Representations in Deep Neural Nets. Paul Humphreys July
|
|
- Brett Golden
- 5 years ago
- Views:
Transcription
1 Representations in Deep Neural Nets Paul Humphreys July
2 Deep learning methods: those that are formed by the composition of multiple non-linear transformations, with the goal of yielding more abstract -- and ultimately more useful -- representations (Bengio et al 2014, p. 1) `Deep neural networks exploit the property that many natural signals are compositional hierarchies, in which higher level features are obtained by composing lower level ones. In images, local combinations of edges form motifs, motifs assemble into parts, and parts form objects. Similar hierarchies exist in speech and text from sounds to phones, phonemes, syllables, words and sentences. The pooling allows representations to vary very little when elements in the previous layer vary in position and appearance. ( LeCun et al 2015, p.439)
3 Convolutional Neural Network 3
4 Definition: (Philosophy) A representation is compositional if what the constituent elements represent remains invariant when the elements are embedded in more complex representations, and what the complex representation represents is a function of the invariant representations of its constituents (and the structure of the complex representation). If a representation R is compositional, then providing an intentional interpretation for the basic (primitive) representations will provide an interpretation for the compound representation R.
5 One use of the term `abstraction in DNN is that minor variations between internal representations of the same type of object, such as small variations in the edge positions, are suppressed. It also can involve amplifying the features that are important. An example of this use is: `For classification tasks, higher layers of representation amplify aspects of the input that are important for discrimination and suppress irrelevant variations (LeCun, et al 2015, p.436) This is a similar account of `abstract to one OED definition (thanks to Chip Levy for the source): `Considered or understood without reference to particular instances or concrete examples: representing the intrinsic, general properties of something in isolation from the peculiar properties of any specific instance or example
6 Representational Opacity A representation is transparent if it represents the states of a system in a way that is open to explicit scrutiny, analysis, interpretation, and understanding by humans, and transitions between those states are represented by rules that have similar properties. Examples: Linguistic representations in the humanities, most mathematical representations in the sciences. An opaque representation is a representation that is not transparent. Example: The representations (if indeed there are any) in many deep neural nets. 6
7 Right hand image is an inverse Radon transform of the left hand sinogram. Is representational content always preserved under mathematical and computational transformations? 7
8 8
9 From Fatescapes by Pavel Smejkal 9
10 What transformations are permissible in arriving at an effective representational process? Possible Answer: Any transformation that maps the referential content in one representational space to another representational space and that preserves the referential content of the initial representation is permissible. 10
11 Conjecture: The content of the fundamental (primitive) representations in DNNs is determined by the training process. When the training process is supervised, there will be a contribution to the content from human intentionality. Under some circumstances, in contrast, unsupervised ab initio reinforcement learning contains very little interpretative content.
12 Reliabilism An instrument has the knowledge that F if and only if the instrument contains a representation of the entirety of F, the representation holds of the target, and a reliable process forms the representation, where F is a fact and a reliable representation-producing process is one that produces a high proportion of accurate representations. 12
Deep learning for music data processing
Deep learning for music data processing A personal (re)view of the state-of-the-art Jordi Pons www.jordipons.me Music Technology Group, DTIC, Universitat Pompeu Fabra, Barcelona. 31st January 2017 Jordi
More informationLecture 9 Source Separation
10420CS 573100 音樂資訊檢索 Music Information Retrieval Lecture 9 Source Separation Yi-Hsuan Yang Ph.D. http://www.citi.sinica.edu.tw/pages/yang/ yang@citi.sinica.edu.tw Music & Audio Computing Lab, Research
More informationCS 1674: Intro to Computer Vision. Face Detection. Prof. Adriana Kovashka University of Pittsburgh November 7, 2016
CS 1674: Intro to Computer Vision Face Detection Prof. Adriana Kovashka University of Pittsburgh November 7, 2016 Today Window-based generic object detection basic pipeline boosting classifiers face detection
More informationDeep Neural Networks Scanning for patterns (aka convolutional networks) Bhiksha Raj
Deep Neural Networks Scanning for patterns (aka convolutional networks) Bhiksha Raj 1 Story so far MLPs are universal function approximators Boolean functions, classifiers, and regressions MLPs can be
More informationStructured training for large-vocabulary chord recognition. Brian McFee* & Juan Pablo Bello
Structured training for large-vocabulary chord recognition Brian McFee* & Juan Pablo Bello Small chord vocabularies Typically a supervised learning problem N C:maj C:min C#:maj C#:min D:maj D:min......
