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Seeing Unseens with Machine Learning -- 
見えていないものを見出す機械学習

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Deep Learningを筆頭に、データから意味やパターンを抽出する機械学習は、いまや誰もが使えるツールになりつつあります。
本セッションでは、AIブームわく最中、機械学習がなぜ大事なのか、どんな使い方をするのが重要になっていくかについて展望しつつ、「見えていなかったものを見出す」というネクストフロンティアになるであろう機械学習の方向性についてお話します。

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Seeing Unseens with Machine Learning -- 
見えていないものを見出す機械学習

  1. 1. May 13th, 2019 Tatsuya Shirakawa Seeing Unseens with Machine Learning
 ⾒えていないものを⾒出す機械学習 Tech-on MeetUp#06 — What can “AI (I)” do?
  2. 2. Tatsuya Shirakawa 2 ABEJA, Inc. (Researcher) - Deep Learning (CV, Graph, NLP, ) - Machine Learning Github https://github.com/TatsuyaShirakawa NTT Data Mathematical Systems Inc. - Mathematical Optimization - Machine Learning / Deep Learning Math. Tech blog http://tech-blog.abeja.asia/ - 異空間への埋め込み!Poincare Embeddingsが拓く表現学習の新展開 - 機は熟した!グラフ構造に対するDeep Learning、Graph Convolutionのご紹介 - 双曲空間でのMachine Learningの最近の進展 - より良い機械学習のためのアノテーションの機械学習 Now
  3. 3. Researchers at ABEJA 3 1. 2. 3. 4. 先に⾒つける シンプルに解く 先に失敗する • 最新テクノロジーのキャッチアップ • 技術視点を交えた新しいビジネス構想 • 独⾃技術の開発・検証 • ⾼難易度タスクのコアロジックの構築 • 技術ソリューションの提案 • プロダクトの根本的な精度改善 • アイデアの検証 • 既存のやり⽅/考え⽅の再検討
 視点を与える
  4. 4. AIの特性ふりかえり 4 MLについて⾔いたいこと AI ML DL
  5. 5. Daniel Kehneman There are two modes of thought System 1(勘・直感) fast, instinctive and emotional System 2(論理的思考) Slower, more deliberative, and more logical 5 MLはコッチ
  6. 6. 6(Andrew Ng, “AI Transformation Playbook”) AIは運⽤することで
 改善する
  7. 7. AIは⼈間の代替ではない 7 Human AI 同じ作業を続ける ✔ スケールさせる ✔ 未知な状況への適応 ✔ 複雑な作業 ✔ 適材適所が⼤事
  8. 8. Today’s Talk 1. Software 2.0
 2. Bigger, Deeper and Better
 3. Discovery 2.0 8 1. Software 2.0
 2. Bigger, Deeper and Better
 3. Discovery 2.0
  9. 9. 9
  10. 10. Software 2.0 Software 1.0 — Write a program that works - Explicit instructions to the computer which identifies a specific point in program space with some desirable behavior Software 2.0 — Find a desirable program that fits to data - A rough skelton of the code (e.g. NNs) that identifies a subset of program space to search - Search this space for a program that works 10
  11. 11. Why Software 2.0? "it is significantly easier to collect the data (or more generally, identify a desirable behavior) than to explicitly write the program” 11
  12. 12. Dogs and Cats Why dogs are dogs and cats are cats? 12
  13. 13. Gender, Age, Recognition is Not Trivial ! 13
  14. 14. Paradigm Change Things which is hard to define/code can be learn implicitly from data 14 00110110110
 11010101010 11010111011
 10110110100 01010111011 Coding Learn Software
  15. 15. Today’s Talk 1. Software 2.0
 2. Bigger, Deeper and Better
 3. Discovery 2.0 15
  16. 16. Bigger, Deeper and Better 16 Large Scale GAN Training for High Fidelity Natural Image Synthesis (2018.9)  BigGAN — 巨⼤な計算リソースで学習された巨⼤なモデルで⾼解像度画像の⽣成に成功。 GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism (2018.11)  GPipe — 巨⼤なNNを効率的に学習するための分散学習ライブラリ。ImageNetで新SOTA。 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018.10)  BERT — 巨⼤なモデルを巨⼤なデータで教師なしすることで⾔語理解系タスクにたいする強⼒な初期モデルを獲得 Language Models are Unsupervised Multitask Learners (2019.2)
  GPT-2 — 巨⼤な⾔語モデルをクリーンで巨⼤なデータで学習し、⽂書⽣成系タスクをゼロショットで⾼精度にこなせるモデルを獲得
  17. 17. BigGAN — Class Conditionalな⾼解像度画像⽣成 既存のSOTA⼿法(SA-GAN)に対して、バッチサイズやチャンネル数を増やし、各種⼯夫を加え ることで、512x512のClass Conditionalな⾼精度画像⽣成に成功。既存SOTAを⼤きく上回るスコ アを達成。 17“Large Scale GAN Training for High Fidelity Natural Image Synthesis ”
  18. 18. GPipe — 巨⼤なNNの学習に最適化された分散学習ライブラリ 複数のGPUを活⽤してForward/Backward計算をスケーラブルかつ効率的に⾏うライブラリ。こ れを⽤いて学習された巨⼤なモデルはImageNetで新たなSOTAを達成。 18(“GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism”)
  19. 19. BERT — ⾔語理解系タスクでの強⼒な教師なし事前学習⼿法 強⼒なモデル(BERT)を教師なしで構成できる下記の2タスクで事前学習することで
