monge blunts bayes hardness results for adversarial
play

Monge blunts Bayes: Hardness Results for Adversarial Training Zac - PowerPoint PPT Presentation

Jun 26, 2023 •34 likes •204 views

Monge blunts Bayes: Hardness Results for Adversarial Training Zac Cranko Aditya Krishna Menon Richard Nock Cheng Soon Ong Zhan Shi Christian Walder Overview Hardness results on adversarial training. Key result applicable to a learner:

  1. Monge blunts Bayes: Hardness Results for Adversarial Training Zac Cranko Aditya Krishna Menon Richard Nock Cheng Soon Ong Zhan Shi Christian Walder

  2. Overview • Hardness results on adversarial training. Key result applicable to a learner: • optimising any loss satisfying a mild statistical requirement, and • learning a classifier from any class satisfying a mild continuity assumption • Implementation disentangles adversarial training: 1. generate adversarial data ( Key result solves the compression of an OT plan) 2. training as usual • Toy experiments against “weakly activated” adversarial data 
 reveal generalisation improves on clean data as well data shot blunts — Cranko et al. 2

  3. <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> <latexit sha1_base64="QlLrGMtZRYzFl65kQE7hHmVXwm4=">AB8HicbVA9TwJBEJ3DL8Qv1NLmIphYkTsaLYk2lpjIh4EL2Vv2YMPu3mV3zoRc+BU2Fhpj68+x89+4wBUKvmSl/dmMjMvTAQ36HnfTmFjc2t7p7hb2ts/ODwqH5+0TZxqylo0FrHuhsQwRVrIUfBuolmRIaCdcLJ7dzvPDFteKwecJqwQJKR4hGnBK30WO2bNDQMq4Nyxat5C7jrxM9JBXI0B+Wv/jCmqWQKqSDG9HwvwSAjGjkVbFbqp4YlhE7IiPUsVUQyE2SLg2fuhVWGbhRrWwrdhfp7IiPSmKkMbackODar3lz8z+ulGF0HGVdJikzR5aIoFS7G7vx7d8g1oyimlhCqub3VpWOiCUWbUcmG4K+vE7a9Zrv1fz7eqVxk8dRhDM4h0vw4QoacAdNaAEFCc/wCm+Odl6cd+dj2Vpw8plT+APn8wdIEZAM</latexit> Four levels of technical results Paper (main body) Paper (appendix) These slides Poster ⊂ ⊂ ⊂ 3 blunts — Cranko et al.

Recommend

Monge blunts Bayes: Hardness Results for Adversarial Training Zac Cranko Aditya Krishna Menon

Monge blunts Bayes: Hardness Results for Adversarial Training Zac Cranko Aditya Krishna Menon

Monge blunts Bayes: Hardness Results for Adversarial Training Zac Cranko Aditya Krishna Menon Richard Nock Cheng Soon Ong Zhan Shi Christian Walder Overview Hardness results on adversarial training. Key result applicable to a learner:

492 views • 15 slides
Markov Chains Toolbox Search: uninformed/heuristic Adversarial search Probability

Markov Chains Toolbox Search: uninformed/heuristic Adversarial search Probability

Markov Chains Toolbox Search: uninformed/heuristic Adversarial search Probability Bayes nets Naive Bayes classifiers Reasoning over time In a Bayes net, each random variable (node) takes on one specific value. Good for

408 views • 37 slides
Mechanical Properties of Paint Coatings Hardness Measurement Shore Hardness D Barcol Hardness

Mechanical Properties of Paint Coatings Hardness Measurement Shore Hardness D Barcol Hardness

Mechanical Properties of Paint Coatings Hardness Measurement Shore Hardness D Barcol Hardness ASTM D2583 Fiberglass Aluminum Aluminum Alloys Soft Metals Plastics Scratch Hardness- (IS 101 - Part 5/Sec 2) BS 3900 Hardness can

1.89k views • 30 slides
Viterbi Training for PCFGs: Hardness Results and Competitiveness of Uniform Initialization Shay

Viterbi Training for PCFGs: Hardness Results and Competitiveness of Uniform Initialization Shay

Viterbi Training for PCFGs: Hardness Results and Competitiveness of Uniform Initialization Shay Cohen Noah Smith Carnegie Mellon University July 14, 2010 Outline Hardness results for unsupervised learning of PCFGs Background and problem

602 views • 46 slides
New hardness results for graph and hypergraph colorings Joshua Brakensiek , Venkatesan Guruswami

New hardness results for graph and hypergraph colorings Joshua Brakensiek , Venkatesan Guruswami

