• Home

  • Documents

  • Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural...
10
Building a Long-Range AI/ML Vision Experiments Compilations Evaluations Processing Validation Applications Supervised Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization

Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

  • Upload
    others

  • View
    2

  • Download
    0

Embed Size (px)

Citation preview

Page 1: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

Building a Long-Range AI/ML Vision

ExperimentsCompilations

EvaluationsProcessing Validation Applications

Supervised Learning

Generative Modeling

Reinforcement Learning

Neural Networks

Gaussian Processes

Deep Q Learning

Bayesian Optimization

Page 2: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

Supervised Learning

Generative Modeling

Reinforcement Learning

Neural Networks

Gaussian Processes

Deep Q Learning

Bayesian Optimization

https://commons.wikimedia.org/wiki/File:Main-qimg-48d5bd214e53d440fa32fc9e5300c894.png

Perform Action

Obtain Reward

Update Policy Model

Assess Current

State

https://pathmind.com/images/wiki/gan_schema.png

Lovell

252Cf(sf)A=108

Page 3: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

Supervised Learning

Generative Modeling

Reinforcement Learning

Neural Networks

Gaussian Processes

Deep Q Learning

Bayesian Optimization

https://commons.wikimedia.org/wiki/File:Main-qimg-48d5bd214e53d440fa32fc9e5300c894.png

Perform Action

Obtain Reward

Update Policy Model

Assess Current

State

arxiv 1703.10593, 2017

Lovell

252Cf(sf)A=108

Page 4: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

ExperimentsCompilations

EvaluationsProcessing Validation Applications

Needed Groundwork• What common community tools are

needed?• Modernizing, documenting, and

open sourcing tools• Improving ease of access• TALYS is a great example.

• Cleaning up experimental data bases• EXFOR• Adding metadata

Pitfalls to be avoided• Need to enforce reproducibility

through peer review• ML models represented and

distributed in a standard format.

• Want to augment missing physics• Favor better physics models over more

complex ML.

Page 5: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

ExperimentsCompilations

EvaluationsProcessing Validation Applications

• Mitigating human error in compilation?

• Identifying and quantifying missing systematic errors• Can we “learn” how to correct them?

• Using ML to prioritize new measurements

• “Validating” old data

Future• Batch effect mitigation or removal tools to be used by AI/ML• Such tools / algorithms could be AI/ML methods• Developed such algorithms for material science and bio-medical domain

• A fully automated NLP pipeline with reviewer user interface

ExperimentsCompilations

EvaluationsProcessing Validation Applications

• NLP can not be 100% accuracy and requires human validation• Intuitive user interface is required

for expert validation• Automation can significantly reduce

manual data extraction burden• Table and Figure extraction from PDF

Yoo

Page 6: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

ExperimentsCompilations

EvaluationsProcessing Validation Applications

• Emulation of complex and expensive model.• Learning model defects• Correcting them?

• How can we enhance evaluations with more fundamental but less precise models?• Can we “learn” the intuition behind

past evaluations.• Codification of senior evaluator intuition.

• Can we apply these ideas/tools to structure evaluations.

Original

Emulated

N. Schunk

Page 7: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

ExperimentsCompilations

EvaluationsProcessing Validation Applications

• How can we gauge the correctness of evaluations and models?• Does “correctness” have context?• What about where there is no data?

• Very unstable systems • r-process

• Can we optimize new experiments to maximize new information gained?• Can we automatic the consistency

checking between models and measure data?

What has been doneRandom forests were used successfully to augment expert knowledge in pin-pointing errors in nuclear data and benchmark experiments leading to bias in simulating criticality benchmarks; E.g.: ML found 19F(n,inl) issue missed by experts

ExperimentsCompilations

EvaluationsProcessing Validation Applications

Incident Energy (MeV)

Cro

ss S

ectio

n (b

arns

)

10.5 2

1

0.5

2

5

1

0.5

2

5

10.5 2

ENDF/B-VIII.0JENDL-4.0Broder 1969

19F(n,inl)

Neudecker

Page 8: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

ExperimentsCompilations

EvaluationsProcessing Validation Applications

• Connect the (unexpectedly) important features of a reaction to particular application.

• Building application model surrogates for uncertainty propagation.

