educating text autoencoders latent representation
play

Educating Text Autoencoders: Latent Representation Guidance via - PowerPoint PPT Presentation

Aug 05, 2023 •150 likes •451 views

Educating Text Autoencoders: Latent Representation Guidance via Denoising Tianxiao Shen Jonas Mueller Regina Barzilay Tommi Jaakkola Text Autoencoders Represent sentences as vectors in a latent space This talk is awesome encoder decoder

  1. Educating Text Autoencoders: Latent Representation Guidance via Denoising Tianxiao Shen Jonas Mueller Regina Barzilay Tommi Jaakkola

  2. Text Autoencoders Represent sentences as vectors in a latent space This talk is awesome encoder decoder This talk is awesome

  3. Text Autoencoders Represent sentences as vectors in a latent space Manipulate sentences via modifying their latent representation tense + vector This talk was awesome This talkisawesome sentiment - This talk is awful vector

  4. Latent Space Geometry Which mapping between sentences and latent vectors will be learned? Sentence Space Latent Space This talk is awesome This talk was awesome ? It’s sunny today It was sunny yesterday

  5. Latent Space Geometry Fortuitous geometry that captures sentence semantics is unlikely to arise Sentence Space Latent Space AAE Latent Space This talk is awesome This talk was awesome It’s sunny today It was sunny yesterday

  6. Latent Space Geometry Fortuitous geometry that captures sentence semantics is unlikely to arise Minimal latent space manipulations can yield random, unpredictable changes in the resulting text Sentence Space Latent Space AAE Latent Space This talk is awesome This talk was awesome It’s sunny today It was sunny yesterday

