sample efficient optimization in the latent space of deep
play

Sample-Efficient Optimization in the Latent Space of Deep Generative - PowerPoint PPT Presentation

Jun 24, 2023 •236 likes •292 views

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining Erik Daxberger*, Austin Tripp* & Jos e Miguel Hern andez-Lobato RealML @ ICML2020 2020-07-18 Problem Optimization of expensive,

  1. Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining Erik Daxberger*, Austin Tripp* & Jos´ e Miguel Hern´ andez-Lobato RealML @ ICML2020 2020-07-18

  2. Problem Optimization of expensive, black box functions on structured input spaces. Examples: Drug Design Materials Discovery Neural Architecture Search Erik Daxberger*, Austin Tripp* & Jos´ e Miguel Hern´ andez-Lobato Keyword: Weighted Retraining 2/4

  3. Latent Space Optimization Optimize in the latent space Z of a deep generative model (instead of data space X ) X X X X Z Z Z Z Normal With weighted retraining Erik Daxberger*, Austin Tripp* & Jos´ e Miguel Hern´ andez-Lobato Keyword: Weighted Retraining 3/4

  4. Results Chemical Design Task weight; retrain 20 weight; no retrain no weight; retrain works with a variety of models no weight; no retrain 15 original easy to implement 10 huge increase in performance and sample efficiency 5 0 0 100 200 300 400 500 Number of objective function evaluations More results in the paper! Erik Daxberger*, Austin Tripp* & Jos´ e Miguel Hern´ andez-Lobato Keyword: Weighted Retraining 4/4

Recommend

Empirical Analysis of Latent Space Embedding David Mount and Eunhui Park Department of Computer

Empirical Analysis of Latent Space Embedding David Mount and Eunhui Park Department of Computer

Latent Space Embeddings Optimization Exploration Tool Empirical Analysis of Latent Space Embedding David Mount and Eunhui Park Department of Computer Science University of Maryland, College Park MURI Meeting June 3, 2011 Latent Space

204 views • 17 slides
Graph Sketching, Sampling, Streaming, and Space Efficient Optimization (Part II) Sudipto Guha

Graph Sketching, Sampling, Streaming, and Space Efficient Optimization (Part II) Sudipto Guha

Graph Sketching, Sampling, Streaming, and Space Efficient Optimization (Part II) Sudipto Guha and Andrew McGregor Space Efficient Optimization for Graphs Impact of Dimensionality Reduction, Embeddings, L p L q , etc. Thesis : Graph

2.3k views • 198 slides
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model CS330

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model CS330

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model CS330 Student Presentation Table of Contents Motivation & problem Method overview Experiments Takeaways Discussion (strengths

653 views • 24 slides
Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses Guan-Hua Huang,

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses Guan-Hua Huang,

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses Guan-Hua Huang, Su-Mei Wang and Chung-Chu Hsu 12/17/2010 Breast cancer data van't Veer et al . Nature 2002 The 78 sporadic lymph-node-negative breast cancer

358 views • 24 slides
Space: Space: System Architecture System Architecture vs Optimization vs Optimization

Space: Space: System Architecture System Architecture vs Optimization vs Optimization

Space: Space: System Architecture System Architecture vs Optimization vs Optimization Vladimir Grebenyuk Raytheon Canada Limited Space: The Space: The First Steps First Steps October 4,1957 April 12, 1961 July 20,

819 views • 52 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
Efficient Model Evaluation in the Search-Based Approach to Latent Structure Discovery Tao Chen,

Efficient Model Evaluation in the Search-Based Approach to Latent Structure Discovery Tao Chen,

Efficient Model Evaluation in the Search-Based Approach to Latent Structure Discovery Tao Chen, Nevin L. Zhang and Yi Wang Department of Computer Science & Engineering The Hong Kong University of Science & Technology 1 Latent Tree

595 views • 25 slides
and Hash Coding with Deep Neural Networks Presenter: MinKu Kang 1 Why did I choose this paper?

and Hash Coding with Deep Neural Networks Presenter: MinKu Kang 1 Why did I choose this paper?

Simultaneous Feature Learning and Hash Coding with Deep Neural Networks Presenter: MinKu Kang 1 Why did I choose this paper? Efficient image retrieval via binary encoding of images - efficient bitwise operations. - space-efficient

606 views • 36 slides
Relational Deep Learning: A Deep Latent Variable Model for Link Prediction Hao Wang, Xingjian

Relational Deep Learning: A Deep Latent Variable Model for Link Prediction Hao Wang, Xingjian

Relational Deep Learning: A Deep Latent Variable Model for Link Prediction Hao Wang, Xingjian Shi, Dit-Yan Yeung Motivation Bayesian Deep Learning Relational Deep Learning Parameter Learning Experiments Conclusion

577 views • 40 slides
Space-efficient quantum Space-efficient quantum automata automata Andris Ambainis Nikolay

Space-efficient quantum Space-efficient quantum automata automata Andris Ambainis Nikolay

Space-efficient quantum Space-efficient quantum automata automata Andris Ambainis Nikolay Nahimov Department of Computer Science University of Latvia TQC 2008 Quantum Finite Automata Mathematical model for quantum computers with

616 views • 29 slides
An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source

