conditional tsne
play

Conditional tSNE Bo Kang Jefrey Lijffijt Tijl De Bie Ghent - PowerPoint PPT Presentation

Dec 02, 2022 •210 likes •556 views

Conditional tSNE Bo Kang Jefrey Lijffijt Tijl De Bie Ghent University Daro Garca Garca Facebook Ral Santos-Rodrguez University of Bristol Motivation High dim data embeddings often have confounding factors. Factors: known,

  1. Conditional tSNE Bo Kang Jefrey Lijffijt Tijl De Bie Ghent University Darío García García Facebook Raúl Santos-Rodríguez University of Bristol

  2. Motivation • High dim data embeddings often have confounding factors. • Factors: known, unknown but dominant, sensitive.

  3. Motivation UCI Adult dataset

  4. Motivation • High dim data embeddings often has confounding factors • Factors that are known, unknown but dominant, sensitive. • Find embedding with confounding factors explained away?

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

TRANSFERRING DIFFUSION BASED MANIFOLD C Diffusion Maps Embryoid Bodies tSNE PHATE PCA

TRANSFERRING DIFFUSION BASED MANIFOLD C Diffusion Maps Embryoid Bodies tSNE PHATE PCA

Myeloid Cells Endothelial-Myeloid Progenitors PHATE 2 Genes tem Cell Endothelium Blastocysts Muscle Precursors Vascular muscle cells Cells PHATE 1 B Diffusion Maps Articifical Tree tSNE PHATE PCA TRANSFERRING DIFFUSION BASED

1.01k views • 37 slides
15. The Conditional 15.1 The conditional: Formation and uses 15.2 Mise en pratique 15.1 The

15. The Conditional 15.1 The conditional: Formation and uses 15.2 Mise en pratique 15.1 The

15. The Conditional 15.1 The conditional: Formation and uses 15.2 Mise en pratique 15.1 The Conditional: Formation and Uses Mme Legendre is talking to a friend about her hopes and wishes for the future: Un jour, jaimerais changer de

191 views • 15 slides
P( ) 1 conditional probability where P(F) > 0 Conditional probability of E given F:

P( ) 1 conditional probability where P(F) > 0 Conditional probability of E given F:

Conditional Probability P( ) 1 conditional probability where P(F) > 0 Conditional probability of E given F: probability that E occurs given that F has occurred. Ehf F FE Conditioning on F Written as P(E|F) Ser Means P(E,

731 views • 35 slides
Conditional Heteroscedasticity (CH) So far, our models are for the conditional mean . For

Conditional Heteroscedasticity (CH) So far, our models are for the conditional mean . For

Conditional Heteroscedasticity (CH) So far, our models are for the conditional mean . For instance, the Gaussian AR(1) model + t y t = y t 1 may be written: , 2 Conditionally on y t 1 , y

411 views • 19 slides
Markov random fields 2. conditional specifications 3. conditional auto-regression Rasmus

Markov random fields 2. conditional specifications 3. conditional auto-regression Rasmus

Outline: 1. specification of joint distributions Markov random fields 2. conditional specifications 3. conditional auto-regression Rasmus Waagepetersen 4. Brooks factorization, 5. conditional independence and graphs 6. Hammersley-Clifford

202 views • 9 slides
Conditional Probability & Independence Conditional Probabilities Question : How should we

Conditional Probability & Independence Conditional Probabilities Question : How should we

Conditional Probability & Independence Conditional Probabilities Question : How should we modify P ( E ) if we learn that event F has occurred? Definition : the conditional probability of E given F is P ( E | F ) = P ( E \ F ) , for P

593 views • 24 slides
Conditional Probability & Independence Conditional Probabilities Question : How should we

Conditional Probability & Independence Conditional Probabilities Question : How should we

Conditional Probability & Independence Conditional Probabilities Question : How should we modify P ( E ) if we learn that event F has occurred? Definition : the conditional probability of E given F is P ( E | F ) = P ( E F ) , for

