sequence models
play

Sequence Models Instructor: John Thickstun Discussion Board: - PowerPoint PPT Presentation

Oct 21, 2022 •304 likes •497 views

Sequence Models Instructor: John Thickstun Discussion Board: Available on Ed! Zoom Link: Available on Canvas Instructor Contact: thickstn@cs.washington.edu Course Webpage: https://courses.cs.washington.edu/courses/cse599i/20au/ CSE 599I:

  1. Sequence Models Instructor: John Thickstun Discussion Board: Available on Ed! Zoom Link: Available on Canvas Instructor Contact: thickstn@cs.washington.edu Course Webpage: https://courses.cs.washington.edu/courses/cse599i/20au/ CSE 599I: Generative Models Lecture 3 University of Washington

  2. <latexit sha1_base64="pz7X+WKUz3gx8FMFitS4K/hxct0=">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</latexit> <latexit sha1_base64="pz7X+WKUz3gx8FMFitS4K/hxct0=">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</latexit> <latexit sha1_base64="pz7X+WKUz3gx8FMFitS4K/hxct0=">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</latexit> <latexit sha1_base64="pz7X+WKUz3gx8FMFitS4K/hxct0=">ADLXicdVJLixNBEO6Mr3V8ZfXowcEQWGVYMkFQ2MvC5iCGMVkA+kYenp6kmb7MXR34sR2jv4aD170p3gQxKv/wLM12fhY1y3oro+q+urRXWkhuHWdzpdGcO78hYuXti6HV65eu36juX1zaPXCUDagWmgzSolgis2cNwJNioMIzIV7DA9Oqj9h0tmLNfqpVsVbCLJTPGcU+LANG3eKadJjDPtbFxOVYQ5HEncPE39i+pVtjtjq7nbVEp0GyAS20kf50u/ED0tGFZMpRQawdJ53CTwxjlPBqhAvLCsIPSIzNgaoiGR24teTVFEbLFmUawNHuWht/ZvhibR1ezFou5Jprd1c1qom2FgQx0rwxGkq49ICjcUzQ4o5p2WcC01cXPOd1sJW0clmXP5o4rkqFo4petxLvhCR01H9clHGDaNOrAQajiME9E5MYQ6eN8wxD0G8xr2FNI/K5ghTpv7HhMzk6SsYP4Zjmv030CPl8RUfgjXGX6ql5U/gOtPpT6BjrIe/L3k0MPIw+/lGV+W1bg78TuVv1f5dhFrQTv4awOs/wNw2/xXqvbDqGQYq+plpKo7Be18r3fSc6q47HSRlYeiyEzDrRZa9iU5N+9OA2G3d0E8PMHrf0nm53ZQrfRXbSDEvQ7aPHqI8GiKJ36D36iD4FH4LPwdfg23Fo0NhwbqETEnz/CeqRCkI=</latexit> <latexit sha1_base64="wJDhuAVhOb5IixefnWmq6D6Tcb8=">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</latexit> <latexit sha1_base64="wJDhuAVhOb5IixefnWmq6D6Tcb8=">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</latexit> <latexit sha1_base64="wJDhuAVhOb5IixefnWmq6D6Tcb8=">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</latexit> <latexit sha1_base64="wJDhuAVhOb5IixefnWmq6D6Tcb8=">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</latexit> <latexit sha1_base64="WgvhVay9SbqGu6FVNCYcSTcwaY=">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</latexit> <latexit sha1_base64="WgvhVay9SbqGu6FVNCYcSTcwaY=">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</latexit> <latexit sha1_base64="WgvhVay9SbqGu6FVNCYcSTcwaY=">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</latexit> <latexit sha1_base64="WgvhVay9SbqGu6FVNCYcSTcwaY=">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</latexit> A Small Clarification • A formal course in probability will carefully distinguish between 1. Random variables, e.g. X : Ω → R d . 2. Samples, e.g. x 1 , . . . , x n ∈ R d . • I will regularly blur the distinction between these objects. • If it helps, I will never talk about the formal construction in this course. Ω CSE 599I: Generative Models Lecture 3 University of Washington

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Sequence to Sequence models: Connectionist Temporal Classification 1 Sequence-to-sequence

Sequence to Sequence models: Connectionist Temporal Classification 1 Sequence-to-sequence

Deep Learning Sequence to Sequence models: Connectionist Temporal Classification 1 Sequence-to-sequence modelling Problem: A sequence goes in A different sequence comes out E.g. Speech recognition:

1.86k views • 172 slides
Sequence to Sequence models: Attention Models 1 Sequence-to-sequence modelling Problem:

Sequence to Sequence models: Attention Models 1 Sequence-to-sequence modelling Problem:

Deep Learning Sequence to Sequence models: Attention Models 1 Sequence-to-sequence modelling Problem: A sequence goes in A different sequence comes out E.g. Speech recognition: Speech goes in, a word