An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source

An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation Nicholas J. Bryan, Stanford University Gautham J. Mysore, Adobe Research ICML 2013 Sound Check 1 Motivation I Real world

465 views • 21 slides
review and more examples Sample space: S = set of all potential outcomes of experiment E.g., flip

review and more examples Sample space: S = set of all potential outcomes of experiment E.g., flip

review and more examples Sample space: S = set of all potential outcomes of experiment E.g., flip two coins: S = {(H,H), (H,T), (T,H), (T,T)} Events: E S is an arbitrary subset of the sample space 1 head in 2 flips: E = {(H,H), (H,T),

493 views • 20 slides
Probability Definitions Probability Space : ordered triple ( , F , P ). ( Sample Space )

Probability Definitions Probability Space : ordered triple ( , F , P ). ( Sample Space )

Probability Definitions Probability Space : ordered triple ( , F , P ). ( Sample Space ) is a set (possible out- comes); elements are called elementary outcomes. F is a family of subsets ( events ) of with the property that F

296 views • 6 slides
Null Space Gradient Flows for Constrained Optimization with Applications to Shape Optimization

Null Space Gradient Flows for Constrained Optimization with Applications to Shape Optimization

Null Space Gradient Flows for Constrained Optimization with Applications to Shape Optimization Florian Feppon Gr egoire Allaire, Charles Dapogny Julien Cortial, Felipe Bordeu SIAM CSE Spokane February 26, 2019 Shape optimization

693 views • 42 slides
Primary reference: Casella-Berger 2 nd Edition Presentation 1-1-1: The Sample Space and Events

Primary reference: Casella-Berger 2 nd Edition Presentation 1-1-1: The Sample Space and Events

Presentation 1-1-1: The Sample Space and Events Primary reference: Casella-Berger 2 nd Edition Presentation 1-1-1: The Sample Space and Events (the sample space) And in it were (of an experiment) 2 Presentation 1-1-1: The Sample Space

333 views • 11 slides
Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan

Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan

Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan Zelener Machine Learning Reading Group January 7 th 2016 The Graduate Center, CUNY Objectives Explain some of the trends of deep learning and

680 views • 44 slides
Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference

Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference

Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference Engine Song Han CVA group, Stanford University Apr 7, 2016 1 Intro about me and my advisor Fourth year PhD with Prof. Bill Dally at Stanford.

2.06k views • 70 slides
Design Space Optimization of Embedded Memory Design Space Optimization of Embedded Memory Systems

Design Space Optimization of Embedded Memory Design Space Optimization of Embedded Memory Systems

Design Space Optimization of Embedded Memory Design Space Optimization of Embedded Memory Systems via Data Remapping Systems via Data Remapping Krishna V. Palem, Rodric M. Rabbah, Vincent J. Mooney III, Pinar Korkmaz and Kiran Puttaswamy Center

775 views • 38 slides
Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference

Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference

Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference Engine Song Han CVA group, Stanford University Jan 6, 2015 A few words about us Fourth year PhD with Prof. Bill Dally at Stanford.

966 views • 54 slides
Consistent and Efficient Reconstruction of Latent Tree Models Myung Jin Choi Joint work with

Consistent and Efficient Reconstruction of Latent Tree Models Myung Jin Choi Joint work with

Stochastic Systems Group Consistent and Efficient Reconstruction of Latent Tree Models Myung Jin Choi Joint work with Vincent Tan, Anima Anandkumar, and Alan S. Willsky Laboratory for Information and Decision Systems Massachusetts Institute

704 views • 52 slides
MLE/MAP With Latent Variables CMSC 691 UMBC Outline Constrained Optimization Distributions of

MLE/MAP With Latent Variables CMSC 691 UMBC Outline Constrained Optimization Distributions of

Examples: MLE/MAP With Latent Variables CMSC 691 UMBC Outline Constrained Optimization Distributions of distributions Example 1: A Model of Rolling a Die Example 2: A Model of Conditional Die Rolls Lagrange multipliers Assume an original

1.05k views • 49 slides
Review A probability space ( , P ) consists of a sample space and a probability measure P A

Review A probability space ( , P ) consists of a sample space and a probability measure P A

Review A probability space ( , P ) consists of a sample space and a probability measure P A set of outcomes A is called an event P should obey three axioms: 1. P ( A ) 0 for all events A 2. P () = 1 3. P ( A B ) = P ( A ) +

1.23k views • 58 slides
Optimization for Training Deep Models presented by Kan Ren Table of Contents Optimization

Optimization for Training Deep Models presented by Kan Ren Table of Contents Optimization

Optimization for Training Deep Models presented by Kan Ren Table of Contents Optimization for machine learning models Challenges of optimizing neural networks Optimizations algorithms initializations adapting the learning

865 views • 62 slides
Redshift-space distortion analysis from the DR14 eBOSS quasar sample in Fourier space Hctor

Redshift-space distortion analysis from the DR14 eBOSS quasar sample in Fourier space Hctor

Redshift-space distortion analysis from the DR14 eBOSS quasar sample in Fourier space Hctor Gil-Marn (Institute Lagrange de Paris Fellow, LPNHE Sorbonne University) Statistical challenges for large-scale structure in the era of LSST Oxford,

401 views • 28 slides

More recommend