  7. <latexit sha1_base64="Yo8xQgU091/mBCITSaS45dNrSEg=">ACA3icbZDLSsNAFIYn9VbrLepON4NFaEFKIgV1VxDRZQV7gTaEyXTSDp1JwsxE2saCG1/FjQtF3PoS7nwbp20W2vrDwMd/zuHM+b2IUaks69vILC2vrK5l13Mbm1vbO+buXl2GscCkhkMWiqaHJGE0IDVFSPNSBDEPUYaXv9yUm/cEyFpGNypYUQcjroB9SlGSlueTBqS8phVBgVT+Bgxu51YfAwKrpm3ipZU8FsFPIg1RV1/xqd0IcxIozJCULduKlJMgoShmZJxrx5JECPdRl7Q0BogT6STG8bwWDsd6IdCv0DBqft7IkFcyiH3dCdHqifnaxPzv1orVv65k9AgihUJ8GyRHzOoQjgJBHaoIFixoQaEBdV/hbiHBMJKx5bTIdjzJy9C/bRkl0sXt+V85SqNIwsOwREoABucgQq4AVQAxg8gmfwCt6MJ+PFeDc+Zq0ZI53ZB39kfP4AIxeWlg=</latexit> <latexit sha1_base64="w1WwdmCQgKP8+X1VJ/5sTU/pn/I=">AB6HicbVDLSgNBEOyNrxhfUY9eBoPgKexKQL0FVPCYgHlAsoTZSW8yZnZ2mZkVQsgXePGgiFc/yZt/4yTZgyYWNBRV3XR3BYng2rjut5NbW9/Y3MpvF3Z29/YPiodHTR2nimGDxSJW7YBqFxiw3AjsJ0opFEgsBWMbmZ+6wmV5rF8MOME/YgOJA85o8ZK9dteseSW3TnIKvEyUoIMtV7xq9uPWRqhNExQrTuemxh/QpXhTOC0E01JpSN6A7lkoaofYn80On5MwqfRLGypY0ZK7+npjQSOtxFNjOiJqhXvZm4n9eJzXhlT/hMkNSrZYFKaCmJjMviZ9rpAZMbaEMsXtrYQNqaLM2GwKNgRv+eV0rwoe5Xydb1Sqt5lceThBE7hHDy4hCrcQw0awADhGV7hzXl0Xpx352PRmnOymWP4A+fzB5wjNc=</latexit> <latexit sha1_base64="BkYWtfe7+OkEV4gwnrmRnKw/E5c=">AB9HicbVBNS8NAEJ34WeNX1aOXxVKol5JIQb0VPBYwX5AG8pmu2mXbjZxd1NoQ3+HFw+KePXHePfuG1z0NYHA4/3ZpiZ58ecKe0439ba+sbm1nZux97d2z84zB8dN1SUSELrJOKRbPlYUc4ErWumOW3FkuLQ57TpD29mfnNEpWKReNTjmHoh7gsWMIK1kbzipKNYiOLS5Ny+7eYLTtmZA60SNyMFyFDr5r86vYgkIRWacKxU23Vi7aVYakY4ndqdRNEYkyHu07ahAodUen86CkqGqWHgkiaEhrN1d8TKQ6VGoe+6QyxHqhlbyb+57UTHVx5KRNxoqkgi0VBwpGO0CwB1GOSEs3HhmAimbkVkQGWmGiTk21CcJdfXiWNi7JbKV8/VArVuyOHJzCGZTAhUuowj3UoA4EnuAZXuHNGlkv1rv1sWhds7KZE/gD6/MH/iuQ+A=</latexit> <latexit sha1_base64="hXZqviBsmUQqQGwr2Sc4CvsupiM=">AB9HicbVBNS8NAEJ34WeNX1aOXxVKol5JIQb0VRPRYwX5AG8pmu2mXbjZxd1NoQ3+HFw+KePXHePfuG1z0NYHA4/3ZpiZ58ecKe0439ba+sbm1nZux97d2z84zB8dN1SUSELrJOKRbPlYUc4ErWumOW3FkuLQ57TpD29mfnNEpWKReNTjmHoh7gsWMIK1kbzipKNYiOLS5Ny+6+YLTtmZA60SNyMFyFDr5r86vYgkIRWacKxU23Vi7aVYakY4ndqdRNEYkyHu07ahAodUen86CkqGqWHgkiaEhrN1d8TKQ6VGoe+6QyxHqhlbyb+57UTHVx5KRNxoqkgi0VBwpGO0CwB1GOSEs3HhmAimbkVkQGWmGiTk21CcJdfXiWNi7JbKV8/VArV2yOHJzCGZTAhUuowj3UoA4EnuAZXuHNGlkv1rv1sWhds7KZE/gD6/MHAsaQ+w=</latexit> <latexit sha1_base64="jaHG0f1sBa5w6CahuBe9dhn5kTI=">AB6HicbVDLSgNBEOyNrxhfUY9eBoPgKexKQL0FJOAxAfOAZAmzk95kzOzsMjMrhJAv8OJBEa9+kjf/xkmyB0saCiqunuChLBtXHdbye3sbm1vZPfLeztHxweFY9PWjpOFcMmi0WsOgHVKLjEpuFGYCdRSKNAYDsY3839hMqzWP5YCYJ+hEdSh5yRo2VGrV+seSW3QXIOvEyUoIM9X7xqzeIWRqhNExQrbuemxh/SpXhTOCs0Es1JpSN6RC7lkoaofani0Nn5MIqAxLGypY0ZKH+npjSOtJFNjOiJqRXvXm4n9eNzXhjT/lMkNSrZcFKaCmJjMvyYDrpAZMbGEMsXtrYSNqKLM2GwKNgRv9eV10roqe5XybaNSqtayOPJwBudwCR5cQxXuoQ5NYIDwDK/w5jw6L86787FszTnZzCn8gfP5A520jNg=</latexit> <latexit sha1_base64="pWrBzfpVufV4naiCogWV9S+MqJs=">AB9HicbVBNS8NAEJ3Urxq/qh69LJZCvZRECuqtIAWPFWwrtKFstpt26W4SdzeFNvR3ePGgiFd/jDf/jds2B219MPB4b4aZeX7MmdKO823lNja3tnfyu/be/sHhUeH4pKWiRBLaJBGP5KOPFeUspE3NKePsaRY+Jy2/dHt3G+PqVQsCh/0JKaewIOQBYxgbSvNO0qJlBcnl7Y9V6h6FScBdA6cTNShAyNXuGr249ImioCcdKdVwn1l6KpWaE05ndTRSNMRnhAe0YGmJBlZcujp6hklH6KIikqVCjhfp7IsVCqYnwTafAeqhWvbn4n9dJdHDtpSyME01DslwUJBzpCM0TQH0mKdF8YgmkplbERliYk2OdkmBHf15XSuqy41crNfbVYq2dx5OEMzqEMLlxBDe6gAU0g8ATP8Apv1th6sd6tj2VrzspmTuEPrM8f/6+Q+Q=</latexit> <latexit