523 views • 26 slides
Blended Conditional Gradients: The unconditioning of conditional gradients Joint work with Gabor

Blended Conditional Gradients: The unconditioning of conditional gradients Joint work with Gabor

Blended Conditional Gradients: The unconditioning of conditional gradients Joint work with Gabor Braun, Sebastian Pokutta, Steve Wright Dan Tu 1/9 CONDITIONAL GRADIENTS: PROJECTION-FREE Given a polytope ! , solve the optimization problem: min

109 views • 9 slides
Multiscale Conditional 1) Generalization of conditional random fields (CRF) to multiscale

Multiscale Conditional 1) Generalization of conditional random fields (CRF) to multiscale

Contributions Multiscale Conditional 1) Generalization of conditional random fields (CRF) to multiscale conditional Random Fields for Image random fields (mCRF) Labeling 2) Learning features of the random field at multiple scales Xuming He,

371 views • 8 slides
Conditional Probability & Conditional Expectations Compute Probabilities by Conditioning IE

Conditional Probability & Conditional Expectations Compute Probabilities by Conditioning IE

Conditional Probability & Conditional Expectations Compute Probabilities by Conditioning IE 502: Probabilistic Models Jayendran Venkateswaran IE & OR Gamblers Chances A gambler has Rs.300 in his pocket when he walks into a

554 views • 9 slides
P( ) 1 2 coin flipping Exampls Suppose you flip two coins & all outcomes are equally

P( ) 1 2 coin flipping Exampls Suppose you flip two coins & all outcomes are equally

10/10/16 conditional probability Conditional probability of E given F: probability that E occurs given that F has occurred. Conditional Probability Conditioning on F S Written as P(E|F) E F Means P(E, given F observed) Sample

407 views • 5 slides
Review: Conditional Probability Conditional Probability The conditional probability of event

Review: Conditional Probability Conditional Probability The conditional probability of event

Review: Conditional Probability Conditional Probability The conditional probability of event given event is = Pr(E ) Pr Pr() Well defined only if Pr Sofya Raskhodnikova; Randomness in

190 views • 5 slides
Conditional Statements Python Conditional Statements Sometimes a statement (or a block of

Conditional Statements Python Conditional Statements Sometimes a statement (or a block of

Conditional Statements Python Conditional Statements Sometimes a statement (or a block of statements) should only be executed if a condition is true. Conditional execution is implemented with the if- statement Form of the

150 views • 14 slides
Conditional Random Fields [Hanna M. Wallach, Conditional Random Fields: An Introduction,

Conditional Random Fields [Hanna M. Wallach, Conditional Random Fields: An Introduction,

Conditional Random Fields [Hanna M. Wallach, Conditional Random Fields: An Introduction, Technical Report MS-CIS- 04-21, University of Pensylvania, 2004 . ] CS 486/686 University of Waterloo Lecture 19: March 13, 2012 Outline Conditional

242 views • 6 slides
11/15/16 Conditional distributions Let X and Y be discrete r.v.s. Conditional probability mass

11/15/16 Conditional distributions Let X and Y be discrete r.v.s. Conditional probability mass

11/15/16 Conditional distributions Let X and Y be discrete r.v.s. Conditional probability mass function of X given that Y=y p X | Y ( x | y ) = Pr ( X = x | Y = y ) Conditional distributions, conditional expectation = Pr ( X = x, Y = y ) = p (

513 views • 3 slides
Party on! A new, conditional variable importance A new, conditional importance measure for

Party on! A new, conditional variable importance A new, conditional importance measure for

Measuring variable Party on! A new, conditional variable importance A new, conditional importance measure for random forests importance Conclusion available in party References Carolin Strobl (LMU M unchen) and Achim Zeileis (WU Wien)

639 views • 21 slides
A fluctuating boundary integral method for Brownian suspensions Yuanxun Bill Bao 1 Manas Rachh 2

