The Future of Machine Intelligence
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Improved Policy Networks for Computer Go
Improved Policy Networks for Computer Go Tristan Cazenave Universite´ Paris-Dauphine, PSL Research University, CNRS, LAMSADE, PARIS, FRANCE Abstract. Golois uses residual policy networks to play Go. Two improvements to these residual policy networks are proposed and tested. The first one is to use three output planes. The second one is to add Spatial Batch Normalization. 1 Introduction Deep Learning for the game of Go with convolutional neural networks has been ad- dressed by [2]. It has been further improved using larger networks [7, 10]. AlphaGo [9] combines Monte Carlo Tree Search with a policy and a value network. Residual Networks improve the training of very deep networks [4]. These networks can gain accuracy from considerably increased depth. On the ImageNet dataset a 152 layers networks achieves 3.57% error. It won the 1st place on the ILSVRC 2015 classifi- cation task. The principle of residual nets is to add the input of the layer to the output of each layer. With this simple modification training is faster and enables deeper networks. Residual networks were recently successfully adapted to computer Go [1]. As a follow up to this paper, we propose improvements to residual networks for computer Go. The second section details different proposed improvements to policy networks for computer Go, the third section gives experimental results, and the last section con- cludes. 2 Proposed Improvements We propose two improvements for policy networks. The first improvement is to use multiple output planes as in DarkForest. The second improvement is to use Spatial Batch Normalization. 2.1 Multiple Output Planes In DarkForest [10] training with multiple output planes containing the next three moves to play has been shown to improve the level of play of a usual policy network with 13 layers. -
Lecture Notes Geoffrey Hinton
Lecture Notes Geoffrey Hinton Overjoyed Luce crops vectorially. Tailor write-ups his glasshouse divulgating unmanly or constructively after Marcellus barb and outdriven squeakingly, diminishable and cespitose. Phlegmatical Laurance contort thereupon while Bruce always dimidiating his melancholiac depresses what, he shores so finitely. For health care about working memory networks are the class and geoffrey hinton and modify or are A practical guide to training restricted boltzmann machines Lecture Notes in. Trajectory automatically learn about different domains, geoffrey explained what a lecture notes geoffrey hinton notes was central bottleneck form of data should take much like a single example, geoffrey hinton with mctsnets. Gregor sieber and password you may need to this course that models decisions. Jimmy Ba Geoffrey Hinton Volodymyr Mnih Joel Z Leibo and Catalin Ionescu. YouTube lectures by Geoffrey Hinton3 1 Introduction In this topic of boosting we combined several simple classifiers into very complex classifier. Separating Figure from stand with a Parallel Network Paul. But additionally has efficient. But everett also look up. You already know how the course that is just to my assignment in the page and writer recognition and trends in the effect of the motivating this. These perplex the or free liquid Intelligence educational. Citation to lose sight of language. Sparse autoencoder CS294A Lecture notes Andrew Ng Stanford University. Geoffrey Hinton on what's nothing with CNNs Daniel C Elton. Toronto Geoffrey Hinton Advanced Machine Learning CSC2535 2011 Spring. Cross validated is sparse, a proof of how to list of possible configurations have to see. Cnns that just download a computational power nor the squared error in permanent electrode array with respect to the. -
CSC321 Lecture 23: Go
CSC321 Lecture 23: Go Roger Grosse Roger Grosse CSC321 Lecture 23: Go 1 / 22 Final Exam Monday, April 24, 7-10pm A-O: NR 25 P-Z: ZZ VLAD Covers all lectures, tutorials, homeworks, and programming assignments 1/3 from the first half, 2/3 from the second half If there's a question on this lecture, it will be easy Emphasis on concepts covered in multiple of the above Similar in format and difficulty to the midterm, but about 3x longer Practice exams will be posted Roger Grosse CSC321 Lecture 23: Go 2 / 22 Overview Most of the problem domains we've discussed so far were natural application areas for deep learning (e.g. vision, language) We know they can be done on a neural architecture (i.e. the human brain) The predictions are inherently ambiguous, so we need to find statistical structure Board games are a classic AI domain which relied heavily on sophisticated search techniques with a little bit of machine learning Full observations, deterministic environment | why would we need uncertainty? This lecture is about AlphaGo, DeepMind's Go playing system which took the world by storm in 2016 by defeating the human Go champion Lee Sedol Roger Grosse CSC321 Lecture 23: Go 3 / 22 Overview Some milestones in computer game playing: 1949 | Claude Shannon proposes the idea of game tree search, explaining how games could be solved algorithmically in principle 1951 | Alan Turing writes a chess program that he executes by hand 1956 | Arthur Samuel writes a program that plays checkers better than he does 1968 | An algorithm defeats human novices at Go 1992 -