sha1_base64="q5P6dhkuGC2Q8lVi/5jR942U=">AB6HicbVDLSgNBEOyNrxhfUY9eBoPgKexKQL0FBPGYgHlAsoTZSW8yZnZ2mZkV4hLw7sWDIl79JG/+jZPHQaMFDUVN91dQSK4Nq75eRWVtfWN/Kbha3tnd294v5BU8epYthgsYhVO6AaBZfYMNwIbCcKaRQIbAWjq6nfukeleSxvzThBP6IDyUPOqLFS/aFXLldwbyl3gLUoIFar3iZ7cfszRCaZigWnc8NzF+RpXhTOCk0E01JpSN6A7lkoaofaz2aETcmKVPgljZUsaMlN/TmQ0nocBbYzomaol72p+J/XSU14WdcJqlByeaLwlQE5Pp16TPFTIjxpZQpri9lbAhVZQZm03BhuAtv/yXNM/KXqV8Wa+UqteP8zjycATHcAoenEMVbqAGDWCA8AQv8OrcOc/Om/M+b805iwgP4Recj28UZI2O</latexit> <latexit sha1_base64="KmHQ+z8AjUNlzY8GN4MNOeSuC+8=">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</latexit> <latexit sha1_base64="gx1WBYLCKHbjbkozjZKCMVTo7r4=">AB6HicbVDLSgNBEOyNrxhfUY9eBoPgKexKQL0FRPSYgHlAsoTZSW8yZnZ2mZkVQsgXePGgiFc/yZt/4yTZgyYWNBRV3XR3BYng2rjut5NbW9/Y3MpvF3Z29/YPiodHTR2nimGDxSJW7YBqFxiw3AjsJ0opFEgsBWMbmZ+6wmV5rF8MOME/YgOJA85o8ZK9bteseSW3TnIKvEyUoIMtV7xq9uPWRqhNExQrTuemxh/QpXhTOC0E01JpSN6A7lkoaofYn80On5MwqfRLGypY0ZK7+npjQSOtxFNjOiJqhXvZm4n9eJzXhlT/hMkNSrZYFKaCmJjMviZ9rpAZMbaEMsXtrYQNqaLM2GwKNgRv+eV0rwoe5Xydb1Sqt5mceThBE7hHDy4hCrcQw0awADhGV7hzXl0Xpx352PRmnOymWP4A+fzB6C8jNo=</latexit> <latexit sha1_base64="3pjpA3+sdcpWmb17KxkWg1NB7c=">AB6HicbVDLSgNBEOyNrxhfUY9eBoPgKexKQL0FBPGYgHlAsoTZSW8yZnZ2mZkVwxLw7sWDIl79JG/+jZPHQaMFDUVN91dQSK4Nq75eRWVtfWN/Kbha3tnd294v5BU8epYthgsYhVO6AaBZfYMNwIbCcKaRQIbAWjq6nfukeleSxvzThBP6IDyUPOqLFS/aFXLldwbyl3gLUoIFar3iZ7cfszRCaZigWnc8NzF+RpXhTOCk0E01JpSN6A7lkoaofaz2aETcmKVPgljZUsaMlN/TmQ0nocBbYzomaol72p+J/XSU14WdcJqlByeaLwlQE5Pp16TPFTIjxpZQpri9lbAhVZQZm03BhuAtv/yXNM/KXqV8Wa+UqteP8zjycATHcAoenEMVbqAGDWCA8AQv8OrcOc/Om/M+b805iwgP4Recj28RXI2M</latexit> <latexit sha1_base64="3pjpA3+sdcpWmb17KxkWg1NB7c=">AB6HicbVDLSgNBEOyNrxhfUY9eBoPgKexKQL0FBPGYgHlAsoTZSW8yZnZ2mZkVwxLw7sWDIl79JG/+jZPHQaMFDUVN91dQSK4Nq75eRWVtfWN/Kbha3tnd294v5BU8epYthgsYhVO6AaBZfYMNwIbCcKaRQIbAWjq6nfukeleSxvzThBP6IDyUPOqLFS/aFXLldwbyl3gLUoIFar3iZ7cfszRCaZigWnc8NzF+RpXhTOCk0E01JpSN6A7lkoaofaz2aETcmKVPgljZUsaMlN/TmQ0nocBbYzomaol72p+J/XSU14WdcJqlByeaLwlQE5Pp16TPFTIjxpZQpri9lbAhVZQZm03BhuAtv/yXNM/KXqV8Wa+UqteP8zjycATHcAoenEMVbqAGDWCA8AQv8OrcOc/Om/M+b805iwgP4Recj28RXI2M</latexit> <latexit sha1_base64="Vm9uwHja0Cr2XJrya0m2u7L26PU=">AB63icbVBNSwMxEJ2tX7V+VT16CRahXsquFKq3giAeK9gPaJeSTbNtaJdkqxQl4K/wIsHRbz6h7z5b8y2PWjrg4HezPMzAtizrRx3W8nt7a+sbmV3y7s7O7tHxQPj1o6ShShTRLxSHUCrClnkjYNM5x2YkWxCDhtB+PrzG8/UKVZJO/NJKa+wEPJQkawyaS4/HjeL5bcijsDWiXegpRgUa/+NUbRCQRVBrCsdZdz42Nn2JlGOF0WuglmsaYjPGQdi2VWFDtp7Nbp+jMKgMURsqWNGim/p5IsdB6IgLbKbAZ6WUvE/zuokJL/2UyTgxVJL5ojDhyEQoexwNmKLE8IklmChmb0VkhBUmxsZTsCF4y+vktZFxatWru6qpfrN0zyOPJzAKZTBgxrU4RYa0AQCI3iGV3hzhPivDsf89acs4jwGP7A+fwBqcObQ=</latexit> Adversarial Autoencoder (AAE) encoder , decoder , discriminator G E D sample to generate new data z ∼ p ( z ) , x ∼ p G ( x | z ) reconstruction loss x z x G E This talk is awesome This talk is awesome D adversarial loss p ( z ) L rec − λ L adv min E,G max D [Makhzani et al., 2015]

