Learning Perceptual Inference by Contrasting - PowerPoint PPT Presentation

Oct 07, 2023 •92 likes •221 views

Learning Perceptual Inference by Contrasting http://wellyzhang.github.io/project/copinet.html Chi Zhang * , Baoxiong Jia * , Feng Gao, Yixin Zhu, Hongjing Lu, Song-Chun Zhu (* indicates equal contribution) Poster: 10:45 AM - 12:45 PM @ East

Learning Perceptual Inference by Contrasting http://wellyzhang.github.io/project/copinet.html Chi Zhang * , Baoxiong Jia * , Feng Gao, Yixin Zhu, Hongjing Lu, Song-Chun Zhu (* indicates equal contribution) Poster: 10:45 AM - 12:45 PM @ East Exhibition Hall B + C #193
Motivation • Raven’s Progressive Matrices is a representative task on “thinking in pictures”, or spatial-temporal reasoning. • Study on contrast effects on cognitive science, biology, and computer science. • Interplay between perception and inference detailed in Carpenter et al. for humans to solve RPM. • Missing permutation-invariance in computational models.
<latexit sha1_base64="xfTUHI5U2L7jMfNiFlDpA2XWeNU=">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</latexit> CoPINet: Formulation A ranking perspective: 8 a 0 2 A , a 0 6 = a ? p ( a ? |O ) � p ( a 0 |O ) ,

Recommend

Learning Perceptual Causality from Video Amy Fire and Song-Chun Zhu Center for Vision,

Learning Perceptual Causality from Video Amy Fire and Song-Chun Zhu Center for Vision, Cognition, Learning, and Art UCLA 1 Ideally: Learn Causality from Raw Video 2 Inference Using Learned Causal Structure c) STC-Parsing a) Input: Video

441 views • 24 slides

Learning of perceptual models of retailproducts using photo-realistic simulations of

Learning of perceptual models of retailproducts using photo-realistic simulations of supermarkets Ferenc B alint-Bencz edi IAI, University of Bremen IROS, 5 th October 2018 About our group Backgounrd building complete cognitive

592 views • 23 slides

Perceptual Asymmetries in Learning Vowel Nasalization Kim Strtjen, Ruben van de Vijver, Dinah

Perceptual Asymmetries in Learning Vowel Nasalization Kim Strtjen, Ruben van de Vijver, Dinah Baer-Henney OCP13 Budapest, 15.01.2016 Agenda 1 Learning biases 2 Vowel nasalization 3 Experiment 4 Results 5 Discussion 6 Conclusion 7 References

785 views • 39 slides

Computer Vision by Learning: Motion in Action Jan van Gemert, UvA 2 Motion and perceptual

Computer Vision by Learning: Motion in Action Jan van Gemert, UvA 2 Motion and perceptual organization Even impoverished motion data can evoke a strong percept 3 Motion and perceptual organization Even impoverished motion

594 views • 44 slides

Variational Inference and Learning Michael Gutmann Probabilistic Modelling and Reasoning

Variational Inference and Learning Michael Gutmann Probabilistic Modelling and Reasoning (INFR11134) School of Informatics, University of Edinburgh Spring semester 2018 Recap Learning and inference often involves intractable integrals

786 views • 36 slides

CS440/ECE448 Lecture 15: Bayesian Inference and Bayesian Learning Slides by Svetlana Lazebnik,

CS440/ECE448 Lecture 15: Bayesian Inference and Bayesian Learning Slides by Svetlana Lazebnik, 10/2016 Modified by Mark Hasegawa-Johnson, 3/2019 Bayesian Inference and Bayesian Learning Bayes Rule Bayesian Inference Misdiagnosis

829 views • 38 slides

AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning Florian Tramr November

AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning Florian Tramr November 14 th 2019 Joint work with Pascal Dupr, Gili Rusak, Giancarlo Pellegrino and Dan Boneh The Future of Ad-Blocking easylist.txt markup

612 views • 23 slides

Perceptual Ad-Blocking: Meet Adversarial Machine Learning Florian Tramr Palo Alto Networks

Perceptual Ad-Blocking: Meet Adversarial Machine Learning Florian Tramr Palo Alto Networks February 22nd 2019 Joint work with Pascal Dupr, Gili Rusak, Giancarlo Pellegrino and Dan Boneh The Future of Ad-Blocking? easylist.txt

315 views • 28 slides

CS480/680 Machine Learning Lecture 11: February 11 th , 2020 Variational Inference Zahra

CS480/680 Machine Learning Lecture 11: February 11 th , 2020 Variational Inference Zahra Sheikhbahaee VARIATIONAL ALGORITHMS FOR APPROXIMATE BAYESIAN INFERENCE (Beal 2003, chapter 2) Variational Inference: A Review for Statisticians (Blei et al.