More information9 th Grade ENGLISH II 2 nd Six Weeks CSCOPE CURRICULUM MAP Timeline: 6 weeks (Units 2A & 2B) RESOURCES TEKS CONCEPTS GUIDING QUESTIONS
Timeline: 6 weeks (Units 2A & 2B) Unit 2A: E2.1A determine the Verbals & Loaded Words Are some words meaning of grade-level technical better than others? academic English words in multiple content areas
More informationAn Introduction to Deep Image Aesthetics
Seminar in Laboratory of Visual Intelligence and Pattern Analysis (VIPA) An Introduction to Deep Image Aesthetics Yongcheng Jing College of Computer Science and Technology Zhejiang University Zhenchuan
More informationImage-to-Markup Generation with Coarse-to-Fine Attention
Image-to-Markup Generation with Coarse-to-Fine Attention Presenter: Ceyer Wakilpoor Yuntian Deng 1 Anssi Kanervisto 2 Alexander M. Rush 1 Harvard University 3 University of Eastern Finland ICML, 2017 Yuntian
More informationAbout Giovanni De Poli. What is Model. Introduction. di Poli: Methodologies for Expressive Modeling of/for Music Performance
Methodologies for Expressiveness Modeling of and for Music Performance by Giovanni De Poli Center of Computational Sonology, Department of Information Engineering, University of Padova, Padova, Italy About
More informationXuelong Li, Thomas Huang. University of Illinois at Urbana-Champaign
Non-Negative N Graph Embedding Jianchao Yang, Shuicheng Yan, Yun Fu, Xuelong Li, Thomas Huang Department of ECE, Beckman Institute and CSL University of Illinois at Urbana-Champaign Outline Non-negative
More informationFirst Step Towards Enhancing Word Embeddings with Pitch Accents for DNN-based Slot Filling on Recognized Text
First Step Towards Enhancing Word Embeddings with Pitch Accents for DNN-based Slot Filling on Recognized Text Sabrina Stehwien, Ngoc Thang Vu IMS, University of Stuttgart March 16, 2017 Slot Filling sequential
More informationTesting Digital Systems II
Testing Digital Systems II Lecture 5: Built-in Self Test (I) Instructor: M. Tahoori Copyright 2010, M. Tahoori TDS II: Lecture 5 1 Outline Introduction (Lecture 5) Test Pattern Generation (Lecture 5) Pseudo-Random
More informationProblems. Speech Perception Facts and things. Talker Normalization. Lack of Invariance Problem. Why the lack of invariance?