2.23k views • 167 slides
Sequence to Sequence models: Attention Models 1 Sequence-to-sequence modelling Problem:

Sequence to Sequence models: Attention Models 1 Sequence-to-sequence modelling Problem:

Deep Learning Sequence to Sequence models: Attention Models 1 Sequence-to-sequence modelling Problem: A sequence goes in A different sequence comes out E.g. Speech recognition: Speech goes in, a word

1.77k views • 162 slides
Machine Translation/ Sequence-to-sequence Models Graham Neubig Site

Machine Translation/ Sequence-to-sequence Models Graham Neubig Site

CS11-737 Multilingual NLP Machine Translation/ Sequence-to-sequence Models Graham Neubig Site http://demo.clab.cs.cmu.edu/11737fa20/ Language Models Language models are generative models of text s ~ P(x) The Malfoys! said Hermione.

584 views • 37 slides
Sequence-to-sequence Models and Attention Graham Neubig Preliminaries: Language Models

Sequence-to-sequence Models and Attention Graham Neubig Preliminaries: Language Models

Sequence-to-sequence Models and Attention Graham Neubig Preliminaries: Language Models Language Models Language models are generative models of text s ~ P(x) The Malfoys! said Hermione. Harry was watching him. He looked like

1.18k views • 37 slides
Sequence to Sequence models: Connectionist Temporal Classification 5 March 2018 1

Sequence to Sequence models: Connectionist Temporal Classification 5 March 2018 1

Deep Learning Sequence to Sequence models: Connectionist Temporal Classification 5 March 2018 1 Sequence-to-sequence modelling Problem: A sequence 1 goes in A different sequence 1 comes out

1.44k views • 141 slides
Introduction to sequence to sequence models N ATURAL LAN GUAGE GEN ERATION IN P YTH ON

Introduction to sequence to sequence models N ATURAL LAN GUAGE GEN ERATION IN P YTH ON

Introduction to sequence to sequence models N ATURAL LAN GUAGE GEN ERATION IN P YTH ON Biswanath Halder Data Scientist Sequence to sequence generation Output a sequence given a sequence as input. Fixed length input. Fixed length output.

884 views • 39 slides
Sequence to Sequence Models for Machine Translation (2) CMSC 723 / LING 723 / INST 725 Marine

Sequence to Sequence Models for Machine Translation (2) CMSC 723 / LING 723 / INST 725 Marine

Sequence to Sequence Models for Machine Translation (2) CMSC 723 / LING 723 / INST 725 Marine Carpuat Slides &amp; figure credits: Graham Neubig Introduction to Neural Machine Translation Neural language models review Sequence to

1.25k views • 25 slides
Machine Translation and Sequence-to-sequence Models http://phontron.com/class/mtandseq2seq2018/

Machine Translation and Sequence-to-sequence Models http://phontron.com/class/mtandseq2seq2018/

Machine Translation and Sequence-to-sequence Models Machine Translation and Sequence-to-sequence Models http://phontron.com/class/mtandseq2seq2018/ Graham Neubig Carnegie Mellon University CS 11-731 1 Machine Translation and

653 views • 44 slides
Recurrent Neural Models: Language Models, and Sequence Prediction and Generation CMSC 473/673

Recurrent Neural Models: Language Models, and Sequence Prediction and Generation CMSC 473/673

Recurrent Neural Models: Language Models, and Sequence Prediction and Generation CMSC 473/673 Frank Ferraro WARNING: Neural methods are NOT the only way to do sequence prediction: Structured Perceptron (478/678) Hidden Markov Models

1.03k views • 79 slides
Neural AMR: Sequence-to- Sequence Models for Parsing and Generation Author: Ioannis Konstas,

Neural AMR: Sequence-to- Sequence Models for Parsing and Generation Author: Ioannis Konstas,

Neural AMR: Sequence-to- Sequence Models for Parsing and Generation Author: Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, Luke Zettlemoyer Presenter: Yuan Cheng Contents Background Outline of the Paper

274 views • 15 slides
Latent Models: Sequence Models Beyond HMMs and Machine Translation Alignment CMSC 473/673 UMBC

Latent Models: Sequence Models Beyond HMMs and Machine Translation Alignment CMSC 473/673 UMBC

Latent Models: Sequence Models Beyond HMMs and Machine Translation Alignment CMSC 473/673 UMBC Outline Review: EM for HMMs Machine Translation Alignment Limited Sequence Models Maximum Entropy Markov Models Conditional Random Fields

1.2k views • 101 slides
Latent Models: Sequence Models Beyond HMMs and Machine Translation Alignment CMSC 473/673 UMBC

Latent Models: Sequence Models Beyond HMMs and Machine Translation Alignment CMSC 473/673 UMBC

Latent Models: Sequence Models Beyond HMMs and Machine Translation Alignment CMSC 473/673 UMBC Outline Review: EM for HMMs Machine Translation Alignment Limited Sequence Models Maximum Entropy Markov Models Conditional Random Fields