New hardness results for graph and hypergraph colorings Joshua Brakensiek , Venkatesan Guruswami Carnegie Mellon University CCC 2016 Joshua Brakensiek, Venkatesan Guruswami New hardness results for graph and hypergraph colorings Graph Coloring

1.09k views • 83 slides
Bayes Theorem Thomas Bayes (1701-1761) Simple form of Bayes Theorem, for

Bayes Theorem Thomas Bayes (1701-1761) Simple form of Bayes Theorem, for

Nave Bayes Classifica/on Debapriyo Majumdar Data Mining Fall 2014 Indian Statistical Institute Kolkata August 14, 2014 Bayes Theorem Thomas Bayes (1701-1761) Simple form of Bayes Theorem, for two

1.04k views • 20 slides
Semi-Lagrangian Methods for Monge-Amp` ere Equations Max Jensen University of Sussex joint

Semi-Lagrangian Methods for Monge-Amp` ere Equations Max Jensen University of Sussex joint

Semi-Lagrangian Methods for Monge-Amp` ere Equations Max Jensen University of Sussex joint work with Xiaobing Feng (University of Tennessee) http://arxiv.org/abs/1602.04758 Slides available at https://goo.gl/v42Zv8 1 Part 1 Monge-Amp` ere

773 views • 48 slides
Beyond NP [HMU06,Chp.11a] Tautology Problem NP-Hardness and co-NP Historical

Beyond NP [HMU06,Chp.11a] Tautology Problem NP-Hardness and co-NP Historical

Beyond NP [HMU06,Chp.11a] Tautology Problem NP-Hardness and co-NP Historical Comments Optimization Problems More Complexity Classes 1 Tautology Problem &amp; NP-Hardness &amp; co-NP 2 NP-Hardness Another

559 views • 23 slides
New Hardness Results for Diophantine Approximation Friedrich Eisenbrand &amp; Thomas Rothvo

New Hardness Results for Diophantine Approximation Friedrich Eisenbrand &amp; Thomas Rothvo

New Hardness Results for Diophantine Approximation Friedrich Eisenbrand &amp; Thomas Rothvo Institute of Mathematics EPFL, Lausanne APPROX09 Simultaneous Diophantine Approximation (SDA) Given: 1 , . . . , n Q bound N

875 views • 59 slides
Competency generates results Universal testing machines Static und portable hardness

Competency generates results Universal testing machines Static und portable hardness

Competency generates results Universal testing machines Static und portable hardness testers Component and furniture testing systems Special testing machines and fully automated solutions Dimension measurement equipment

267 views • 13 slides
Random generation of deterministic automata Fr ed erique Bassino Institut Gaspard-Monge

Random generation of deterministic automata Fr ed erique Bassino Institut Gaspard-Monge

Introduction Random generation Experimental results and Open problems Random generation of deterministic automata Fr ed erique Bassino Institut Gaspard-Monge Universit e de Marne-la-Vall ee Joint work with Cyril Nicaud Fr ed

724 views • 30 slides
Deep Adversarial Learning for NLP 9:00 10:30 Introduction and Adversarial Training, GANs

Deep Adversarial Learning for NLP 9:00 10:30 Introduction and Adversarial Training, GANs

Deep Adversarial Learning for NLP 9:00 10:30 Introduction and Adversarial Training, GANs William Wang 10:30 11:00 Break - 11:00 12:15 Adversarial Examples Sameer Singh 12:15 12:30 Conclusions and Question Answering William

1.75k views • 105 slides
Subsection 2 NP -hardness 36 / 109 NP -Hardness Do hard problems exist? Depends on P = NP

Subsection 2 NP -hardness 36 / 109 NP -Hardness Do hard problems exist? Depends on P = NP

Subsection 2 NP -hardness 36 / 109 NP -Hardness Do hard problems exist? Depends on P = NP Next best thing: define hardest problem in NP A problem P is NP -hard if Every problem Q in NP can be solved in this way: 1. given an instance

609 views • 14 slides
1 Bayes Nets: Assumptions Independence in a BN Assumptions we are required to make to define

1 Bayes Nets: Assumptions Independence in a BN Assumptions we are required to make to define

1 2 Recap: Bayes Nets CS 473: Artificial Intelligence Bayes Nets: Independence A Bayes net is an efficient encoding of a probabilistic model of a domain Questions we can ask: Inference: given a fixed BN, what is P(X | e)?