Problems Lack of Invariance Problem Speech Perception Facts and things Lack of invariance Talker normalization Segmentation Speech is too fast to hear! There is no unique acoustic pattern associated with
More informationFoundations in Data Semantics. Chapter 4
Foundations in Data Semantics Chapter 4 1 Introduction IT is inherently incapable of the analog processing the human brain is capable of. Why? Digital structures consisting of 1s and 0s Rule-based system
More informationArts, Computers and Artificial Intelligence
Arts, Computers and Artificial Intelligence Sol Neeman School of Technology Johnson and Wales University Providence, RI 02903 Abstract Science and art seem to belong to different cultures. Science and
More informationApplication of Measurement Instrumentation (1)
Slide Nr. 0 of 23 Slides Application of Measurement Instrumentation (1) Slide Nr. 1 of 23 Slides Application of Measurement Instrumentation (2) a. Monitoring of processes and operations 1. Thermometers,
More informationDesign of Fault Coverage Test Pattern Generator Using LFSR
Design of Fault Coverage Test Pattern Generator Using LFSR B.Saritha M.Tech Student, Department of ECE, Dhruva Institue of Engineering & Technology. Abstract: A new fault coverage test pattern generator
More informationIncommensurability and Partial Reference
Incommensurability and Partial Reference Daniel P. Flavin Hope College ABSTRACT The idea within the causal theory of reference that names hold (largely) the same reference over time seems to be invalid
More informationА. A BRIEF OVERVIEW ON TRANSLATION THEORY
Ефимова А. A BRIEF OVERVIEW ON TRANSLATION THEORY ABSTRACT Translation has existed since human beings needed to communicate with people who did not speak the same language. In spite of this, the discipline
More informationJazz Melody Generation from Recurrent Network Learning of Several Human Melodies
Jazz Melody Generation from Recurrent Network Learning of Several Human Melodies Judy Franklin Computer Science Department Smith College Northampton, MA 01063 Abstract Recurrent (neural) networks have
More informationLINGUISTICS 321 Lecture #8. BETWEEN THE SEGMENT AND THE SYLLABLE (Part 2) 4. SYLLABLE-TEMPLATES AND THE SONORITY HIERARCHY
LINGUISTICS 321 Lecture #8 BETWEEN THE SEGMENT AND THE SYLLABLE (Part 2) 4. SYLLABLE-TEMPLATES AND THE SONORITY HIERARCHY Syllable-template for English: [21] Only the N position is obligatory. Study [22]
More informationSinger Traits Identification using Deep Neural Network
Singer Traits Identification using Deep Neural Network Zhengshan Shi Center for Computer Research in Music and Acoustics Stanford University kittyshi@stanford.edu Abstract The author investigates automatic
More informationGender and Age Estimation from Synthetic Face Images with Hierarchical Slow Feature Analysis
Gender and Age Estimation from Synthetic Face Images with Hierarchical Slow Feature Analysis Alberto N. Escalante B. and Laurenz Wiskott Institut für Neuroinformatik, Ruhr-University of Bochum, Germany,
More informationA New Scheme for Citation Classification based on Convolutional Neural Networks
A New Scheme for Citation Classification based on Convolutional Neural Networks Khadidja Bakhti 1, Zhendong Niu 1,2, Ally S. Nyamawe 1 1 School of Computer Science and Technology Beijing Institute of Technology
More informationAudio spectrogram representations for processing with Convolutional Neural Networks
Audio spectrogram representations for processing with Convolutional Neural Networks Lonce Wyse 1 1 National University of Singapore arxiv:1706.09559v1 [cs.sd] 29 Jun 2017 One of the decisions that arise
More informationGlossary alliteration allusion analogy anaphora anecdote annotation antecedent antimetabole antithesis aphorism appositive archaic diction argument
Glossary alliteration The repetition of the same sound or letter at the beginning of consecutive words or syllables. allusion An indirect reference, often to another text or an historic event. analogy
More informationLEARNING AUDIO SHEET MUSIC CORRESPONDENCES. Matthias Dorfer Department of Computational Perception
LEARNING AUDIO SHEET MUSIC CORRESPONDENCES Matthias Dorfer Department of Computational Perception Short Introduction... I am a PhD Candidate in the Department of Computational Perception at Johannes Kepler
