SlideShare una empresa de Scribd logo

On Calibration of Modern Neural Networks (2017)

•
•
Terry Taewoong Um
Terry Taewoong Um

The document discusses calibration of modern neural networks. It notes that the softmax output of a network does not necessarily represent confidence. It examines ways to measure miscalibration in networks, including factors that can cause miscalibration like overfitting. Methods to calibrate networks are presented, such as temperature scaling. The findings of comparing these calibration techniques to related works are summarized.

Ingeniería
1 de 14
Descargar para leer sin conexión
Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry Taewoong Um ON CALIBRATION OF MODERN NE URAL NETWORKS 1
TODAY’S PAPER Terry Taewoong Um (terry.t.um@gmail.com) https://vimeo.com/238242536
GOAL Terry Taewoong Um (terry.t.um@gmail.com) Softmax output (ෝ𝒑) != Confidence (ෝ𝒒)
MEASURING MISCALIBRATION Terry Taewoong Um (terry.t.um@gmail.com)
MEASURING MISCALIBRATION Terry Taewoong Um (terry.t.um@gmail.com)
MEASURING MISCALIBRATION Terry Taewoong Um (terry.t.um@gmail.com) What factors cause misclalibration?
WHAT CAUSES MISCALIBRATION? Terry Taewoong Um (terry.t.um@gmail.com)
OVERFITTING Terry Taewoong Um (terry.t.um@gmail.com)
CALIBRATION Temperature scaling
COMPARISON TO RELATED WORKS Terry Taewoong Um (terry.t.um@gmail.com) Histogram Binning Plot Scaling Bayesian Binning into Quantiles (BBQ)
FINDINGS & CONCLUSION Terry Taewoong Um (terry.t.um@gmail.com)
MY THOUGHTS Terry Taewoong Um (terry.t.um@gmail.com)
TODAY’S PAPER Terry Taewoong Um (terry.t.um@gmail.com) https://vimeo.com/238242536

Recomendados

Revisiting the Calibration of Modern Neural Networks
Revisiting the Calibration of Modern Neural NetworksRevisiting the Calibration of Modern Neural Networks
Revisiting the Calibration of Modern Neural NetworksSungchul Kim
 
Deep Reinforcement Learning from Human Preferences
Deep Reinforcement Learning from Human PreferencesDeep Reinforcement Learning from Human Preferences
Deep Reinforcement Learning from Human Preferencestaeseon ryu
 
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper reviewtaeseon ryu
 
007 20151214 Deep Unsupervised Learning using Nonequlibrium Thermodynamics
007 20151214 Deep Unsupervised Learning using Nonequlibrium Thermodynamics007 20151214 Deep Unsupervised Learning using Nonequlibrium Thermodynamics
007 20151214 Deep Unsupervised Learning using Nonequlibrium ThermodynamicsHa Phuong
 
Generative Adversarial Network (+Laplacian Pyramid GAN)
Generative Adversarial Network (+Laplacian Pyramid GAN)Generative Adversarial Network (+Laplacian Pyramid GAN)
Generative Adversarial Network (+Laplacian Pyramid GAN)NamHyuk Ahn
 
leewayhertz.com-Reinforcement Learning from Human Feedback RLHF.pdf
leewayhertz.com-Reinforcement Learning from Human Feedback RLHF.pdfleewayhertz.com-Reinforcement Learning from Human Feedback RLHF.pdf
leewayhertz.com-Reinforcement Learning from Human Feedback RLHF.pdfKristiLBurns
 
PR-409: Denoising Diffusion Probabilistic Models
PR-409: Denoising Diffusion Probabilistic ModelsPR-409: Denoising Diffusion Probabilistic Models
PR-409: Denoising Diffusion Probabilistic ModelsHyeongmin Lee
 
오토인코더의 모든 것
오토인코더의 모든 것오토인코더의 모든 것
오토인코더의 모든 것NAVER Engineering
 

Recomendados

Revisiting the Calibration of Modern Neural Networks
Revisiting the Calibration of Modern Neural NetworksRevisiting the Calibration of Modern Neural Networks
Revisiting the Calibration of Modern Neural NetworksSungchul Kim
 