Recommend

Variational Autoencoders Tom Fletcher March 25, 2019 Talking about this paper: Diederik Kingma

Variational Autoencoders Tom Fletcher March 25, 2019 Talking about this paper: Diederik Kingma

Variational Autoencoders Tom Fletcher March 25, 2019 Talking about this paper: Diederik Kingma and Max Welling, Auto-Encoding Variational Bayes, In International Conference on Learning Representation (ICLR) , 2014. Autoencoders Input Latent

409 views • 15 slides
CSC321 Lecture 20: Autoencoders Roger Grosse Roger Grosse CSC321 Lecture 20: Autoencoders 1 /

CSC321 Lecture 20: Autoencoders Roger Grosse Roger Grosse CSC321 Lecture 20: Autoencoders 1 /

CSC321 Lecture 20: Autoencoders Roger Grosse Roger Grosse CSC321 Lecture 20: Autoencoders 1 / 16 Overview Latent variable models so far: mixture models Boltzmann machines Both of these involve discrete latent variables. Now lets talk

200 views • 17 slides
From Variational to Deterministic Autoencoders or the joys of density estimation in latent spaces

From Variational to Deterministic Autoencoders or the joys of density estimation in latent spaces

From Variational to Deterministic Autoencoders or the joys of density estimation in latent spaces Antonio Vergari Joint work with: Partha Ghosh, Mehdi S.M. Sajjadi , Bernhard Schlkopf, Michael Black University of California, Los Angeles

1.29k views • 79 slides
Understanding and Organising the Latent Space of Autoencoders Alasdair Newson Tlcom

Understanding and Organising the Latent Space of Autoencoders Alasdair Newson Tlcom

Understanding and Organising the Latent Space of Autoencoders Alasdair Newson Tlcom ParisTech alasdair.newson@telecom-paristech.fr 6 February, 2020 1 / 53 1 / 53 Collaborators This work was carried out in collaboration with the

1.4k views • 71 slides
Retrieval by Content Part 3: Text Retrieval Latent Semantic Indexing Srihari: CSE 626 1 Latent

Retrieval by Content Part 3: Text Retrieval Latent Semantic Indexing Srihari: CSE 626 1 Latent

Retrieval by Content Part 3: Text Retrieval Latent Semantic Indexing Srihari: CSE 626 1 Latent Semantic Indexing (LSI) Disadvantage of exclusive use of representing a document as a T-dimensional vector of term weights Users may pose

667 views • 15 slides
Plug and Play Autoencoders for Conditional Text Generation Florian Mai , Nikolaos Pappas

Plug and Play Autoencoders for Conditional Text Generation Florian Mai , Nikolaos Pappas

Plug and Play Autoencoders for Conditional Text Generation Florian Mai , Nikolaos Pappas Ivan Montero Noah A. Smith , James Henderson Idiap Research Institute, EPFL, Switzerland University of Washington,