More informationInternational Journal of Advance Engineering and Research Development MUSICAL INSTRUMENT IDENTIFICATION AND STATUS FINDING WITH MFCC
Scientific Journal of Impact Factor (SJIF): 5.71 International Journal of Advance Engineering and Research Development Volume 5, Issue 04, April -2018 e-issn (O): 2348-4470 p-issn (P): 2348-6406 MUSICAL
More informationClassical Music Generation in Distinct Dastgahs with AlimNet ACGAN
Classical Music Generation in Distinct Dastgahs with AlimNet ACGAN Saber Malekzadeh Computer Science Department University of Tabriz Tabriz, Iran Saber.Malekzadeh@sru.ac.ir Maryam Samami Islamic Azad University,
More informationRole of Color Processing in Display
Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 7 (2017) pp. 2183-2190 Research India Publications http://www.ripublication.com Role of Color Processing in Display Mani
More informationSyllabus for Music Secondary cycle (S1-S5)
Schola Europaea Office of the Secretary-General Pedagogical Development Unit Ref: 2017-01-D-60-en-3 Orig.: EN Syllabus for Music Secondary cycle (S1-S5) APPROVED BY THE JOINT TEACHING COMMITTEE ON 9 AND
More informationVBM683 Machine Learning
VBM683 Machine Learning Pinar Duygulu Slides are adapted from Dhruv Batra, David Sontag, Aykut Erdem Quotes If you were a current computer science student what area would you start studying heavily? Answer:
More informationCS 7643: Deep Learning
CS 7643: Deep Learning Topics: Computational Graphs Notation + example Computing Gradients Forward mode vs Reverse mode AD Dhruv Batra Georgia Tech Administrativia HW1 Released Due: 09/22 PS1 Solutions
More informationReply to Stalnaker. Timothy Williamson. In Models and Reality, Robert Stalnaker responds to the tensions discerned in Modal Logic
1 Reply to Stalnaker Timothy Williamson In Models and Reality, Robert Stalnaker responds to the tensions discerned in Modal Logic as Metaphysics between contingentism in modal metaphysics and the use of
More informationLearning Word Meanings and Descriptive Parameter Spaces from Music. Brian Whitman, Deb Roy and Barry Vercoe MIT Media Lab
Learning Word Meanings and Descriptive Parameter Spaces from Music Brian Whitman, Deb Roy and Barry Vercoe MIT Media Lab Music intelligence Structure Structure Genre Genre / / Style Style ID ID Song Song
More informationIntroduction: A Musico-Logical Offering
Chapter 3 Introduction: A Musico-Logical Offering Normal is a Distribution Unknown 3.1 Introduction to the Introduction As we have finally reached the beginning of the book proper, these notes should mirror
More informationMETA-COGNITIVE UNITY IN INDIRECT PROOFS
META-COGNITIVE UNITY IN INDIRECT PROOFS Ferdinando Arzarello, Cristina Sabena Dipartimento di Matematica, Università di Torino, Italia The paper focuses on indirect argumentation and proving processes
More informationTwentieth Excursus: Reference Magnets and the Grounds of Intentionality
Twentieth Excursus: Reference Magnets and the Grounds of Intentionality David J. Chalmers A recently popular idea is that especially natural properties and entites serve as reference magnets. Expressions
More informationCS 7643: Deep Learning
CS 7643: Deep Learning Topics: Stride, padding Pooling layers Fully-connected layers as convolutions Backprop in conv layers Dhruv Batra Georgia Tech Invited Talks Sumit Chopra on CNNs for Pixel Labeling
More informationData-Driven Solo Voice Enhancement for Jazz Music Retrieval
Data-Driven Solo Voice Enhancement for Jazz Music Retrieval Stefan Balke1, Christian Dittmar1, Jakob Abeßer2, Meinard Müller1 1International Audio Laboratories Erlangen 2Fraunhofer Institute for Digital
More informationA. Ideal Ratio Mask If there is no RIR, the IRM for time frame t and frequency f can be expressed as [17]: ( IRM(t, f) =
1 Two-Stage Monaural Source Separation in Reverberant Room Environments using Deep Neural Networks Yang Sun, Student Member, IEEE, Wenwu Wang, Senior Member, IEEE, Jonathon Chambers, Fellow, IEEE, and
More informationImpact of Deep Learning
Impact of Deep Learning Speech Recogni4on Computer Vision Recommender Systems Language Understanding Drug Discovery and Medical Image Analysis [Courtesy of R. Salakhutdinov] Deep Belief Networks: Training