A fluctuating boundary integral method for Brownian suspensions Yuanxun Bill Bao 1 Manas Rachh 2

A fluctuating boundary integral method for Brownian suspensions Yuanxun Bill Bao 1 Manas Rachh 2 Eric Keaveny 3 Leslie Greengard 1 Aleksandar Donev 1 1 Courant Institute, NYU 2 Yale University 3 Imperial College London Complex Creeping Fluids:

560 views • 22 slides
Ellie Dodge, PhD Angelina Maia, PhD, RD University of New England, Program R.I.T,

Ellie Dodge, PhD Angelina Maia, PhD, RD University of New England, Program R.I.T,

Ellie Dodge, PhD Angelina Maia, PhD, RD University of New England, Program R.I.T, Nutrition Lecturer, Wegmans Director, MS Applied Nutrition School of Health and Nutrition Participants will engage in a conversation about

415 views • 23 slides
Stochastic Deep Networks Gwendoline De Bie, Gabriel Peyr, Marco Cuturi Deep Architectures on

Stochastic Deep Networks Gwendoline De Bie, Gabriel Peyr, Marco Cuturi Deep Architectures on

Stochastic Deep Networks Gwendoline De Bie, Gabriel Peyr, Marco Cuturi Deep Architectures on Density Inputs t=1, 1000 cells t=2, 650 cells t=3, 890 cells of varying physical attributes Representing inputs as densities (discretized in practice)

303 views • 7 slides
2 Dirac-Dunkl operator and a higher rank Bannai-Ito algebra Vincent X. Genest Department of

2 Dirac-Dunkl operator and a higher rank Bannai-Ito algebra Vincent X. Genest Department of

The Z n 2 Dirac-Dunkl operator and a higher rank Bannai-Ito algebra Vincent X. Genest Department of Mathematics Massachusetts Institute of Technology Collaborators: H. De Bie (Ghent) L. Vinet (Montreal)

425 views • 27 slides
Gaussian Processes for Active Sensor Management Alexander N. Dolia , University of Southampton

Gaussian Processes for Active Sensor Management Alexander N. Dolia , University of Southampton

OED for KRR, and the MVCE Gaussian Processes for Active Sensor Management Alexander N. Dolia , University of Southampton This poster is based on A.N.Dolia, C.J.Harris, J.Shawe-Taylor, D.M.Titterington, Kernel Ellipsoidal Trimming , submitted

160 views • 11 slides
Numb3rs 11 2 10 3 GCD 9 4 8 5 7 6 The Skippy Clock 0 12 1 Has 13 hours on its dial!

Numb3rs 11 2 10 3 GCD 9 4 8 5 7 6 The Skippy Clock 0 12 1 Has 13 hours on its dial!

0 12 1 Numb3rs 11 2 10 3 GCD 9 4 8 5 7 6 The Skippy Clock 0 12 1 Has 13 hours on its dial! 11 2 Needle moves two hours at a time 10 3 Which all numbers will the needle 9 4 reach? 8 5 7 6 Reaches all of them!

420 views • 12 slides
March 8, 2019 Agenda GILTI-related Consolidated Issues FDII-related Issues

March 8, 2019 Agenda GILTI-related Consolidated Issues FDII-related Issues

Impact of TCJA on Corporations, Part II: Section 951A (GILTI), Section 250 (FDII), Section 59A (BEAT), and Section 965 (Transition Tax) Moderator Scott Levine, Jones Day Panelists Lawrence Axelrod, Morgan Lewis & Bockius LLP Bryan

1.08k views • 94 slides
Passenger-oriented railway disposition timetables in case of severe disruptions Yousef Maknoon

Passenger-oriented railway disposition timetables in case of severe disruptions Yousef Maknoon

Passenger-oriented railway disposition timetables in case of severe disruptions Yousef Maknoon Michel Bierlaire Stefan Binder STRC 2015, April 17th Outline Motivation Problem description Research question Assumptions Formal problem

371 views • 33 slides

More recommend