1.4k views • 36 slides
DeepMDP Learning Latent Space Continuous Models for Representation Learning Carles Gelada,

DeepMDP Learning Latent Space Continuous Models for Representation Learning Carles Gelada,

DeepMDP Learning Latent Space Continuous Models for Representation Learning Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare Simple Representations for RL 2 12 DeepMDP Latent Space Model: Neural networks MDP:

573 views • 17 slides
GANocracy Outline Background: Text Generation Latent-Variable Generation Learning

GANocracy Outline Background: Text Generation Latent-Variable Generation Learning

Controlling Text Generation Alexander Rush (and Sam Wiseman) Harvard / Cornell Tech GANocracy Outline Background: Text Generation Latent-Variable Generation Learning Neural Templates Machine Learning for Text Generation y 1: T =

710 views • 55 slides
Introduction to Deep Models Part II: Variational Autoencoders and Latent Spaces Nick Winovich

Introduction to Deep Models Part II: Variational Autoencoders and Latent Spaces Nick Winovich

TensorFlow Workshop 2018 Introduction to Deep Models Part II: Variational Autoencoders and Latent Spaces Nick Winovich Department of Mathematics Purdue University July 2018 SIAM@Purdue 2018 - Nick Winovich Introduction to Deep Models : Part

836 views • 25 slides
1 Introduction The Text Mining Process Text representation Learning Conclusion Introduction

1 Introduction The Text Mining Process Text representation Learning Conclusion Introduction

Introduction The Text Mining Process Text representation Learning Conclusion Introduction The Text Mining Process Text representation Learning Conclusion Aim of presentation Strategies in Identifying Issues Addressed in Legal Reports

640 views • 5 slides
CMP784 DEEP LEARNING Lecture #11 Variational Autoencoders Aykut Erdem // Hacettepe

CMP784 DEEP LEARNING Lecture #11 Variational Autoencoders Aykut Erdem // Hacettepe

latent by Tom White CMP784 DEEP LEARNING Lecture #11 Variational Autoencoders Aykut Erdem // Hacettepe University // Spring 2020 Artificial faces synthesized by StyleGAN (Nvidia) Previously on CMP784 Supervised vs. Unsupervised

1.48k views • 89 slides
Text Summarization Using A Trainable Summarizer and Latent Semantic Analysis Jen-Yuan Yeh 1 ,

Text Summarization Using A Trainable Summarizer and Latent Semantic Analysis Jen-Yuan Yeh 1 ,

Text Summarization Using A Trainable Summarizer and Latent Semantic Analysis Jen-Yuan Yeh 1 , Hao-Ren Ke 2 , and Wei-Pang Yang 1 1 Department of Computer &amp; Information Science, National Chiao-Tung University, Taiwan, R.O.C. 2 Digital Library

1.25k views • 36 slides
Adversarially Regularized Autoencoders Jake Zhao* 1 , 3 Yoon Kim* 2 Kelly Zhang 1 Alexander Rush 2

Adversarially Regularized Autoencoders Jake Zhao* 1 , 3 Yoon Kim* 2 Kelly Zhang 1 Alexander Rush 2

Adversarially Regularized Autoencoders Jake Zhao* 1 , 3 Yoon Kim* 2 Kelly Zhang 1 Alexander Rush 2 Yann LeCun 1 , 3 1 NYU CILVR Lab 2 Harvard NLP 3 Facebook AI Research Training Deep Latent Variable Models Two dominant approaches Variational

400 views • 27 slides
Representing Documents via Latent Keyphrase Inference April. 15 th , 2016 Document Representation

Representing Documents via Latent Keyphrase Inference April. 15 th , 2016 Document Representation

Representing Documents via Latent Keyphrase Inference April. 15 th , 2016 Document Representation in Vector Space Critical for document retrieval, categorization 2 Traditional Methods q Bag-of-Words or Phrases q Cons: Sparse on short texts 3 q

296 views • 26 slides
Text Representation http://www.cse.iitb.ac.in/~soumen/mining-the-web/ Ahmed Rafea Text

Text Representation http://www.cse.iitb.ac.in/~soumen/mining-the-web/ Ahmed Rafea Text

Text Representation http://www.cse.iitb.ac.in/~soumen/mining-the-web/ Ahmed Rafea Text Representation Document Preprocessing Vector Space Model for Document Storage Measure of Similarity 2 Document preprocessing(1/3)