More informationNoise (Music) Composition Using Classification Algorithms Peter Wang (pwang01) December 15, 2017
Noise (Music) Composition Using Classification Algorithms Peter Wang (pwang01) December 15, 2017 Background Abstract I attempted a solution at using machine learning to compose music given a large corpus
More informationRepresentations of Sound in Deep Learning of Audio Features from Music
Representations of Sound in Deep Learning of Audio Features from Music Sergey Shuvaev, Hamza Giaffar, and Alexei A. Koulakov Cold Spring Harbor Laboratory, Cold Spring Harbor, NY Abstract The work of a
More informationarxiv: v1 [cs.sd] 18 Oct 2017
REPRESENTATION LEARNING OF MUSIC USING ARTIST LABELS Jiyoung Park 1, Jongpil Lee 1, Jangyeon Park 2, Jung-Woo Ha 2, Juhan Nam 1 1 Graduate School of Culture Technology, KAIST, 2 NAVER corp., Seongnam,
More informationDeepID: Deep Learning for Face Recognition. Department of Electronic Engineering,
DeepID: Deep Learning for Face Recognition Xiaogang Wang Department of Electronic Engineering, The Chinese University i of Hong Kong Machine Learning with Big Data Machine learning with small data: overfitting,
More informationCity, University of London Institutional Repository
City Research Online City, University of London Institutional Repository Citation: Seago, K. (2017). Reading, Translating, Rewriting: Angela Carter's Translational Poetics. Translation Studies, 10(1),
More informationAUTOMATIC MUSIC TRANSCRIPTION WITH CONVOLUTIONAL NEURAL NETWORKS USING INTUITIVE FILTER SHAPES. A Thesis. presented to
AUTOMATIC MUSIC TRANSCRIPTION WITH CONVOLUTIONAL NEURAL NETWORKS USING INTUITIVE FILTER SHAPES A Thesis presented to the Faculty of California Polytechnic State University, San Luis Obispo In Partial Fulfillment
More informationHSE Learning from Fatal Coating Plant Accident
HSE Learning from Fatal Coating Plant Accident Incident description On Wednesday 4 th October 2006, at about 1700 hours, a five-man cleaning crew was cleaning a concrete mixer unit at the premises of a
More informationON GESTURAL MEANING IN ACTS OF EXPRESSION
ON GESTURAL MEANING IN ACTS OF EXPRESSION Sunnie D. Kidd In this presentation the focus is on what Maurice Merleau-Ponty calls the gestural meaning of the word in language and speech as it is an expression
More informationAnnotating Expressions of Opinions and Emotions in Language
Annotating Expressions of Opinions and Emotions in Language Janyce Wiebe, Theresa Wilson, and Claire Cardie Kuan Ting Chen University of Pennsylvania kche@seas.upenn.edu February 4, 2013 K. Chen CIS 630
More informationLanguage & Literature Comparative Commentary
Language & Literature Comparative Commentary What are you supposed to demonstrate? In asking you to write a comparative commentary, the examiners are seeing how well you can: o o READ different kinds of
More informationOff-line Handwriting Recognition by Recurrent Error Propagation Networks
Off-line Handwriting Recognition by Recurrent Error Propagation Networks A.W.Senior* F.Fallside Cambridge University Engineering Department Trumpington Street, Cambridge, CB2 1PZ. Abstract Recent years
More informationOn the Analogy between Cognitive Representation and Truth
On the Analogy between Cognitive Representation and Truth Mauricio SUÁREZ and Albert SOLÉ BIBLID [0495-4548 (2006) 21: 55; pp. 39-48] ABSTRACT: In this paper we claim that the notion of cognitive representation
More informationBOOK REVIEWS. University of Southern California. The Philosophical Review, XCI, No. 2 (April 1982)
obscurity of purpose makes his continual references to science seem irrelevant to our views about the nature of minds. This can only reinforce what Wilson would call the OA prejudices that he deplores.
More informationPromoting Poor Features to Supervisors: Some Inputs Work Better as Outputs
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs Rich Caruana JPRC and Carnegie Mellon University Pittsburgh, PA 15213 caruana@cs.cmu.edu Virginia R. de Sa Sloan Center for Theoretical
More informationTitle. Author(s)Lai, Wai Ling. Citation 哲学 = Annals of the Philosophical Society of Hokkaido. Issue Date Doc URL. Type.