713 views • 28 slides

Machine Learning: Foundations Lecturer: Yishay Mansour Lecture 2 Bayesian Inference Kfir Bar

Machine Learning: Foundations Lecturer: Yishay Mansour Lecture 2 Bayesian Inference Kfir Bar Yaniv Bar Marcelo Bacher Based on notes by Shahar Yifrah, Keren Yizhak, Hadas Zur (2012) Bayesian Inference Bayesian inference is a method of

600 views • 37 slides

Graphical Models Graphical Models MAP inference Siamak Ravanbakhsh Winter 2018 Learning

Graphical Models Graphical Models MAP inference Siamak Ravanbakhsh Winter 2018 Learning objectives Learning objectives MAP inference and its complexity exact & approximate MAP inference max-product and max-sum message passing

979 views • 62 slides

Meta-Amoruized Variational Inference and Learning Kristy Choi CS236: December 4th, 2019

Meta-Amoruized Variational Inference and Learning Kristy Choi CS236: December 4th, 2019 Probabilistic Inference Probabilistic inference is a paruicular way of viewing the world: + = Observations Updated (posterior) Prior belief Typically

452 views • 28 slides

Variational Inference CMSC 691 UMBC Goal: Posterior Inference Hyperparameters Unknown

Approximate Inference: Variational Inference CMSC 691 UMBC Goal: Posterior Inference Hyperparameters Unknown parameters Data: p ( | ) Likelihood model: p( | ) (Some) Learning Techniques MAP/MLE: Point

1.09k views • 81 slides

Statistical Natural Language Processing Statistical models: learning, inference, estimation,

Statistical Natural Language Processing Statistical models: learning, inference, estimation, prediction ar ltekin University of Tbingen Seminar fr Sprachwissenschaft Summer Semester 2017 Statistical models: learning, inference,

497 views • 25 slides

Learning Perceptual Kernels for Visualization Design a atay Demiralp Michael Bernstein

Learning Perceptual Kernels for Visualization Design a atay Demiralp Michael Bernstein Jeffrey Heer Stanford University Stanford University University of Washington Interactive Data Lab @ UW 15 11.25 7.5 3.75 0 13 9.75 6.5 3.25

1.06k views • 92 slides

Learning Perceptual Shape Style Similarity Zhaoliang Lun 1 Evangelos Kalogerakis 1 Alla Sheffer 2 1

Slide 1 Elements of Style: Learning Perceptual Shape Style Similarity Zhaoliang Lun 1 Evangelos Kalogerakis 1 Alla Sheffer 2 1 University of Massachusetts Amherst 2 University of British Columbia Hi, I am Evangelos Kalogerakis, and together with

862 views • 51 slides

Inference and Learning for Probabilistic Logic Programming Fabrizio Riguzzi Dipartimento di

Inference and Learning for Probabilistic Logic Programming Fabrizio Riguzzi Dipartimento di Matematica e Informatica Universit di Ferrara F. Riguzzi (DMI) Inference and learning for PLP 1 / 25 Outline Probabilistic Logic Languages 1

405 views • 25 slides

Model inference . Course of Machine Learning Master Degree in Computer Science University of

Model inference . Course of Machine Learning Master Degree in Computer Science University of Rome ``Tor Vergata'' Giorgio Gambosi a.a. 2018-2019 1 Model inference Purpose the data domain. Dataset distributed (iid): they can be seen as

760 views • 36 slides

Probabilistic Graphical Models Probabilistic Graphical Models MAP inference Siamak Ravanbakhsh

Probabilistic Graphical Models Probabilistic Graphical Models MAP inference Siamak Ravanbakhsh Fall 2019 Learning objectives Learning objectives MAP inference and its complexity exact & approximate MAP inference max-product and max-sum

957 views • 74 slides

Learning and Inference in Markov Logic Networks CS 486/686 University of Waterloo Lecture 23:

Learning and Inference in Markov Logic Networks CS 486/686 University of Waterloo Lecture 23: November 27, 2012 Outline Markov Logic Networks Parameter learning Lifted inference 2 CS486/686 Lecture Slides (c) 2012 P. Poupart 1

223 views • 10 slides

Neural Variational Inference and Learning Andriy Mnih, Karol Gregor 22 June 2014 1 / 14

Neural Variational Inference and Learning Andriy Mnih, Karol Gregor 22 June 2014 1 / 14 Introduction Training directed latent variable models is difficult because inference in them is intractable. Both MCMC and traditional variational

322 views • 14 slides

Low-dose CT Enhancement Network with a Perceptual Loss Function in the Spatial Frequency and Image

Low-dose CT Enhancement Network with a Perceptual Loss Function in the Spatial Frequency and Image Domains Medical Imaging with Deep Learning 2020 Kevin J. Chung, 1,2 Roberto Souza, 3,4 Richard Frayne, 3,4 Ting-Yim Lee 1,2 1 Department of Medical

227 views • 5 slides

Interpreting WBR in terms of middle ear mechanics and contrasting Tympanometry with WBR Robert

Interpreting WBR in terms of middle ear mechanics and contrasting Tympanometry with WBR Robert Withnell Ph.D. Department of Speech & Hearing Sciences Indiana University, IN. Patricia S. Jeng Ph.D Mimosa Acoustics Champaign, IL. Pierre

386 views • 19 slides

Transatlantic Intercreditor Agreements: Comparing, Contrasting and Reconciling U.S. and European

Presenting a live 90-minute webinar with interactive Q&A Transatlantic Intercreditor Agreements: Comparing, Contrasting and Reconciling U.S. and European Approaches Navigating Enforcement, Payment Obligations, Releases of Collateral,

668 views • 46 slides