• How do we fill in gaps of missing information needed by applications

Hutchinson

66Zn (n, 2n)

Page 9: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

Discussion Time!

ExperimentsCompilations

EvaluationsProcessing Validation Applications

Supervised Learning

Generative Modeling

Reinforcement Learning

Neural Networks

Gaussian Processes

Deep Q Learning

Bayesian Optimization

Page 10: Building a Long-Range AI/ML Vision...Learning Generative Modeling Reinforcement Learning Neural Networks Gaussian Processes Deep Q Learning Bayesian Optimization •Howtoaddresshaving

ExperimentsCompilations

EvaluationsProcessing Validation Applications

Supervised Learning

Generative Modeling

Reinforcement Learning

Neural Networks

Gaussian Processes

Deep Q Learning

Bayesian Optimization

• How to address having very little data.• What is really needed to train a ML model.

• Virtues of expt. data only vs including model data.• Cautions when physics is unknown.• Caution when fitting GP (collapsing length scales).


Learning Representations and Generative Models for 3D Point …proceedings.mlr.press/v80/achlioptas18a/achlioptas18a.pdf · 2019-02-08 · Learning Representations and Generative

Learning Representations and Generative Models for 3D Point …proceedings.mlr.press/v80/achlioptas18a/achlioptas18a.pdf · 2019-02-08 · Learning Representations and Generative

Documents
Engineeri… · Web viewIt covers generative models, Bayesian learning methods, decision theory, Gaussian models, Bayesian statistics, frequentist statistics, regression models, generalized

Engineeri… · Web viewIt covers generative models, Bayesian learning methods, decision theory, Gaussian models, Bayesian statistics, frequentist statistics, regression models, generalized

Documents
THE INFLUENCE OF APPLICATION GENERATIVE LEARNING …

THE INFLUENCE OF APPLICATION GENERATIVE LEARNING …

Documents
Deep Learning Feature Learning Representation Learning Generative Learning

Deep Learning Feature Learning Representation Learning Generative Learning

Documents
Learning from Demonstration with Gaussian Processes

Learning from Demonstration with Gaussian Processes

Documents
Relational Learning with Gaussian Processes

Relational Learning with Gaussian Processes

Documents
Generative Technology for Literacy Learning

Generative Technology for Literacy Learning

Documents
Learning Generative Models of 3D Structures

Learning Generative Models of 3D Structures

Documents
Semi Supervised Learning with Generative Adversarial

Semi Supervised Learning with Generative Adversarial

Documents
Semi-Supervised Learning with Generative Adversarial Networks › thesis › centraal › files › f1945916913.pdf · Semi-Supervised Learning with Generative Adversarial Networks

Semi-Supervised Learning with Generative Adversarial Networks › thesis › centraal › files › f1945916913.pdf · Semi-Supervised Learning with Generative Adversarial Networks

Documents
Learning Generative Models across Incomparable Spaces

Learning Generative Models across Incomparable Spaces

Documents
Learning Deep Generative Models of Graphs ...yujiali/files/talks/Learning_deep_graph... · Learning Deep Generative Models of Graphs — Yujia Li Generative Models of Graphs Stochastic

Learning Deep Generative Models of Graphs ...yujiali/files/talks/Learning_deep_graph... · Learning Deep Generative Models of Graphs — Yujia Li Generative Models of Graphs Stochastic

Documents
Learning the generative principles of a symbol system from ... · Statistical learning Deep learning Generative learning Symbol systems Education ABSTRACT The processes and mechanisms

Learning the generative principles of a symbol system from ... · Statistical learning Deep learning Generative learning Symbol systems Education ABSTRACT The processes and mechanisms

Documents
Gaussian processes for machine learning

Gaussian processes for machine learning

Education
Generative Adversarial Imitation Learning

Generative Adversarial Imitation Learning

Documents
Generative chemistry: drug discovery with deep learning … · 2020. 8. 21. · 1 Generative chemistry: drug discovery with deep learning generative models Yuemin Bian1,2 and Xiang-Qun

Generative chemistry: drug discovery with deep learning … · 2020. 8. 21. · 1 Generative chemistry: drug discovery with deep learning generative models Yuemin Bian1,2 and Xiang-Qun

Documents
Effectiveness of a Generative Learning-Based Biology ... · The Generative Learning-based biology module was the application of the Generative Learning model integrated in the modul