Title The Dilemma of Intentionality Author(s)Lai, Wai Ling Citation 哲学 = Annals of the Philosophical Society of Hokkaido Issue Date 2010-03-21 Doc URL http://hdl.handle.net/2115/45269 Type bulletin (article)
More informationStage 5 unit starter Novel: Miss Peregrine s home for peculiar children
Stage 5 unit starter Novel: Miss Peregrine s home for peculiar children Rationale Through the close study of Miss Peregrine s home for peculiar children, students will explore the ways that genre can be
More informationMusic Emotion Recognition. Jaesung Lee. Chung-Ang University
Music Emotion Recognition Jaesung Lee Chung-Ang University Introduction Searching Music in Music Information Retrieval Some information about target music is available Query by Text: Title, Artist, or
More informationDiotima s Speech as Apophasis
Diotima s Speech as Apophasis A Holistic Reading of the Symposium 2013-03-20 RELIGST 290 Lee, Tae Shin Among philosophical texts, Plato s dialogues present a challenge that is infrequent, if not rare:
More informationAn AI Approach to Automatic Natural Music Transcription
An AI Approach to Automatic Natural Music Transcription Michael Bereket Stanford University Stanford, CA mbereket@stanford.edu Karey Shi Stanford Univeristy Stanford, CA kareyshi@stanford.edu Abstract
More informationMusic Composition with RNN
Music Composition with RNN Jason Wang Department of Statistics Stanford University zwang01@stanford.edu Abstract Music composition is an interesting problem that tests the creativity capacities of artificial
More informationIntroduction to Data Conversion and Processing
Introduction to Data Conversion and Processing The proliferation of digital computing and signal processing in electronic systems is often described as "the world is becoming more digital every day." Compared
More informationLooking at Movies. From the text by Richard Barsam. In this presentation: Beginning to think about what Looking at Movies in a new way means.
Looking at Movies From the text by Richard Barsam. In this presentation: Beginning to think about what Looking at Movies in a new way means. 1 Cinematic Language The visual vocabulary of film Composed
More informationExpressive performance in music: Mapping acoustic cues onto facial expressions
International Symposium on Performance Science ISBN 978-94-90306-02-1 The Author 2011, Published by the AEC All rights reserved Expressive performance in music: Mapping acoustic cues onto facial expressions
More informationDistortion Analysis Of Tamil Language Characters Recognition
www.ijcsi.org 390 Distortion Analysis Of Tamil Language Characters Recognition Gowri.N 1, R. Bhaskaran 2, 1. T.B.A.K. College for Women, Kilakarai, 2. School Of Mathematics, Madurai Kamaraj University,
More informationChord Classification of an Audio Signal using Artificial Neural Network
Chord Classification of an Audio Signal using Artificial Neural Network Ronesh Shrestha Student, Department of Electrical and Electronic Engineering, Kathmandu University, Dhulikhel, Nepal ---------------------------------------------------------------------***---------------------------------------------------------------------
More informationHow to overcome/avoid High Frequency Effects on Debug Interfaces Trace Port Design Guidelines
How to overcome/avoid High Frequency Effects on Debug Interfaces Trace Port Design Guidelines An On-Chip Debugger/Analyzer (OCD) like isystem s ic5000 (Figure 1) acts as a link to the target hardware by
More informationBrand Guidelines. v.4 - February, 2018
Brand Guidelines v.4 - February, 2018 Hello! We Are SAV Digital Environments, 2018 Brand Guidelines»2« A Comprehensive, Process-Driven, Integration Firm 2018 Brand Guidelines»3« Offering Design & Installation
More information452 AMERICAN ANTHROPOLOGIST [N. S., 21, 1919
452 AMERICAN ANTHROPOLOGIST [N. S., 21, 1919 Nubuloi Songs. C. R. Moss and A. L. Kroeber. (University of California Publications in American Archaeology and Ethnology, vol. 15, no. 2, pp. 187-207, May
More informationCCE RR REVISED & UN-REVISED KARNATAKA SECONDARY EDUCATION EXAMINATION BOARD, MALLESWARAM, BANGALORE G È.G È.G È..
CCE RR REVISED & UN-REVISED O %lo ÆË v ÃO y Æ fio» flms ÿ,» fl Ê«fiÀ M, ÊMV fl 560 003 KARNATAKA SECONDARY EDUCATION EXAMINATION BOARD, MALLESWARAM, BANGALORE 560 003 G È.G È.G È.. Æ fioê, d È 2018 S.