Deep Reinforcement Learning from Human Preferences
Deep Reinforcement Learning from Human PreferencesDeep Reinforcement Learning from Human Preferences
Deep Reinforcement Learning from Human Preferencestaeseon ryu
 
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper reviewtaeseon ryu
 
007 20151214 Deep Unsupervised Learning using Nonequlibrium Thermodynamics
007 20151214 Deep Unsupervised Learning using Nonequlibrium Thermodynamics007 20151214 Deep Unsupervised Learning using Nonequlibrium Thermodynamics
007 20151214 Deep Unsupervised Learning using Nonequlibrium ThermodynamicsHa Phuong
 
Generative Adversarial Network (+Laplacian Pyramid GAN)
Generative Adversarial Network (+Laplacian Pyramid GAN)Generative Adversarial Network (+Laplacian Pyramid GAN)
Generative Adversarial Network (+Laplacian Pyramid GAN)NamHyuk Ahn
 
leewayhertz.com-Reinforcement Learning from Human Feedback RLHF.pdf
leewayhertz.com-Reinforcement Learning from Human Feedback RLHF.pdfleewayhertz.com-Reinforcement Learning from Human Feedback RLHF.pdf
leewayhertz.com-Reinforcement Learning from Human Feedback RLHF.pdfKristiLBurns
 
PR-409: Denoising Diffusion Probabilistic Models
PR-409: Denoising Diffusion Probabilistic ModelsPR-409: Denoising Diffusion Probabilistic Models
PR-409: Denoising Diffusion Probabilistic ModelsHyeongmin Lee
 
오토인코더의 모든 것
오토인코더의 모든 것오토인코더의 모든 것
오토인코더의 모든 것NAVER Engineering
 
修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」
修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」
修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」Naoki Hayashi
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks남주 김
 
Prml6
Prml6Prml6
Prml6Arata Honda
 
Applying Deep Learning with Weak and Noisy labels
Applying Deep Learning with Weak and Noisy labelsApplying Deep Learning with Weak and Noisy labels
Applying Deep Learning with Weak and Noisy labelsDarian Frajberg
 
Tensor Decomposition and its Applications
Tensor Decomposition and its ApplicationsTensor Decomposition and its Applications
Tensor Decomposition and its ApplicationsKeisuke OTAKI
 
OpenOpt の線形計画で圧縮センシング
OpenOpt の線形計画で圧縮センシングOpenOpt の線形計画で圧縮センシング
OpenOpt の線形計画で圧縮センシングToshihiro Kamishima
 
GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and ApplicationsHoang Nguyen
 
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)Deep Learning JP
 
[DL輪読会]Hindsight Experience Replay
[DL輪読会]Hindsight Experience Replay[DL輪読会]Hindsight Experience Replay
[DL輪読会]Hindsight Experience ReplayDeep Learning JP
 
Bayesian Model-Agnostic Meta-Learning
Bayesian Model-Agnostic Meta-LearningBayesian Model-Agnostic Meta-Learning
Bayesian Model-Agnostic Meta-LearningSangwoo Mo
 
Differential privacy and applications to location privacy
Differential privacy and applications to location privacyDifferential privacy and applications to location privacy
Differential privacy and applications to location privacyPôle Systematic Paris-Region
 
Active learning lecture
Active learning lectureActive learning lecture
Active learning lectureazuring
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksDing Li
 
Connie zweig & steve wolf o jogo das sombras (doc)(rev)
Connie zweig & steve wolf o jogo das sombras (doc)(rev)Connie zweig & steve wolf o jogo das sombras (doc)(rev)
Connie zweig & steve wolf o jogo das sombras (doc)(rev)Saphira Trilena
 
Noisy Labels と戦う深層学習
Noisy Labels と戦う深層学習Noisy Labels と戦う深層学習
Noisy Labels と戦う深層学習Plot Hong
 
Automatic Mixed Precision の紹介
Automatic Mixed Precision の紹介Automatic Mixed Precision の紹介
Automatic Mixed Precision の紹介Kuninobu SaSaki
 
[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning
[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning
[DL輪読会]Recent Advances in Autoencoder-Based Representation LearningDeep Learning JP
 