Halifax, Nova Scotia Canada August 13 17, 2017
Halifax, Nova Scotia Canada August 13 17, 2017 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Contents KDD 2017 Agenda at a Glance KDD 2017 Chairs’ Welcome Message Program Highlights Keynote Talks Research and Applied Data Science Tracks Applied Data Science Track Invited Talks Applied Data Science Panels Tutorials Hands‐On Tutorials Workshops KDD 2017 Tutorial Program KDD 2017 Workshop Program Full Day Workshops ‐ Monday August 14, 8:00am ‐5:00pm Half Day Workshops ‐ Monday August 14, 8:00am ‐ 12:00pm Half Day Workshops ‐ Monday August 14, 1:00am ‐ 5:00pm KDD Cup Workshop ‐ Wednesday August 16, 1:30pm ‐ 5:00pm KDD 2017 Hands‐On Tutorial Program Tuesday August 15, 2017 Wednesday August 16, 2017 Thursday August 17, 2017 KDD 2017 Conference Program Monday August 14 2017 Detailed Program Monday August 14, 2017 5:15pm – 7:00pm, KDD 2017 Opening Session ‐ Scoabank Centre Tuesday August 15, 2017 Detailed Program Wednesday August 16, 2017 Detailed Program Thursday August 17, 2017 Detailed Program KDD 2017 Conference Organizaon KDD 2017 Organizing Commiee Research Track Senior Program Commiee Applied Data Science Track Senior Program Commiee Research Track Program Commiee Applied Data Science Track Program Commiee KDD 2017 Sponsors & Supporters Halifax, Points of Interest Useful Links and Emergency Contacts KDD 2017 Agenda at a Glance Saturday, August 12th Level 8 Summit 8:00AM 5:00PM Workshop: Broadening Participation in Data Mining (BPDM) Day 1 Suite/Meeting Room 5 4:00PM 6:00PM KDD 2017 Registration Level 1 Atrium -
The History Began from Alexnet: a Comprehensive Survey on Deep Learning Approaches
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches Md Zahangir Alom1, Tarek M. Taha1, Chris Yakopcic1, Stefan Westberg1, Paheding Sidike2, Mst Shamima Nasrin1, Brian C Van Essen3, Abdul A S. Awwal3, and Vijayan K. Asari1 Abstract—In recent years, deep learning has garnered I. INTRODUCTION tremendous success in a variety of application domains. This new ince the 1950s, a small subset of Artificial Intelligence (AI), field of machine learning has been growing rapidly, and has been applied to most traditional application domains, as well as some S often called Machine Learning (ML), has revolutionized new areas that present more opportunities. Different methods several fields in the last few decades. Neural Networks have been proposed based on different categories of learning, (NN) are a subfield of ML, and it was this subfield that spawned including supervised, semi-supervised, and un-supervised Deep Learning (DL). Since its inception DL has been creating learning. Experimental results show state-of-the-art performance ever larger disruptions, showing outstanding success in almost using deep learning when compared to traditional machine every application domain. Fig. 1 shows, the taxonomy of AI. learning approaches in the fields of image processing, computer DL (using either deep architecture of learning or hierarchical vision, speech recognition, machine translation, art, medical learning approaches) is a class of ML developed largely from imaging, medical information processing, robotics and control, 2006 onward. Learning is a procedure consisting of estimating bio-informatics, natural language processing (NLP), cybersecurity, and many others. -
Uncertainty in Deep Learning
References Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jefrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geofrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. URL http://tensorĆow.org/. Software available from tensorĆow.org. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mane. Concrete problems in ai safety. arXiv preprint arXiv:1606.06565, 2016. C Angermueller and O Stegle. Multi-task deep neural network to predict CpG methylation proĄles from low-coverage sequencing data. In NIPS MLCB workshop, 2015. O Anjos, C Iglesias, F Peres, J Martínez, Á García, and J Taboada. Neural networks applied to discriminate botanical origin of honeys. Food chemistry, 175:128Ű136, 2015. Christopher Atkeson and Juan Santamaria. A comparison of direct and model-based reinforcement learning. In In International Conference on Robotics and Automation. Citeseer, 1997. Jimmy Ba and Brendan Frey. Adaptive dropout for training deep neural networks. In Advances in Neural Information Processing Systems, pages 3084Ű3092, 2013. P Baldi, P Sadowski, and D Whiteson. Searching for exotic particles in high-energy physics with deep learning. Nature communications, 5, 2014. Pierre Baldi and Peter J Sadowski. Understanding dropout. In Advances in Neural Information Processing Systems, pages 2814Ű2822, 2013. -
