Applying Deep Belief Networks to the Game of Go
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Deep Belief Networks for Phone Recognition
Deep Belief Networks for phone recognition Abdel-rahman Mohamed, George Dahl, and Geoffrey Hinton Department of Computer Science University of Toronto {asamir,gdahl,hinton}@cs.toronto.edu Abstract Hidden Markov Models (HMMs) have been the state-of-the-art techniques for acoustic modeling despite their unrealistic independence assumptions and the very limited representational capacity of their hidden states. There are many proposals in the research community for deeper models that are capable of modeling the many types of variability present in the speech generation process. Deep Belief Networks (DBNs) have recently proved to be very effective for a variety of ma- chine learning problems and this paper applies DBNs to acoustic modeling. On the standard TIMIT corpus, DBNs consistently outperform other techniques and the best DBN achieves a phone error rate (PER) of 23.0% on the TIMIT core test set. 1 Introduction A state-of-the-art Automatic Speech Recognition (ASR) system typically uses Hidden Markov Mod- els (HMMs) to model the sequential structure of speech signals, with local spectral variability mod- eled using mixtures of Gaussian densities. HMMs make two main assumptions. The first assumption is that the hidden state sequence can be well-approximated using a first order Markov chain where each state St at time t depends only on St−1. Second, observations at different time steps are as- sumed to be conditionally independent given a state sequence. Although these assumptions are not realistic, they enable tractable decoding and learning even with large amounts of speech data. Many methods have been proposed for relaxing the very strong conditional independence assumptions of standard HMMs (e.g. -
A Survey Paper on Deep Belief Network for Big Data
International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 5, September-October 2018, pp. 161–166, Article ID: IJCET_09_05_019 Available online at http://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5 Journal Impact Factor (2016): 9.3590(Calculated by GISI) www.jifactor.com ISSN Print: 0976-6367 and ISSN Online: 0976–6375 © IAEME Publication A SURVEY PAPER ON DEEP BELIEF NETWORK FOR BIG DATA Azhagammal Alagarsamy Research Scholar, Bharathiyar University, Coimbatore, Tamil Nadu, India Assistant Professor, Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi, Tamilnadu Dr. K. Ruba Soundar Head, Department of CSE, PSR Engineering College, Sivakasi, Tamilnadu, India ABSTRACT Deep Belief Network (DBN) , as one of the deep learning architecture also noticeable machine learning technique, used in many applications like image processing, speech recognition and text mining. It uses supervised and unsupervised method to understand features in hierarchical architecture for the classification and pattern recognition. Recent year development in technologies has enabled the collection of large data. It also poses many challenges in data mining and processing due its nature of large volume, variety, velocity and veracity. In this paper we review the research of DBN for Big data. Key words: DBN, Big Data, Deep Belief Network, Classification. Cite this Article: Azhagammal Alagarsamy and Dr. K. Ruba Soundar, A Survey Paper on Deep Belief Network for Big Data. International Journal of Computer Engineering and Technology, 9(5), 2018, pp. 161-166. http://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5 1. INTRODUCTION Deep learning is also named as deep structured learning or hierarchal learning. -
Senior Issue 2020
2019 - 2020 The Miegian Vol. 63, Iss. 5 | Bishop Miege High School | 5041 Reinhardt Dr, Roeland Park, KS | July 17, 2020 2 CONTENTS 02 Senior Issue Introduction 04 The Miegian’s COVID-19 Response 06 Collage 08 Seniors and their Bright Futures 12 Collage 14 Featured Seniors 16 Senior Survey 18 Featured Writings 19 Senior Sports 24 Senior Advice & Teacher Memories 26 Farewell Miege 2019-2020 Miegian Staff Maren O’Connell Moira Sutherlin Luke Lazarczyk Editor-in-Chief Senior Issue Co-Editor Sports Editor Maddy McBride Kaitlin Thrun Isabel Copeland Staff Writer Staff Writer Staff Writer Sindy Garcia Allison Masilionis Cooper Brandmeyer Staff Writer Staff Designer Staff Photographer 3 Letter from the Editor Maren O’Connell eye-catching and a colorful keepsake granted the beauty of CTRL+Z (undo for each student if they so desire. keys). (Of course technology is often Editor-in-Chief The first step was transitioning unreliable—ahem—sometimes re- from a traditional newspaper to a sulting in an entire deletion of the f you know me, you probably are magazine-type format through the newspaper’s servers. Don’t worry, we aware that I tend to downplay my help of our printing company, Best recovered from this setback.) Iachievements and shrug them off Graphics. Mrs. McCambridge said After receiving a note from Ms. as not being that important. It was she shared that her editor loves the Hartwig at the end of my sophomore no exception when I learned that I smell of the pages, after I walked year, enticing me to join the news- was going to be Editor in Chief for into class raving about the scent. -