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介Takeshi Mikami
 
Differentiable Ray Sampling for Neural 3D Representation
Differentiable Ray Sampling for Neural 3D RepresentationDifferentiable Ray Sampling for Neural 3D Representation
Differentiable Ray Sampling for Neural 3D RepresentationPreferred Networks
 
How good is your prediction a gentle introduction to conformal prediction.
How good is your prediction a gentle introduction to conformal prediction.How good is your prediction a gentle introduction to conformal prediction.
How good is your prediction a gentle introduction to conformal prediction.Deep Learning Italia
 
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전Terry Taewoong Um
 
A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)Terry Taewoong Um
 

Más contenido relacionado

La actualidad más candente

修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」
修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」
修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」Naoki Hayashi
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks남주 김
 
Applying Deep Learning with Weak and Noisy labels
Applying Deep Learning with Weak and Noisy labelsApplying Deep Learning with Weak and Noisy labels
Applying Deep Learning with Weak and Noisy labelsDarian Frajberg
 
Tensor Decomposition and its Applications
Tensor Decomposition and its ApplicationsTensor Decomposition and its Applications
Tensor Decomposition and its ApplicationsKeisuke OTAKI
 
OpenOpt の線形計画で圧縮センシング
OpenOpt の線形計画で圧縮センシングOpenOpt の線形計画で圧縮センシング
OpenOpt の線形計画で圧縮センシングToshihiro Kamishima
 
GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and ApplicationsHoang Nguyen
 
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)Deep Learning JP
 
[DL輪読会]Hindsight Experience Replay
[DL輪読会]Hindsight Experience Replay[DL輪読会]Hindsight Experience Replay
[DL輪読会]Hindsight Experience ReplayDeep Learning JP
 
Bayesian Model-Agnostic Meta-Learning
Bayesian Model-Agnostic Meta-LearningBayesian Model-Agnostic Meta-Learning
Bayesian Model-Agnostic Meta-LearningSangwoo Mo
 
Differential privacy and applications to location privacy
Differential privacy and applications to location privacyDifferential privacy and applications to location privacy
Differential privacy and applications to location privacyPôle Systematic Paris-Region
 
Active learning lecture
Active learning lectureActive learning lecture
Active learning lectureazuring
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksDing Li
 
Connie zweig & steve wolf o jogo das sombras (doc)(rev)
Connie zweig & steve wolf o jogo das sombras (doc)(rev)Connie zweig & steve wolf o jogo das sombras (doc)(rev)
Connie zweig & steve wolf o jogo das sombras (doc)(rev)Saphira Trilena
 
Noisy Labels と戦う深層学習
Noisy Labels と戦う深層学習Noisy Labels と戦う深層学習
Noisy Labels と戦う深層学習Plot Hong
 
Automatic Mixed Precision の紹介
Automatic Mixed Precision の紹介Automatic Mixed Precision の紹介
Automatic Mixed Precision の紹介Kuninobu SaSaki
 
[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning
[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning
[DL輪読会]Recent Advances in Autoencoder-Based Representation LearningDeep Learning JP
 
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介Takeshi Mikami
 
Differentiable Ray Sampling for Neural 3D Representation
Differentiable Ray Sampling for Neural 3D RepresentationDifferentiable Ray Sampling for Neural 3D Representation
Differentiable Ray Sampling for Neural 3D RepresentationPreferred Networks
 
How good is your prediction a gentle introduction to conformal prediction.
How good is your prediction a gentle introduction to conformal prediction.How good is your prediction a gentle introduction to conformal prediction.
How good is your prediction a gentle introduction to conformal prediction.Deep Learning Italia
 

La actualidad más candente (20)

修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」
修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」
修士論文発表：「非負値行列分解における漸近的Bayes汎化誤差」
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
Prml6
Prml6Prml6
Prml6
 
Applying Deep Learning with Weak and Noisy labels
Applying Deep Learning with Weak and Noisy labelsApplying Deep Learning with Weak and Noisy labels
Applying Deep Learning with Weak and Noisy labels
 
Tensor Decomposition and its Applications
Tensor Decomposition and its ApplicationsTensor Decomposition and its Applications
Tensor Decomposition and its Applications
 