1.37k views • 100 slides
A Quick Introduction to Machine Translation with Sequence-to-Sequence Models Kevin Duh Johns

A Quick Introduction to Machine Translation with Sequence-to-Sequence Models Kevin Duh Johns

A Quick Introduction to Machine Translation with Sequence-to-Sequence Models Kevin Duh Johns Hopkins University Fall 2019 Number of Languages in the World 6000 Image courtesy of nasa.gov There

429 views • 40 slides
Sequence to Sequence Models Matt Gormley Lecture 5 Sep. 11, 2019 1 Q&amp;A Q: What did the

Sequence to Sequence Models Matt Gormley Lecture 5 Sep. 11, 2019 1 Q&amp;A Q: What did the

10-418 / 10-618 Machine Learning for Structured Data Machine Learning Department School of Computer Science Carnegie Mellon University Sequence to Sequence Models Matt Gormley Lecture 5 Sep. 11, 2019 1 Q&amp;A Q: What did the results of the

505 views • 46 slides
Transformer Sequence Models and Sequence Applications (Machine Translation, Speech Recognition)

Transformer Sequence Models and Sequence Applications (Machine Translation, Speech Recognition)

Transformer Sequence Models and Sequence Applications (Machine Translation, Speech Recognition) CSE392 - Spring 2019 Special Topic in CS Most NLP Tasks. E.g. Transformer Networks Sequence Tasks Transformers Language

1.13k views • 100 slides
Autoregressive Models Overview Direct Structures P Direct structures x ( n ) = a k x

Autoregressive Models Overview Direct Structures P Direct structures x ( n ) = a k x

Autoregressive Models Overview Direct Structures P Direct structures x ( n ) = a k x ( n k ) + w ( n ) Types of estimators k =1 Parametric spectral estimation Notation differs (again) from text Parametric

510 views • 13 slides
CS7015 (Deep Learning) : Lecture 22 Autoregressive Models (NADE, MADE) Mitesh M. Khapra

CS7015 (Deep Learning) : Lecture 22 Autoregressive Models (NADE, MADE) Mitesh M. Khapra

CS7015 (Deep Learning) : Lecture 22 Autoregressive Models (NADE, MADE) Mitesh M. Khapra Department of Computer Science and Engineering Indian Institute of Technology Madras 1/24 Mitesh M. Khapra CS7015 (Deep Learning) : Lecture 22 Module

772 views • 24 slides
ArDec: Autoregressive-based time series decomposition in R Susana Barbosa Universidade do Porto,

ArDec: Autoregressive-based time series decomposition in R Susana Barbosa Universidade do Porto,

Introduction Method Application Summary ArDec: Autoregressive-based time series decomposition in R Susana Barbosa Universidade do Porto, Portugal : Introduction Method Application Summary Time series decomposition Approaches:

350 views • 20 slides
Adaptive Control Chapter 7: Digital Control Strategies 1 Adaptive Control Landau,Lozano,

Adaptive Control Chapter 7: Digital Control Strategies 1 Adaptive Control Landau,Lozano,

Adaptive Control Chapter 7: Digital Control Strategies 1 Adaptive Control Landau,Lozano, MSaad, Karimi Chapter 7: Digital Control Strategies 2 Adaptive Control Landau,Lozano, MSaad, Karimi The R-S-T Digital Controller r(t)

811 views • 55 slides
Learning unknown forces in nonlinear models with Gaussian processes and autoregressive flows Wil

Learning unknown forces in nonlinear models with Gaussian processes and autoregressive flows Wil

Learning unknown forces in nonlinear models with Gaussian processes and autoregressive flows Wil O C Ward w.ward@sheffield.ac.uk Department of Physics and Astronomy, The University of Sheffield GPSS Workshop: Structurally Constrained Gaussian

923 views • 89 slides
Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,

Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,

Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, ukasz Kaiser, Illia Polosukhin From: Google brain Google research Presented by: Hsuan-Yu Chen RNN Advantages:

2.59k views • 20 slides
Innovation in Pediatric Healthcare Delivery Utah Regional Healthcare Innovation Day April 27,

Innovation in Pediatric Healthcare Delivery Utah Regional Healthcare Innovation Day April 27,

Innovation in Pediatric Healthcare Delivery Utah Regional Healthcare Innovation Day April 27, 2016 Comprehensive Care for Children with Complex Medical Conditions N Murphy MD E Clark MD Department of Pediatrics University of Utah School

345 views • 12 slides
Lecture 6 Firms and Markets in the Performing Arts Nonprofits and For-Profits Professor

Lecture 6 Firms and Markets in the Performing Arts Nonprofits and For-Profits Professor

Lecture 6 Firms and Markets in the Performing Arts Nonprofits and For-Profits Professor Julia Lowell lowell@econ.ucsb.edu Spring 2012 4/ 18/ 2012 Econ 191ac -- Lecture 6 1 Outline: Lecture 6 Go over HW5 (due today) Key

761 views • 49 slides

More recommend