Mesh-Tensorflow: Deep Learning for Supercomputers
Mesh-TensorFlow: Deep Learning for Supercomputers Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman Google Brain {noam, ylc, nikip, trandustin, avaswani, penporn, phawkins, hyouklee, hongm, cliffy, rsepassi, blakehechtman}@google.com Abstract Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to dis- cover, describe, and to implement, particularly on large clusters. We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and op- erations along the "batch" dimension, in Mesh-TensorFlow, the user can specify any tensor-dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A Mesh-TensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication prim- itives such as Allreduce. We use Mesh-TensorFlow to implement an efficient data-parallel, model-parallel version of the Transformer [16] sequence-to-sequence model. Using TPU meshes of up to 512 cores, we train Transformer models with up to 5 billion parameters, surpassing state of the art results on WMT’14 English- to-French translation task and the one-billion-word language modeling benchmark. -
Big Graph Analytics Platforms
Full text available at: http://dx.doi.org/10.1561/1900000056 Big Graph Analytics Platforms Da Yan The University of Alabama at Birmingham [email protected] Yingyi Bu Couchbase, Inc. [email protected] Yuanyuan Tian IBM Almaden Research Center, USA [email protected] Amol Deshpande University of Maryland [email protected] Boston — Delft Full text available at: http://dx.doi.org/10.1561/1900000056 Foundations and Trends R in Databases Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 United States Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is D. Yan, Y. Bu, Y. Tian, and A. Deshpande. Big Graph Analytics Platforms. Foundations and Trends R in Databases, vol. 7, no. 1-2, pp. 1–195, 2015. R This Foundations and Trends issue was typeset in LATEX using a class file designed by Neal Parikh. Printed on acid-free paper. ISBN: 978-1-68083-242-6 c 2017 D. Yan, Y. Bu, Y. Tian, and A. Deshpande All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Cen- ter, Inc., 222 Rosewood Drive, Danvers, MA 01923. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by now Publishers Inc for users registered with the Copyright Clearance Center (CCC). -
An Introduction to Deep Reinforcement Learning Full Text Available At
Full text available at: http://dx.doi.org/10.1561/2200000071 An Introduction to Deep Reinforcement Learning Full text available at: http://dx.doi.org/10.1561/2200000071 Other titles in Foundations and Trends® in Machine Learning Non-convex Optimization for Machine Learningy Prateek Jain and Purushottam Ka ISBN: 978-1-68083-368-3 Kernel Mean Embedding of Distributions: A Review and Beyond Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur and Bernhard Scholkopf ISBN: 978-1-68083-288-4 Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions Andrzej Cichocki, Anh-Huy Phan, Qibin Zhao, Namgil Lee, Ivan Oseledets, Masashi Sugiyama and Danilo P. Mandic ISBN: 978-1-68083-222-8 Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives Andrzej Cichocki, Anh-Huy Phan, Qibin Zhao, Namgil Lee, Ivan Oseledets, Masashi Sugiyama and Danilo P. Mandic ISBN: 978-1-68083-276-1 Patterns of Scalable Bayesian Inference Elaine Angelino, Matthew James Johnson and Ryan P. Adams ISBN: 978-1-68083-218-1 Generalized Low Rank Models Madeleine Udell, Corinne Horn, Reza Zadeh and Stephen Boyd ISBN: 978-1-68083-140-5 Full text available at: http://dx.doi.org/10.1561/2200000071 An Introduction to Deep Reinforcement Learning Vincent François-Lavet Peter Henderson McGill University McGill University [email protected] [email protected] Riashat Islam Marc G. Bellemare McGill University Google Brain [email protected] [email protected] Joelle Pineau Facebook, McGill University [email protected] Boston — Delft Full text available at: http://dx.doi.org/10.1561/2200000071 Foundations and Trends® in Machine Learning Published, sold and distributed by: now Publishers Inc. -
Combining Tactical Search and Deep Learning in the Game of Go