OpenOpt の線形計画で圧縮センシング
OpenOpt の線形計画で圧縮センシングOpenOpt の線形計画で圧縮センシング
OpenOpt の線形計画で圧縮センシング
 
GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and Applications
 
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)
【DL輪読会】Egocentric Video Task Translation (CVPR 2023 Highlight)
 
[DL輪読会]Hindsight Experience Replay
[DL輪読会]Hindsight Experience Replay[DL輪読会]Hindsight Experience Replay
[DL輪読会]Hindsight Experience Replay
 
Bayesian Model-Agnostic Meta-Learning
Bayesian Model-Agnostic Meta-LearningBayesian Model-Agnostic Meta-Learning
Bayesian Model-Agnostic Meta-Learning
 
Differential privacy and applications to location privacy
Differential privacy and applications to location privacyDifferential privacy and applications to location privacy
Differential privacy and applications to location privacy
 
Active learning lecture
Active learning lectureActive learning lecture
Active learning lecture
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
Connie zweig & steve wolf o jogo das sombras (doc)(rev)
Connie zweig & steve wolf o jogo das sombras (doc)(rev)Connie zweig & steve wolf o jogo das sombras (doc)(rev)
Connie zweig & steve wolf o jogo das sombras (doc)(rev)
 
Noisy Labels と戦う深層学習
Noisy Labels と戦う深層学習Noisy Labels と戦う深層学習
Noisy Labels と戦う深層学習
 
Automatic Mixed Precision の紹介
Automatic Mixed Precision の紹介Automatic Mixed Precision の紹介
Automatic Mixed Precision の紹介
 
[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning
[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning
[DL輪読会]Recent Advances in Autoencoder-Based Representation Learning
 
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介
K meansによるクラスタリングの解説と具体的なクラスタリングの活用方法の紹介
 
Differentiable Ray Sampling for Neural 3D Representation
Differentiable Ray Sampling for Neural 3D RepresentationDifferentiable Ray Sampling for Neural 3D Representation
Differentiable Ray Sampling for Neural 3D Representation
 
How good is your prediction a gentle introduction to conformal prediction.
How good is your prediction a gentle introduction to conformal prediction.How good is your prediction a gentle introduction to conformal prediction.
How good is your prediction a gentle introduction to conformal prediction.
 

Más de Terry Taewoong Um

#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전Terry Taewoong Um
 
A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)Terry Taewoong Um
 
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...Terry Taewoong Um
 
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)Terry Taewoong Um
 
Deep learning (Machine learning) tutorial for beginners
Deep learning (Machine learning) tutorial for beginnersDeep learning (Machine learning) tutorial for beginners
Deep learning (Machine learning) tutorial for beginnersTerry Taewoong Um
 
Deep Variational Bayes Filters (2017)
Deep Variational Bayes Filters (2017)Deep Variational Bayes Filters (2017)
Deep Variational Bayes Filters (2017)Terry Taewoong Um
 
Deep Learning: A Critical Appraisal (2018)
Deep Learning: A Critical Appraisal (2018)Deep Learning: A Critical Appraisal (2018)
Deep Learning: A Critical Appraisal (2018)Terry Taewoong Um
 
Understanding Black-box Predictions via Influence Functions (2017)
Understanding Black-box Predictions via Influence Functions (2017)Understanding Black-box Predictions via Influence Functions (2017)
Understanding Black-box Predictions via Influence Functions (2017)Terry Taewoong Um
 
Learning with side information through modality hallucination (2016)
Learning with side information through modality hallucination (2016)Learning with side information through modality hallucination (2016)
Learning with side information through modality hallucination (2016)Terry Taewoong Um
 
Human Motion Forecasting (Generation) with RNNs
Human Motion Forecasting (Generation) with RNNsHuman Motion Forecasting (Generation) with RNNs
Human Motion Forecasting (Generation) with RNNsTerry Taewoong Um
 
Deformable Convolutional Network (2017)
Deformable Convolutional Network (2017)Deformable Convolutional Network (2017)
Deformable Convolutional Network (2017)Terry Taewoong Um
 
Introduction to Deep Learning with TensorFlow
Introduction to Deep Learning with TensorFlowIntroduction to Deep Learning with TensorFlow
Introduction to Deep Learning with TensorFlowTerry Taewoong Um
 