Auto-Encoding a Knowledge Graph Using a Deep Belief Network
ABSTRACT We started with a knowledge graph of connected entities and descriptive properties of those entities, from which, a hierarchical representation of the knowledge graph is derived. Using a graphical, energy-based neural network, we are able to show that the structure of the hierarchy can be internally captured by the neural network, which allows for efficient output of the underlying equilibrium distribution from which the data are drawn. AUTO-ENCODING A Robert A. Murphy [email protected] KNOWLEDGE GRAPH USING A DEEP BELIEF NETWORK A Random Fields Perspective Table of Contents Introduction .................................................................................................................................................. 2 GloVe for Knowledge Expansion ................................................................................................................... 2 The Approach ................................................................................................................................................ 3 Deep Belief Network ................................................................................................................................. 4 Random Field Setup .............................................................................................................................. 4 Random Field Illustration ...................................................................................................................... 5 Restricted Boltzmann Machine ................................................................................................................ -
Unsupervised Pre-Training of a Deep LSTM-Based Stacked Autoencoder for Multivariate Time Series Forecasting Problems Alaa Sagheer 1,2,3* & Mostafa Kotb2,3
www.nature.com/scientificreports OPEN Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems Alaa Sagheer 1,2,3* & Mostafa Kotb2,3 Currently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is increasing. Recently, the deep architecture of the recurrent neural network (RNN) and its variant long short-term memory (LSTM) have been proven to be more accurate than traditional statistical methods in modelling time series data. Despite the reported advantages of the deep LSTM model, its performance in modelling multivariate time series (MTS) data has not been satisfactory, particularly when attempting to process highly non-linear and long-interval MTS datasets. The reason is that the supervised learning approach initializes the neurons randomly in such recurrent networks, disabling the neurons that ultimately must properly learn the latent features of the correlated variables included in the MTS dataset. In this paper, we propose a pre-trained LSTM- based stacked autoencoder (LSTM-SAE) approach in an unsupervised learning fashion to replace the random weight initialization strategy adopted in deep LSTM recurrent networks. For evaluation purposes, two diferent case studies that include real-world datasets are investigated, where the performance of the proposed approach compares favourably with the deep LSTM approach. In addition, the proposed approach outperforms several reference models investigating the same case studies. Overall, the experimental results clearly show that the unsupervised pre-training approach improves the performance of deep LSTM and leads to better and faster convergence than other models. -
A Video Recognition Method by Using Adaptive Structural Learning Of
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network Shin Kamada Takumi Ichimura Advanced Artificial Intelligence Project Research Center, Advanced Artificial Intelligence Project Research Center, Research Organization of Regional Oriented Studies, Research Organization of Regional Oriented Studies, Prefectural University of Hiroshima and Faculty of Management and Information System, 1-1-71, Ujina-Higashi, Minami-ku, Prefectural University of Hiroshima Hiroshima 734-8558, Japan 1-1-71, Ujina-Higashi, Minami-ku, E-mail: [email protected] Hiroshima 734-8558, Japan E-mail: [email protected] Abstract—Deep learning builds deep architectures such as function that classifies an given image or detects an object multi-layered artificial neural networks to effectively represent while predicting the future, is required. Understanding of time multiple features of input patterns. The adaptive structural series video is expected in various kinds industrial fields, learning method of Deep Belief Network (DBN) can realize a high classification capability while searching the optimal network such as human detection, pose or facial estimation from video structure during the training. The method can find the optimal camera, autonomous driving system, and so on [10]. number of hidden neurons of a Restricted Boltzmann Machine LSTM (Long Short Term Memory) is a well-known method (RBM) by neuron generation-annihilation algorithm to train the for time-series prediction and is applied to deep learning given input data, and then it can make a new layer in DBN methods[11]. The method enabled the traditional recurrent by the layer generation algorithm to actualize a deep data representation. -
