks Matthew Nokleby, Member, IEEE, and Waheed U. Bajwa, Senior Member, IEEE T Abstract—Motivated by machine learning applications in T training samples fξ(t) 2 Υgt=1 drawn independently from networks of sensors, internet-of-things (IoT) devices, and au- the distribution P (ξ), which is supported on a subset of Υ. tonomous agents, we propose techniques for distributed stochastic There are, in particular, two main categorizations of training convex learning from high-rate data streams. The setup involves a network of nodes—each one of which has a stream of data data that, in turn, determine the types of methods that can be arriving at a constant rate—that solve a stochastic convex used to find approximate solutions to the SO problem. These optimization problem by collaborating with each other over rate- are (i) batch training data and (ii) streaming training data. limited communication links. To this end, we present and analyze In the case of batch training data, where all T samples two algorithms—termed distributed stochastic approximation fξ(t)g are pre-stored and simultaneously available, a common mirror descent (D-SAMD) and accelerated distributed stochastic approximation mirror descent (AD-SAMD)—that are based on strategy is sample average approximation (SAA) (also referred two stochastic variants of mirror descent and in which nodes to as empirical risk minimization (ERM)), in which one collaborate via approximate averaging of the local, noisy subgra- minimizes the empirical average of the “risk” function φ(·; ·) dients using distributed consensus. Our main contributions are in lieu of the true expectation. -
Metaheuristics1
METAHEURISTICS1 Kenneth Sörensen University of Antwerp, Belgium Fred Glover University of Colorado and OptTek Systems, Inc., USA 1 Definition A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms (Sörensen and Glover, To appear). Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, and ant colony optimization, although many more exist. A problem-specific implementation of a heuristic optimization algorithm according to the guidelines expressed in a metaheuristic framework is also referred to as a metaheuristic. The term was coined by Glover (1986) and combines the Greek prefix meta- (metá, beyond in the sense of high-level) with heuristic (from the Greek heuriskein or euriskein, to search). Metaheuristic algorithms, i.e., optimization methods designed according to the strategies laid out in a metaheuristic framework, are — as the name suggests — always heuristic in nature. This fact distinguishes them from exact methods, that do come with a proof that the optimal solution will be found in a finite (although often prohibitively large) amount of time. Metaheuristics are therefore developed specifically to find a solution that is “good enough” in a computing time that is “small enough”. As a result, they are not subject to combinatorial explosion – the phenomenon where the computing time required to find the optimal solution of NP- hard problems increases as an exponential function of the problem size. Metaheuristics have been demonstrated by the scientific community to be a viable, and often superior, alternative to more traditional (exact) methods of mixed- integer optimization such as branch and bound and dynamic programming. -
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. -
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. -
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). -
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. -
Automated Source Code Generation and Auto-Completion Using Deep Learning: Comparing and Discussing Current Language Model-Related Approaches
Article Automated Source Code Generation and Auto-Completion Using Deep Learning: Comparing and Discussing Current Language Model-Related Approaches Juan Cruz-Benito 1,* , Sanjay Vishwakarma 2,†, Francisco Martin-Fernandez 1 and Ismael Faro 1 1 IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA; [email protected] (F.M.-F.); [email protected] (I.F.) 2 Electrical and Computer Engineering, Carnegie Mellon University, Mountain View, CA 94035, USA; [email protected] * Correspondence: [email protected] † Intern at IBM Quantum at the time of writing this paper. Abstract: In recent years, the use of deep learning in language models has gained much attention. Some research projects claim that they can generate text that can be interpreted as human writ- ing, enabling new possibilities in many application areas. Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages. For years, the machine learning community has been researching this software engi- neering area, pursuing goals like applying different approaches to auto-complete, generate, fix, or evaluate code programmed by humans. Considering the increasing popularity of the deep learning- enabled language models approach, we found a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code. This paper compares different neural network architectures like Average Stochastic Gradient Descent (ASGD) Weight-Dropped LSTMs (AWD-LSTMs), AWD-Quasi-Recurrent Neural Networks (QRNNs), and Citation: Cruz-Benito, J.; Transformer while using transfer learning and different forms of tokenization to see how they behave Vishwakarma, S.; Martin- Fernandez, F.; Faro, I. -
GEE: a Gradient-Based Explainable Variational Autoencoder for Network Anomaly Detection