About Two Motion Planning Papers
About Two Motion Planning PapersAbout Two Motion Planning Papers
About Two Motion Planning PapersTerry Taewoong Um
 
기계학습(Machine learning) 입문하기
기계학습(Machine learning) 입문하기기계학습(Machine learning) 입문하기
기계학습(Machine learning) 입문하기Terry Taewoong Um
 
Lie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsLie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsTerry Taewoong Um
 
로봇과 인공지능, 그리고 미래의 노동
로봇과 인공지능, 그리고 미래의 노동로봇과 인공지능, 그리고 미래의 노동
로봇과 인공지능, 그리고 미래의 노동Terry Taewoong Um
 
Introduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep LearningIntroduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep LearningTerry Taewoong Um
 
Lie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsLie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsTerry Taewoong Um
 

Más de Terry Taewoong Um (18)

#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
 
A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)
 
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
 
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
 
Deep learning (Machine learning) tutorial for beginners
Deep learning (Machine learning) tutorial for beginnersDeep learning (Machine learning) tutorial for beginners
Deep learning (Machine learning) tutorial for beginners
 
Deep Variational Bayes Filters (2017)
Deep Variational Bayes Filters (2017)Deep Variational Bayes Filters (2017)
Deep Variational Bayes Filters (2017)
 
Deep Learning: A Critical Appraisal (2018)
Deep Learning: A Critical Appraisal (2018)Deep Learning: A Critical Appraisal (2018)
Deep Learning: A Critical Appraisal (2018)
 
Understanding Black-box Predictions via Influence Functions (2017)
Understanding Black-box Predictions via Influence Functions (2017)Understanding Black-box Predictions via Influence Functions (2017)
Understanding Black-box Predictions via Influence Functions (2017)
 
Learning with side information through modality hallucination (2016)
Learning with side information through modality hallucination (2016)Learning with side information through modality hallucination (2016)
Learning with side information through modality hallucination (2016)
 
Human Motion Forecasting (Generation) with RNNs
Human Motion Forecasting (Generation) with RNNsHuman Motion Forecasting (Generation) with RNNs
Human Motion Forecasting (Generation) with RNNs
 
Deformable Convolutional Network (2017)
Deformable Convolutional Network (2017)Deformable Convolutional Network (2017)
Deformable Convolutional Network (2017)
 
Introduction to Deep Learning with TensorFlow
Introduction to Deep Learning with TensorFlowIntroduction to Deep Learning with TensorFlow
Introduction to Deep Learning with TensorFlow
 
About Two Motion Planning Papers
About Two Motion Planning PapersAbout Two Motion Planning Papers
About Two Motion Planning Papers
 
기계학습(Machine learning) 입문하기
기계학습(Machine learning) 입문하기기계학습(Machine learning) 입문하기
기계학습(Machine learning) 입문하기
 
Lie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsLie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot Mechanics
 
로봇과 인공지능, 그리고 미래의 노동
로봇과 인공지능, 그리고 미래의 노동로봇과 인공지능, 그리고 미래의 노동
로봇과 인공지능, 그리고 미래의 노동
 
Introduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep LearningIntroduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep Learning
 
Lie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsLie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot Mechanics
 

Último

chapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineeringchapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineeringmulugeta48
 
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance BookingCall Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance Bookingroncy bisnoi
 
UNIT - IV - Air Compressors and its Performance
UNIT - IV - Air Compressors and its PerformanceUNIT - IV - Air Compressors and its Performance
UNIT - IV - Air Compressors and its Performancesivaprakash250
 
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night StandCall Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Standamitlee9823
 
KubeKraft presentation @CloudNativeHooghly
KubeKraft presentation @CloudNativeHooghlyKubeKraft presentation @CloudNativeHooghly
KubeKraft presentation @CloudNativeHooghlysanyuktamishra911
 
Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...
Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...
Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...soginsider
 
Thermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.pptThermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.pptDineshKumar4165
 
Hostel management system project report..pdf
Hostel management system project report..pdfHostel management system project report..pdf
Hostel management system project report..pdfKamal Acharya
 
Unit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdfUnit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdfRagavanV2
 