Combining tactical search and deep learning in the game of Go Tristan Cazenave PSL-Universite´ Paris-Dauphine, LAMSADE CNRS UMR 7243, Paris, France [email protected] Abstract Elaborated search algorithms have been developed to solve tactical problems in the game of Go such as capture problems In this paper we experiment with a Deep Convo- [Cazenave, 2003] or life and death problems [Kishimoto and lutional Neural Network for the game of Go. We Muller,¨ 2005]. In this paper we propose to combine tactical show that even if it leads to strong play, it has search algorithms with deep learning. weaknesses at tactical search. We propose to com- Other recent works combine symbolic and deep learn- bine tactical search with Deep Learning to improve ing approaches. For example in image surveillance systems Golois, the resulting Go program. A related work [Maynord et al., 2016] or in systems that combine reasoning is AlphaGo, it combines tactical search with Deep with visual processing [Aditya et al., 2015]. Learning giving as input to the network the results The next section presents our deep learning architecture. of ladders. We propose to extend this further to The third section presents tactical search in the game of Go. other kind of tactical search such as life and death The fourth section details experimental results. search. 2 Deep Learning 1 Introduction In the design of our network we follow previous work [Mad- Deep Learning has been recently used with a lot of success dison et al., 2014; Tian and Zhu, 2015]. Our network is fully in multiple different artificial intelligence tasks. -
Arxiv:2102.01293V1 [Cs.LG] 2 Feb 2021
Scaling Laws for Transfer Danny Hernandez∗ Jared Kaplanyz Tom Henighany Sam McCandlishy OpenAI Abstract We study empirical scaling laws for transfer learning between distributions in an unsuper- vised, fine-tuning setting. When we train increasingly large neural networks from-scratch on a fixed-size dataset, they eventually become data-limited and stop improving in per- formance (cross-entropy loss). When we do the same for models pre-trained on a large language dataset, the slope in performance gains is merely reduced rather than going to zero. We calculate the effective data “transferred” from pre-training by determining how much data a transformer of the same size would have required to achieve the same loss when training from scratch. In other words, we focus on units of data while holding ev- erything else fixed. We find that the effective data transferred is described well in the low data regime by a power-law of parameter count and fine-tuning dataset size. We believe the exponents in these power-laws correspond to measures of the generality of a model and proximity of distributions (in a directed rather than symmetric sense). We find that pre-training effectively multiplies the fine-tuning dataset size. Transfer, like overall perfor- mance, scales predictably in terms of parameters, data, and compute. arXiv:2102.01293v1 [cs.LG] 2 Feb 2021 ∗Correspondence to: [email protected] Author contributions listed at end of paper. y Work performed at OpenAI z Johns Hopkins University Contents 1 Introduction 3 1.1 Key Results . .3 1.2 Notation . .6 2 Methods 6 3 Results 7 3.1 Ossification – can pre-training harm performance? . -
MMDS 2014: Workshop on Algorithms for Modern Massive Data Sets
MMDS 2014: Workshop on Algorithms for Modern Massive Data Sets Stanley Hall University of California at Berkeley June 17{20, 2014 The 2014 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2014) will address algorithmic and statistical challenges in modern large-scale data analysis. The goals of MMDS 2014 are to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and internet data sets; and to bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to promote the cross-fertilization of ideas. Organizers: Michael Mahoney, Alexander Shkolnik Petros Drineas, Reza Zadeh, and Fernando Perez Workshop Schedule Tuesday, June 17, 2014: Data Analysis and Statistical Data Analysis Time Event Location/Page Registration & Opening Lobby outside Stanley Hall 8:00{9:45am Breakfast and registration 9:45{10:00am Organizers Welcome and opening remarks First Session Stanley Hall 10:00{11:00am Ashok Srivastava, Verizon pp. 13 Large Scale Machine Learning at Verizon (Tutorial) 11:00{11:30am Dan Suciu, University of Washington pp. 14 Communication Cost in Big Data Processing 11:30{12:00pm Gerald Friedland, ICSI pp. 9 Content-based search in 50TB of consumer-produced videos 12:00{2:00pm Lunch (on your own) Second Session Stanley Hall 2:00{2:30pm Bill Howe, University of Washington pp. 10 Myria: Scalable Analytics as a Service 2:30{3:00pm Devavrat Shah, MIT pp. 13 Computing stationary distribution, locally 3:00{3:30pm Yiannis Koutis, University of Puerto Rico pp. 12 Spectral algorithms for graph mining and analysis 3:30{4:00pm Jiashun Jin, Carnegie Mellon University pp.