Friday, Dec. 14Th 7:30 to 9:00Am Celebration in Late October with Our An- You, Delton, for Your Service
NE Community Leader Spotlight - Mayor Laura Wheat 2 Tarrant County Domestic Relations Office 2 NE Transportation Summit- Save the Date! 3 NW Metroport Chamber of Commerce 4 United Way Fundraiser Follow-up 4 An Historic Moment In Time - Birdville Historical Society 5 Tarrant County Heroes Recognized 5 1895 Courthouse Bell Tower Rededication 5 Holiday Photo Contest Winner Announced 5 Mid-Cities Care Corps 6 Holy Trinity Catholic School 6 Haslet Public Library 7 Honoring our Veterans 7 Transportation Updates on Northeast Tarrant County Road Projects 8 Calendar of Events 9 Contact Information 9 Christmas is here along with some County. He will be enjoying time at his cold weather, and it is time to cele- home in the country and not having to drive brate. The Precinct 3 staff started the one hour or more one way to work. Thank Friday, Dec. 14th 7:30 to 9:00am celebration in late October with our an- you, Delton, for your service. Enjoy the nual United Way Fundraising BBQ time…you will be missed. Please join me for coffee and donut s! Luncheon. We served over 200 people – Happenings in Tarrant County in the barbecue brisket and sausage, potato last few months: salad, beans, bread, desserts, ice tea or water - while raising funds for United The 1895 Courthouse Clock Tower Way of Tarrant County. (see page 4) Rededication in late October (see photo page 5). Northeast Sub-Courthouse Our staff Christmas photo is at our Precinct 3 will be moving into a new 645 Grapevine Hwy., Hurst TX 76054 Maintenance Center on Precinct Line Courthouse in late 2014 or early 2015. -
Youtube Charts
Youtube Top 100 Charts For May 12, 2021 TW LW Song Artist Weeks Views 26 22 Peaches Justin Bieber 7 3589742 1 51 Your Power Billie Eilish 2 8262948 27 In the morning ITZY 1 3512315 2 2 RAPSTAR Polo G 4 7594479 28 33 Outside MO3 & Og Bobby Billions 7 3437020 3 1 MONTERO (Call Me By Your Name) Lil Nas X 6 7130480 29 27 Streets Doja Cat 17 3436033 4 SORRY NOT SORRY DJ Khaled 1 6807026 30 29 First Day Out (Beat Box) NLE Choppa 5 3228511 5 6 Track Star Mooski 12 6115626 31 31 Hellcats & Trackhawks Only the Family & Lil Durk 9 3222937 6 5 Leave The Door Open Bruno Mars, Anderson .Paak & Silk Sonic 9 6036525 32 74 BIG PURR (Prrdd) (feat. Pooh Shiesty) Coi Leray 6 3168973 7 7 Back In Blood (feat. Lil Durk) Pooh Shiesty 26 5965224 33 25 deja vu Olivia Rodrigo 6 3165571 8 Final Warning NLE Choppa 1 5902722 34 32 Best Friend (feat. Doja Cat) Saweetie 16 3085580 9 4 On Me Lil Baby 22 5849669 35 28 Period (feat. DaBaby) Boosie Badazz 3 2972902 10 8 Calling My Phone Lil Tjay & 6LACK 12 5704679 36 24 If Pain Was A Person Moneybagg Yo 2 2933191 11 9 BeatBox 2 (feat. Pooh Shiesty) SpotemGottem 20 5345570 37 63 Wockesha Moneybagg Yo 2 2888930 12 14 Kiss Me More (feat. SZA) Doja Cat 4 5130894 38 34 DÁKITI Bad Bunny & Jhay Cortez 27 2846925 13 11 No More Parties (feat. Lil Durk) Coi Leray 14 4945698 39 36 Throat Baby (Go Baby) BRS Kash 35 2806002 14 3 Save Your Tears The Weeknd & Ariana Grande 18 4897168 40 Shit Crazy (feat. -
Board Game Accessibility for Persons with Visual Impairment by Frederico
Board Game Accessibility for Persons with Visual Impairment by Frederico Da Rocha Tomé Filho A Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Computer Science The Faculty of Science University of Ontario Institute of Technology Supervisors: Dr. Pejman Mirza-Babaei Dr. Bill Kapralos Oshawa, Ontario, Canada December 2018 Copyright © Frederico da Rocha Tomé Filho, 2018 THESIS EXAMINATION INFORMATION Submitted by: Frederico da Rocha Tomé Filho Master of Science in Computer Science Thesis title: Board Game Accessibility for Persons with Visual Impairment An oral defense of this thesis took place on December 5, 2018 in front of the following examining committee: Examining Committee: Chair of Examining Committee Dr. Karthik Sankaranarayanan Research Supervisor Dr. Pejman Mirza-Babaei Research Co-supervisor Dr. Bill Kapralos Examining Committee Member Dr. Alvaro Uribe-Quevedo External Examiner Dr. Loutfouz Zaman The above committee determined that the thesis is acceptable in form and content and that a satisfactory knowledge of the field covered by the thesis was demonstrated by the candidate during an oral examination. A signed copy of the Certificate of Approval is available from the School of Graduate and Postdoctoral Studies. Abstract Despite the huge popularity and benefits of traditional board games, they present serious accessibility issues to players, particularly those with visual impairments, due to the heavy use of visuals to communi- cate gameplay information. Limited research investigating -
Central Florida Future, Vol. 28 No. 05, September 7, 1995