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...roncy bisnoi
 
Introduction to Serverless with AWS Lambda
Introduction to Serverless with AWS LambdaIntroduction to Serverless with AWS Lambda
Introduction to Serverless with AWS LambdaOmar Fathy
 
Unit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdfUnit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdfRagavanV2
 
Thermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VThermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VDineshKumar4165
 
Block diagram reduction techniques in control systems.ppt
Block diagram reduction techniques in control systems.pptBlock diagram reduction techniques in control systems.ppt
Block diagram reduction techniques in control systems.pptNANDHAKUMARA10
 
Work-Permit-Receiver-in-Saudi-Aramco.pptx
Work-Permit-Receiver-in-Saudi-Aramco.pptxWork-Permit-Receiver-in-Saudi-Aramco.pptx
Work-Permit-Receiver-in-Saudi-Aramco.pptxJuliansyahHarahap1
 

Último (20)

chapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineeringchapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineering
 
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance BookingCall Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Wakad Call Me 7737669865 Budget Friendly No Advance Booking
 
UNIT - IV - Air Compressors and its Performance
UNIT - IV - Air Compressors and its PerformanceUNIT - IV - Air Compressors and its Performance
UNIT - IV - Air Compressors and its Performance
 
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night StandCall Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Bangalore ☎ 7737669865 🥵 Book Your One night Stand
 
KubeKraft presentation @CloudNativeHooghly
KubeKraft presentation @CloudNativeHooghlyKubeKraft presentation @CloudNativeHooghly
KubeKraft presentation @CloudNativeHooghly
 
Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...
Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...
Hazard Identification (HAZID) vs. Hazard and Operability (HAZOP): A Comparati...
 
(INDIRA) Call Girl Meerut Call Now 8617697112 Meerut Escorts 24x7
(INDIRA) Call Girl Meerut Call Now 8617697112 Meerut Escorts 24x7(INDIRA) Call Girl Meerut Call Now 8617697112 Meerut Escorts 24x7
(INDIRA) Call Girl Meerut Call Now 8617697112 Meerut Escorts 24x7
 
(INDIRA) Call Girl Bhosari Call Now 8617697112 Bhosari Escorts 24x7
(INDIRA) Call Girl Bhosari Call Now 8617697112 Bhosari Escorts 24x7(INDIRA) Call Girl Bhosari Call Now 8617697112 Bhosari Escorts 24x7
(INDIRA) Call Girl Bhosari Call Now 8617697112 Bhosari Escorts 24x7
 
Thermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.pptThermal Engineering -unit - III & IV.ppt
Thermal Engineering -unit - III & IV.ppt
 
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak HamilCara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
 
Hostel management system project report..pdf
Hostel management system project report..pdfHostel management system project report..pdf
Hostel management system project report..pdf
 
Unit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdfUnit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdf
 
Call Girls in Ramesh Nagar Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Ramesh Nagar Delhi 💯 Call Us 🔝9953056974 🔝 Escort ServiceCall Girls in Ramesh Nagar Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Ramesh Nagar Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
 
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
Call Girls Pimpri Chinchwad Call Me 7737669865 Budget Friendly No Advance Boo...
 
Introduction to Serverless with AWS Lambda
Introduction to Serverless with AWS LambdaIntroduction to Serverless with AWS Lambda
Introduction to Serverless with AWS Lambda
 
Unit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdfUnit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdf
 
Thermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VThermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - V
 
Block diagram reduction techniques in control systems.ppt
Block diagram reduction techniques in control systems.pptBlock diagram reduction techniques in control systems.ppt
Block diagram reduction techniques in control systems.ppt
 
Water Industry Process Automation & Control Monthly - April 2024
Water Industry Process Automation & Control Monthly - April 2024Water Industry Process Automation & Control Monthly - April 2024
Water Industry Process Automation & Control Monthly - April 2024
 
Work-Permit-Receiver-in-Saudi-Aramco.pptx
Work-Permit-Receiver-in-Saudi-Aramco.pptxWork-Permit-Receiver-in-Saudi-Aramco.pptx
Work-Permit-Receiver-in-Saudi-Aramco.pptx
 

On Calibration of Modern Neural Networks (2017)