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection Quoc Phong Nguyen Kar Wai Lim Dinil Mon Divakaran National University of Singapore National University of Singapore Trustwave [email protected] [email protected] [email protected] Kian Hsiang Low Mun Choon Chan National University of Singapore National University of Singapore [email protected] [email protected] Abstract—This paper looks into the problem of detecting number of factors, such as end-user behavior, customer busi- network anomalies by analyzing NetFlow records. While many nesses (e.g., banking, retail), applications, location, time of the previous works have used statistical models and machine learning day, and are expected to evolve with time. Such diversity and techniques in a supervised way, such solutions have the limitations that they require large amount of labeled data for training and dynamism limits the utility of rule-based detection systems. are unlikely to detect zero-day attacks. Existing anomaly detection Next, as capturing, storing and processing raw traffic from solutions also do not provide an easy way to explain or identify such high capacity networks is not practical, Internet routers attacks in the anomalous traffic. To address these limitations, today have the capability to extract and export meta data such we develop and present GEE, a framework for detecting and explaining anomalies in network traffic. GEE comprises of two as NetFlow records [3]. With NetFlow, the amount of infor- components: (i) Variational Autoencoder (VAE) — an unsuper- mation captured is brought down by orders of magnitude (in vised deep-learning technique for detecting anomalies, and (ii) a comparison to raw packet capture), not only because a NetFlow gradient-based fingerprinting technique for explaining anomalies. -
Optimization and Gradient Descent INFO-4604, Applied Machine Learning University of Colorado Boulder
Optimization and Gradient Descent INFO-4604, Applied Machine Learning University of Colorado Boulder September 11, 2018 Prof. Michael Paul Prediction Functions Remember: a prediction function is the function that predicts what the output should be, given the input. Prediction Functions Linear regression: f(x) = wTx + b Linear classification (perceptron): f(x) = 1, wTx + b ≥ 0 -1, wTx + b < 0 Need to learn what w should be! Learning Parameters Goal is to learn to minimize error • Ideally: true error • Instead: training error The loss function gives the training error when using parameters w, denoted L(w). • Also called cost function • More general: objective function (in general objective could be to minimize or maximize; with loss/cost functions, we want to minimize) Learning Parameters Goal is to minimize loss function. How do we minimize a function? Let’s review some math. Rate of Change The slope of a line is also called the rate of change of the line. y = ½x + 1 “rise” “run” Rate of Change For nonlinear functions, the “rise over run” formula gives you the average rate of change between two points Average slope from x=-1 to x=0 is: 2 f(x) = x -1 Rate of Change There is also a concept of rate of change at individual points (rather than two points) Slope at x=-1 is: f(x) = x2 -2 Rate of Change The slope at a point is called the derivative at that point Intuition: f(x) = x2 Measure the slope between two points that are really close together Rate of Change The slope at a point is called the derivative at that point Intuition: Measure the -
Gradient Descent (PDF)
18.657: Mathematics of Machine Learning Lecturer: Philippe Rigollet Lecture 11 Scribe: Kevin Li Oct. 14, 2015 2. CONVEX OPTIMIZATION FOR MACHINE LEARNING In this lecture, we will cover the basics of convex optimization as it applies to machine learning. There is much more to this topic than will be covered in this class so you may be interested in the following books. Convex Optimization by Boyd and Vandenberghe Lecture notes on Convex Optimization by Nesterov Convex Optimization: Algorithms and Complexity by Bubeck Online Convex Optimization by Hazan The last two are drafts and can be obtained online. 2.1 Convex Problems A convex problem is an optimization problem of the form min f(x) where f andC are x2C convex. First, we will debunk the idea that convex problems are easy by showing that virtually all optimization problems can be written as a convex problem. We can rewrite an optimization problem as follows. min f(x) , min t , min t X 2X t≥f(x);x2X (x;t)2epi(f) where the epigraph of a function is defined by epi(f)=f(x; t) 2X 2 ×IR : t ≥f(x)g Figure 1: An example of an epigraph. Source: https://en.wikipedia.org/wiki/Epigraph_(mathematics) 1 Now we observe that for linear functions, min c>x = min c>x x2D x2conv(D) where the convex hull is defined N N X X conv(D) = fy : 9 N 2 Z+; x1; : : : ; xN 2 D; αi ≥ 0; αi = 1; y = αixig i=1 i=1 To prove this, we know that the left side is a least as big as the right side since D ⊂ conv(D). -
Lecture 10: Recurrent Neural Networks
Lecture 10: Recurrent Neural Networks Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 10 - 1 May 7, 2020 Administrative: Midterm - Midterm next Tue 5/12 take home - 1H 20 Mins (+20m buffer) within a 24 hour time period. - Will be released on Gradescope. - See Piazza for detailed information - Midterm review session: Fri 5/8 discussion section - Midterm covers material up to this lecture (Lecture 10) Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 10 - 2 May 7, 2020 Administrative - Project proposal feedback has been released - Project milestone due Mon 5/18, see Piazza for requirements ** Need to have some baseline / initial results by then, so start implementing soon if you haven’t yet! - A3 will be released Wed 5/13, due Wed 5/27 Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 10 - 3 May 7, 2020 Last Time: CNN Architectures GoogLeNet AlexNet Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 10 - 4 May 7, 2020 Last Time: CNN Architectures ResNet SENet Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 10 - 5 May 7, 2020 Comparing complexity... An Analysis of Deep Neural Network Models for Practical Applications, 2017. Figures copyright Alfredo Canziani, Adam Paszke, Eugenio Culurciello, 2017. Reproduced with permission. Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 10 - 6 May 7, 2020 Efficient networks... MobileNets: Efficient Convolutional Neural Networks for Mobile Applications [Howard et al. 2017] - Depthwise separable convolutions replace BatchNorm standard convolutions by Pool BatchNorm factorizing them into a C2HW Pointwise Conv (1x1, C->C) convolutions depthwise convolution and a Pool 1x1 convolution BatchNorm 9C2HW Conv (3x3, C->C) - Much more efficient, with Pool little loss in accuracy Stanford network Conv (3x3, C->C, Depthwise 2 9CHW - Follow-up MobileNetV2 work Total compute:9C HW groups=C) convolutions in 2018 (Sandler et al.) MobileNets - ShuffleNet: Zhang et al, Total compute:9CHW + C2HW CVPR 2018 Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 10 - May 7, 2020 Meta-learning: Learning to learn network architectures..