453 views • 11 slides
Text Representation http://www.cse.iitb.ac.in/~soumen/mining-the-web/ Ahmed Rafea Text

Text Representation http://www.cse.iitb.ac.in/~soumen/mining-the-web/ Ahmed Rafea Text

Text Representation http://www.cse.iitb.ac.in/~soumen/mining-the-web/ Ahmed Rafea Text Representation Document Preprocessing Vector Space Model for Document Storage Measure of Similarity 2 Document preprocessing(1/4)

456 views • 13 slides
Latent Variable Models for Text, Event, and Network Data MURI Project: University of California,

Latent Variable Models for Text, Event, and Network Data MURI Project: University of California,

Latent Variable Models for Text, Event, and Network Data MURI Project: University of California, Irvine Annual Review Meeting December 8 th 2009 Padhraic Smyth (joint work with Arthur Asuncion and Chris DuBois) Event, Text, Network Data

641 views • 40 slides
MCMC and Variational Inference for AutoEncoders Achille Thin 1 , Alain Durmus 2 , Eric Moulines 1 1

MCMC and Variational Inference for AutoEncoders Achille Thin 1 , Alain Durmus 2 , Eric Moulines 1 1

Introduction Deep Latent Generative Models (DLGMs) MetFlow and MetVAE: MCMC &amp; VI From classical to Flow-based MCMC Experiments MCMC and Variational Inference for AutoEncoders Achille Thin 1 , Alain Durmus 2 , Eric Moulines 1 1 Ecole

799 views • 67 slides
Chinese Text Summarization Using A Trainable Summarizer and Latent Semantic Analysis Jen-Yuan Yeh

Chinese Text Summarization Using A Trainable Summarizer and Latent Semantic Analysis Jen-Yuan Yeh

Chinese Text Summarization Using A Trainable Summarizer and Latent Semantic Analysis Jen-Yuan Yeh 1 , Hao-Ren Ke 2 , and Wei-Pang Yang 1 1 Department of Computer &amp; Information Science, National Chiao-Tung University, Taiwan, R.O.C. 2 Digital

439 views • 31 slides
Image Representation CS 105 Data Representation Types of data: Numbers Text

Image Representation CS 105 Data Representation Types of data: Numbers Text

Image Representation CS 105 Data Representation Types of data: Numbers Text Audio Images &amp; Graphics Video What is an image? Rectangular grid of pixels- 5x5 grid If we are using 1 bit per cell, how many

463 views • 22 slides
learning using WaveNet autoencoders https://arxiv.org/abs/1901.08810 Jan Chorowski University of

learning using WaveNet autoencoders https://arxiv.org/abs/1901.08810 Jan Chorowski University of

Unsupervised speech representation learning using WaveNet autoencoders https://arxiv.org/abs/1901.08810 Jan Chorowski University of Wrocaw 06.06.2019 Deep Model = Hierarchy of Concepts Cat Dog Moon Banana M. Zieler, Visualizing

896 views • 53 slides
Topological Autoencoders 13. November, 2019 Machine Learning and Computational Biology Group, ETH

Topological Autoencoders 13. November, 2019 Machine Learning and Computational Biology Group, ETH

Topological Autoencoders 13. November, 2019 Machine Learning and Computational Biology Group, ETH Zurich Michael Moor , Max Horn , Bastian Rieck and Karsten Borgwardt Motivation Representation of our data , but in 100 dimensional

511 views • 24 slides
Text 1. A text is a sequence of characters 2. Each character is taken from a finite alphabete

Text 1. A text is a sequence of characters 2. Each character is taken from a finite alphabete

Computer Architecture applied computer science 02.03 Representation of non-numerical sets urbino worldwide campus 02 Information theory 02.03 Representation of non-numerical sets Texts Images Signals (Audio/Video)

420 views • 6 slides
PRINCIPAL COMPONENT ANALYSIS(PCA) By Deepen naorem Latent(hidden) representation Method A

PRINCIPAL COMPONENT ANALYSIS(PCA) By Deepen naorem Latent(hidden) representation Method A

PRINCIPAL COMPONENT ANALYSIS(PCA) By Deepen naorem Latent(hidden) representation Method A method or mechanism to see or view data(matrix) in different ways. Data matrix 2 4 5 7 Change of Basis Let us consider a scenario System

985 views • 16 slides

More recommend