More informationLOW POWER & AREA EFFICIENT LAYOUT ANALYSIS OF CMOS ENCODER
90 LOW POWER & AREA EFFICIENT LAYOUT ANALYSIS OF CMOS ENCODER Tanuj Yadav Electronics & Communication department National Institute of Teacher s Training and Research Chandigarh ABSTRACT An Encoder is
More informationConsiderations for Specifying, Installing and Interfacing Rotary Incremental Optical Encoders
Considerations for Specifying, Installing and Interfacing Rotary Incremental Optical Encoders Scott Hewitt, President SICK STEGMANN, INC. Dayton, OH www.stegmann.com sales@stegmann.com 800-811-9110 The
More informationAn assessment of learned score features for modeling expressive dynamics in music
TRANSACTIONS ON MULTIMEDIA: SPECIAL ISSUE ON MUSIC DATA MINING 1 An assessment of learned score features for modeling expressive dynamics in music Maarten Grachten, Florian Krebs Abstract The study of
More informationWhy Is It Important Today to Show and Look at Images of Destroyed Human Bodies?
Why Is It Important Today to Show and Look at Images of Destroyed Human Bodies? I will try to clarify, in eight points, why it s important today to look at images of mutilated human bodies like those I
More information2016 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT , 2016, SALERNO, ITALY
216 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 13 16, 216, SALERNO, ITALY A FULLY CONVOLUTIONAL DEEP AUDITORY MODEL FOR MUSICAL CHORD RECOGNITION Filip Korzeniowski and
More informationOutline. EECS150 - Digital Design Lecture 27 - Asynchronous Sequential Circuits. Cross-coupled NOR gates. Asynchronous State Transition Diagram
EECS150 - Digital Design Lecture 27 - Asynchronous Sequential Circuits Nov 26, 2002 John Wawrzynek Outline SR Latches and other storage elements Synchronizers Figures from Digital Design, John F. Wakerly
More informationDeep Jammer: A Music Generation Model
Deep Jammer: A Music Generation Model Justin Svegliato and Sam Witty College of Information and Computer Sciences University of Massachusetts Amherst, MA 01003, USA {jsvegliato,switty}@cs.umass.edu Abstract
More informationCS/MA 109 Quantitative Reasoning. Wayne Snyder Computer Science Department Boston University
CS/MA 109 Quantitative Reasoning Wayne Snyder Department Boston University Today Recursion and self-reference: a scientific and culture exploration Next: Cryptography Soon: Artificial Intelligence and
More informationMUSICAL INSTRUMENT RECOGNITION WITH WAVELET ENVELOPES
MUSICAL INSTRUMENT RECOGNITION WITH WAVELET ENVELOPES PACS: 43.60.Lq Hacihabiboglu, Huseyin 1,2 ; Canagarajah C. Nishan 2 1 Sonic Arts Research Centre (SARC) School of Computer Science Queen s University
More informationHistorical/Biographical
Historical/Biographical Biographical avoid/what it is not Research into the details of A deep understanding of the events Do not confuse a report the author s life and works and experiences of an author
More informationSinging voice synthesis based on deep neural networks
INTERSPEECH 2016 September 8 12, 2016, San Francisco, USA Singing voice synthesis based on deep neural networks Masanari Nishimura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, and Keiichi Tokuda
More informationMidterm Exam. Academic Year 1435/1436 H (2014/2015 G),First Semester (141) Applied Linguistics Department. ENGL 101 Writing the Paragraph
Kingdom of Saudi Arabia The Royal Commission at Yanbu Yanbu University College Yanbu Al-Sinaiyah Midterm Exam Academic Year 1435/1436 H (2014/2015 G),First Semester (141) Applied Linguistics Department
More informationNotes on Digital Circuits
PHYS 331: Junior Physics Laboratory I Notes on Digital Circuits Digital circuits are collections of devices that perform logical operations on two logical states, represented by voltage levels. Standard
More informationIMPLEMENTING AI-AIDED CONTENT DISTRIBUTION STRATEGIES IN THE GDPR ERA
IMPLEMENTING AI-AIDED CONTENT DISTRIBUTION STRATEGIES IN THE GDPR ERA Alain Nochimowski, Alice Wittenberg, David Zucker Viaccess-Orca, Israel ABSTRACT While the mathematical foundations of artificial intelligence