197 views • 5 slides
PDEs from Monge-Kantorovich Mass Transportation Theory Luca Petrelli Math &amp; Computer Science

PDEs from Monge-Kantorovich Mass Transportation Theory Luca Petrelli Math &amp; Computer Science

PDEs from Monge-Kantorovich Mass Transportation Theory Luca Petrelli Math &amp; Computer Science Dept. Mount St Marys University Outline Monge-Kantorovich mass transportation problem Gradient Flow formalism Time-step

990 views • 27 slides
Adversarial Examples and Adversarial Training Ian Goodfellow, Sta ff Research Scientist, Google

Adversarial Examples and Adversarial Training Ian Goodfellow, Sta ff Research Scientist, Google

Adversarial Examples and Adversarial Training Ian Goodfellow, Sta ff Research Scientist, Google Brain CS 231n, Stanford University, 2017-05-30 Overview What are adversarial examples? Why do they happen? How can they be used to

866 views • 43 slides
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training Xi Wu

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training Xi Wu

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training Xi Wu xiwu@cs.wisc.edu Joint work with Uyeong Jang, Jiefeng Chen, Lingjiao Chen, and Somesh Jha July 19, 2018 Xi Wu Model Confidence and Adversarial

740 views • 9 slides
Short Range Operator Contributions to 0 decay from LQCD Henry Monge-Camacho 1 , 2 1 College

Short Range Operator Contributions to 0 decay from LQCD Henry Monge-Camacho 1 , 2 1 College

Short Range Operator Contributions to 0 decay from LQCD Henry Monge-Camacho 1 , 2 1 College of William &amp; and Mary 2 LBNL 36th International Symposium on Lattice Field Theory July 24, 2018 Henry Monge-Camacho (W&amp;M, LBNL) July 24,

565 views • 19 slides
Surprising Negative Results for Generative Adversarial Tree Search Kamyar Azizzadenesheli 1,2,5 ,

Surprising Negative Results for Generative Adversarial Tree Search Kamyar Azizzadenesheli 1,2,5 ,

Surprising Negative Results for Generative Adversarial Tree Search Kamyar Azizzadenesheli 1,2,5 , Brandon Yang 2 , Weitang Liu 3 , Emma Brunskill 2 , Zachary C Lipton 4 , Animashree Anandkumar 5 1 UC Irvine, 2 Stanford University, 3 UC Davis, 4

317 views • 10 slides
Bayes Classifiers Nave Bayes Classification Patrick Mair Bayes Classifiers Weather data

Bayes Classifiers Nave Bayes Classification Patrick Mair Bayes Classifiers Weather data

Bayes Classifiers Nave Bayes Classification Patrick Mair Bayes Classifiers Weather data set Predictors: Outlook, Temperature, Humidity, Windy. Response: Play (yes/no) Starting point Cross classification: Predictor/Response

98 views • 6 slides
New Hardness Results for the Permanent Using Linear Optics Daniel Grier Luke Schae ff er MIT

New Hardness Results for the Permanent Using Linear Optics Daniel Grier Luke Schae ff er MIT

New Hardness Results for the Permanent Using Linear Optics Daniel Grier Luke Schae ff er MIT Permanent Review Permanent: Given matrix Example: &lt;latexit

488 views • 19 slides
Nave Bayes in a Nutshell Bayes rule: Assuming conditional independence among X i s: So,

Nave Bayes in a Nutshell Bayes rule: Assuming conditional independence among X i s: So,

Machine Learning 10-701 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 25, 2011 Today: Readings: Nave Bayes Required: discrete-valued X i s Mitchell: Nave Bayes and Document

624 views • 15 slides
? AdVersarial: Defeating Perceptual Ad Blocking with Adversarial Examples Florian Tramr

? AdVersarial: Defeating Perceptual Ad Blocking with Adversarial Examples Florian Tramr

Todays Story: How I got my first Death Threat ? AdVersarial: Defeating Perceptual Ad Blocking with Adversarial Examples Florian Tramr October 8 th 2019 Joint work with Pascal Dupr, Gili Rusak, Giancarlo Pellegrino and Dan Boneh The

415 views • 29 slides
DATA MINING: NAVE BAYES 1 Nave Bayes Classifier Thomas Bayes 1702 - 1761 We will start off

DATA MINING: NAVE BAYES 1 Nave Bayes Classifier Thomas Bayes 1702 - 1761 We will start off

DATA MINING: NAVE BAYES 1 Nave Bayes Classifier Thomas Bayes 1702 - 1761 We will start off with some mathematical background. But first we start with some visual intuition. 2 Grasshoppers Katydids 10 9 8 7 Antenna Length 6 5 4

1.29k views • 61 slides

More recommend