University of Central Florida STARS Central Florida Future University Archives 9-7-1995 Central Florida Future, Vol. 28 No. 05, September 7, 1995 Part of the Mass Communication Commons, Organizational Communication Commons, Publishing Commons, and the Social Influence and oliticalP Communication Commons Find similar works at: https://stars.library.ucf.edu/centralfloridafuture University of Central Florida Libraries http://library.ucf.edu This Newsletter is brought to you for free and open access by the University Archives at STARS. It has been accepted for inclusion in Central Florida Future by an authorized administrator of STARS. For more information, please contact [email protected]. Recommended Citation "Central Florida Future, Vol. 28 No. 05, September 7, 1995" (1995). Central Florida Future. 1319. https://stars.library.ucf.edu/centralfloridafuture/1319 Culpepper named Division I-AA offensive player of the week-p. 14 The Flori Future Get a jump on next I The 'Noles give 0-town a demonstration year's job market a recruiting-consulting firm in New D Internships and York. Whil e Ha nigan 's research ) preparation will shows a modest upturn in 1994 , th e get you the job · damage has been done. Not only are fewer ent~y-level jobs available you want. upon graduation (only half as many COLLEGE PRESS SERVICE as 1989), but in anticipation of this Special to the Future reduced hiring, many internship pro grams have been scaled back as As fall gets underway, on- well. campus job interviews begin on These data mean that honing many colleges and universities your job-interviewing skills is more around the nation'. -
Hope Remains” - a Conversation with Renee Yohe Please Note: This Transcript Has Been Lightly Edited to Remove Filler Words Or Sounds
Episode BW03: “Hope Remains” - A Conversation With Renee Yohe Please note: This transcript has been lightly edited to remove filler words or sounds. RENEE YOHE: And, again, that defiant response that something in me was still sitting there saying, “But, but that might be different. There might be something else, but just in case there's something else, at least, you know, put a lifeline out. Just one more thing.” And I was willing to take that action in calling. I made a phone call. And that's all it took. Even though I couldn't see it in that moment, even though I was in the depths of despair, that hope that I still mattered and that there was a future for me, and that nothing was beyond redemption in my story or anyone else's–that was something I was able to hold on to. [music playing] LINDSAY KOLSH: Y ou're listening to the To Write Love on Her Arms Podcast, a show about mental health and the things that make us human. Each episode we bring you conversations about the things that can feel hard to talk about, like depression, addiction, self-injury, and suicide. We share stories and explore big themes like hope, healing, and recovery. [music playing] CHAD MOSES: This is the second of the three Black and White statements we released a few weeks ago in honor of Mental Health Month. You may recall that, as we introduced this statement, we were drawn back to the original story where Renee told us to “Remember hope.” Renee likened it to the stars, saying that while our view may be obscured by dust and clouds and storms, they’re still there, that hope still remains. -
Deep Reinforcement Learning with Experience Replay Based on SARSA
Deep Reinforcement Learning with Experience Replay Based on SARSA Dongbin Zhao, Haitao Wang, Kun Shao and Yuanheng Zhu Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences, Beijing 100190, China [email protected], [email protected], [email protected], [email protected] Abstract—SARSA, as one kind of on-policy reinforcement designed to deal with decision-making problems, it has run into learning methods, is integrated with deep learning to solve the great difficulties when handling high dimension data. With the video games control problems in this paper. We use deep development of feature detection method like DL, such convolutional neural network to estimate the state-action value, problems are to be well solved. and SARSA learning to update it. Besides, experience replay is introduced to make the training process suitable to scalable A new method, called deep reinforcement learning (DRL), machine learning problems. In this way, a new deep emerges to lead the direction of advanced AI research. DRL reinforcement learning method, called deep SARSA is proposed combines excellent perceiving ability of DL with decision- to solve complicated control problems such as imitating human making ability of RL. In 2010, Lange [10] proposed a typical to play video games. From the experiments results, we can algorithm which applied a deep auto-encoder neural network conclude that the deep SARSA learning shows better (DANN) into a visual control task. Later, Abtahi and Fasel [11] performances in some aspects than deep Q learning. employed a deep belief network (DBN) as the function approximation to improve the learning efficiency of traditional Keywords—SARSA learning; Q learning; experience replay; neural fitted-Q method.