Effectiveness of a Generative Learning-Based Biology ... · The Generative Learning-based biology module was the application of the Generative Learning model integrated in the modul

Documents
Generative Hierarchical Learning of Sparse FRAME …openaccess.thecvf.com/content_cvpr_2017/papers/Xie... · Generative Hierarchical Learning of Sparse FRAME Models Jianwen Xie1,

Generative Hierarchical Learning of Sparse FRAME …openaccess.thecvf.com/content_cvpr_2017/papers/Xie... · Generative Hierarchical Learning of Sparse FRAME Models Jianwen Xie1,

Documents
Dynamic Learning Gaussian Process Bandits€¦ · Diogo Dubart Norinho (UCL) December 10, 2012 8 / 44. Dynamic Learning Gaussian Process Bandits Gaussian Process (GP) Example of a

Dynamic Learning Gaussian Process Bandits€¦ · Diogo Dubart Norinho (UCL) December 10, 2012 8 / 44. Dynamic Learning Gaussian Process Bandits Gaussian Process (GP) Example of a

Documents
Variational Bayesian learning of generative models · 70 Variational Bayesian learning of generative models 3.1 Bayesian modeling and variational learning Unsupervised learning methods

Variational Bayesian learning of generative models · 70 Variational Bayesian learning of generative models 3.1 Bayesian modeling and variational learning Unsupervised learning methods

Documents
GENERATIVE ADVERSARIAL SELF-IMITATION LEARNING

GENERATIVE ADVERSARIAL SELF-IMITATION LEARNING

Documents
Generative and discriminative classification techniquesSummary generative classification methods (Semi-) Parametric models, e.g. p(x|y) is Gaussian, or mixture of … Pros: no need

Generative and discriminative classification techniquesSummary generative classification methods (Semi-) Parametric models, e.g. p(x|y) is Gaussian, or mixture of … Pros: no need

Documents
Generative Learning of Reading Comprehension

Generative Learning of Reading Comprehension

Documents
Unsupervised Learning – Generative Models (PixelRNNs, PixelCNNs) – Generative ... · 2019. 3. 28. · Generative Adversarial Networks Ian Goodfellow et al., “Generative Adversarial

Unsupervised Learning – Generative Models (PixelRNNs, PixelCNNs) – Generative ... · 2019. 3. 28. · Generative Adversarial Networks Ian Goodfellow et al., “Generative Adversarial

Documents
Generative probabilistic models for multimedia retrieval ... · This paper1 presents the use of generative probabilistic models for multimedia retrieval. We estimate Gaussian mixture

Generative probabilistic models for multimedia retrieval ... · This paper1 presents the use of generative probabilistic models for multimedia retrieval. We estimate Gaussian mixture

Documents
PENGARUH PENERAPAN MODEL GENERATIVE LEARNING

PENGARUH PENERAPAN MODEL GENERATIVE LEARNING

Documents
Learning Generative Models of Sentences and Images Generative Models of Sentences and Images Learning Generative Models of Sentences and Images Richard Zemel July 8, 2015 Microsoft

Learning Generative Models of Sentences and Images Generative Models of Sentences and Images Learning Generative Models of Sentences and Images Richard Zemel July 8, 2015 Microsoft

Documents
Generative - dsba.korea.ac.krdsba.korea.ac.kr/wp/wp-content/seminar/Paper Review/VanillaGan... · •Generative models can be incorporated into reinforcement learning •Generative

Generative - dsba.korea.ac.krdsba.korea.ac.kr/wp/wp-content/seminar/Paper Review/VanillaGan... · •Generative models can be incorporated into reinforcement learning •Generative

Documents
STATISTICAL LEARNING: GENERATIVE MODEL FOR …

STATISTICAL LEARNING: GENERATIVE MODEL FOR …

Documents
Introduction to Generative Models - CSE · PDF fileIntroduction to Generative Models Piyush Rai Machine Learning (CS771A) Sept 23, 2016 Machine Learning (CS771A) Introduction to Generative

Introduction to Generative Models - CSE · PDF fileIntroduction to Generative Models Piyush Rai Machine Learning (CS771A) Sept 23, 2016 Machine Learning (CS771A) Introduction to Generative

Documents