More informationMELONET I: Neural Nets for Inventing Baroque-Style Chorale Variations
MELONET I: Neural Nets for Inventing Baroque-Style Chorale Variations Dominik Hornel dominik@ira.uka.de Institut fur Logik, Komplexitat und Deduktionssysteme Universitat Fridericiana Karlsruhe (TH) Am
More informationMUSIC tags are descriptive keywords that convey various
JOURNAL OF L A TEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging Keunwoo Choi, György Fazekas, Member, IEEE, Kyunghyun Cho,
More informationPEP-Lower Elementary Report Card 12-13
PEP-Lower Elementary Report Card - Student Name tical Life The student understands and follows the ground rules. Lakeland Montessori Lower Elementary (6-9) The student exhibits self-control in group lessons;
More informationWhy is there the need for explanation? objects and their realities Dr Kristina Niedderer Falmouth College of Arts, England
Why is there the need for explanation? objects and their realities Dr Kristina Niedderer Falmouth College of Arts, England An ongoing debate in doctoral research in art and design
More informationENGINEERING COMMITTEE Interface Practices Subcommittee SCTE STANDARD SCTE
ENGINEERING COMMITTEE Interface Practices Subcommittee SCTE STANDARD Test Method for Reverse Path (Upstream) Intermodulation Using Two Carriers NOTICE The Society of Cable Telecommunications Engineers
More informationThe Turing Test and Its Discontents
The Turing Test and Its Discontents Administrivia Class Website: http://l3d.cs.colorado.edu/~ctg/classes/issmeth08/issmeth0 8.html Midterm paper (due March 18; 35 percent of grade) Final paper (due May
More informationDirections and Implementation
Directions and Implementation Thank you so much for purchasing my Forms of Energy Flip-Flap Book! I hope you enjoy implementing it in your classroom. This Flip-Flap book is to be used in conjunction with
More informationNew forms of video compression
New forms of video compression New forms of video compression Why is there a need? The move to increasingly higher definition and bigger displays means that we have increasingly large amounts of picture
More informationgresearch Focus Cognitive Sciences
Learning about Music Cognition by Asking MIR Questions Sebastian Stober August 12, 2016 CogMIR, New York City sstober@uni-potsdam.de http://www.uni-potsdam.de/mlcog/ MLC g Machine Learning in Cognitive
More informationUsing Variational Autoencoders to Learn Variations in Data
Using Variational Autoencoders to Learn Variations in Data By Dr. Ethan M. Rudd and Cody Wild Often, we would like to be able to model probability distributions of high-dimensional data points that represent
More informationTransforming Electronic Interconnect Breaking through historical boundaries Tim Olson Founder & CTO
Transforming Electronic Interconnect Breaking through historical boundaries Tim Olson Founder & CTO Remember when? There were three distinct industries Wafer Foundries SATS EMS Semiconductor Devices Nanometers
More informationChord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations
Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations Hendrik Vincent Koops 1, W. Bas de Haas 2, Jeroen Bransen 2, and Anja Volk 1 arxiv:1706.09552v1 [cs.sd]
More informationThank you for your continued support, and as always your feedback is welcome.
Subject: New Tait Logo Dear Sir/Madam, To visually demonstrate the importance of the relationship between Tait Communications and your business, we have created a new Tait logo for you to use. The term
More informationDISTRIBUTION STATEMENT A 7001Ö
Serial Number 09/678.881 Filing Date 4 October 2000 Inventor Robert C. Higgins NOTICE The above identified patent application is available for licensing. Requests for information should be addressed to:
More informationAutomatic Construction of Synthetic Musical Instruments and Performers
Ph.D. Thesis Proposal Automatic Construction of Synthetic Musical Instruments and Performers Ning Hu Carnegie Mellon University Thesis Committee Roger B. Dannenberg, Chair Michael S. Lewicki Richard M.
More information