 さまざまな⾔語理解系タスクでSOTAを⼤幅更新 19 The cat [MASK] on the mat sat 1. 単語の⽳埋め GLUE test results (論⽂より) 1. The man went to [MASK] store 2. He bought a gallon [MASK] milk → IsNext / NotNext? 2. ⼆⽂が連続⽂かどうかの判定
  20. 20. GPT-2 — クリーンで多様なデータで学習された巨⼤な⾔語モデル 信頼性の⾼いWebページをクローリングして得たクリーンで多様なコーパス(WebText)上で 強⼒な⾔語モデル(GPT-2)を教師なし学習(尤度最⼤化)。
 ⽂書⽣成系のさまざまなタスクのZero-shot学習でSOTAを更新 20
  21. 21. 21 Winning Way Larger (Cleaner) Datasets 
 + Deeper Neural Networks
  22. 22. Today’s Talk 1. Software 2.0
 2. Bigger, Deeper and Better
 3.Discovery 2.0 22
  23. 23. Can You See Gender/Age from Ears? 23 0 10 20 30 40 50 60 70 80 Age
  24. 24. Can You See Gender, Age and BMI from Eyes (Fundus)?
 How about Heart / Brain Diseases? 24 0 10 20 30 40 50 60 70 80 Age
  25. 25. DNNs Can See Gender/Age from Ears D. Yaman+, “Age and Gender Classification from Ear Images”, IWBF2018 25 Age: 18-28 / 29-38 / 39-48 / 49-58 / 58-68+
  26. 26. DNNs Can See Gender, Age, BMI and even 
 Brain/Heart Diseases from Eyes R. Poplin+, “Prediction of cardiovascular risk factors from retinal fundus 
 photographs via deep learning”, Nature Biomedical Engineering 2018 26
  27. 27. What happens? DNNs can see what human cannot see or recognize. 27 …
  28. 28. Discovery 2.0 Discovery 1.0 — Fully Utilizing Domain Knowledge - explicit construction of hypothesis is constructed mainly from domain knowledge or deep understanding of the domain Discovery 2.0 — Seeing by Training - capture some aspects of data by training models on it - not new but should be emphasized again 28 ※ serendipity could be another source of discovery :)
  29. 29. Discovery 2.0 — Seeing by Training 1. Seeing Predictability / Correlation 29
  30. 30. Discovery 2.0 — Seeing by Training 1. Seeing Predictability / Correlation Beyond human imagination
 - Every data should be connect to create new connections Correlation first - Correlation finding is the first goal - Causality should be checked post-hook if possible Relatively cheap to apply if data exists - Models should have weak domain dependence (e.g. NNs) 30
  31. 31. Graph Convolutional Neural Networks (GCNNs) A specific type of neural networks which is 
 designed for processing connectivity of data well 31 Tech blog http://tech-blog.abeja.asia/ - 異空間への埋め込み!Poincare Embeddingsが拓く表現学習の新展開 - 機は熟した!グラフ構造に対するDeep Learning、Graph Convolutionのご紹介 - 双曲空間でのMachine Learningの最近の進展 - より良い機械学習のためのアノテーションの機械学習
  32. 32. Task Relations — Taskonomy Relation = Transferability
 A. R. Zamir+, “Taskonomy: Disentangling Task Transfer Learning”, CVPR2018 32 Autoencoding Object Class. Scene Class. Curvature Denoising Occlusion Edges Egomotion Cam. Pose (fix) 2D Keypoint 3D Keypoint Cam. Pose (nonfix) Matching Reshading Distance Z-Depth Normals Layout 2.5D Segm. 2D Segm. Semantic Segm. Vanishing Pts. Novel Task 1 Novel Task 2 Novel Task 3 Autoencoding Object Class. Scene Class. Curvature Denoising Occlusion Edges Egomotion Cam. Pose (fix) 2D Keypoint 3D Keypoint Cam. Pose (nonfix) Matching Reshading Distance Z-Depth Normals Layout 2.5D Segm. 2D Segm. Semantic Segm. Vanishing Pts. Novel Task 1 Novel Task 2 Novel Task 3 https://storage.googleapis.com/taskonomy_slides/taskonomy_slides.html
  33. 33. Discovery 2.0 — Seeing by Training 1. Seeing Predictability / Correlation
 2. Representation Learning / Embeddings 33 T. Mikolov+, “Distributed representation of words and phrases and their 
 compositionality, NeurIPS2013 https://github.com/facebookresearch/poincare-embeddings
  34. 34. Hyperbolic Space • Manifolds with positive constant sectional curvature • Tree structure is naturally aligned in the space
 → automatic tree structure detection! 34 Tech blog http://tech-blog.abeja.asia/ - 異空間への埋め込み!Poincare Embeddingsが拓く表現学習の新展開 - 機は熟した!グラフ構造に対するDeep Learning、Graph Convolutionのご紹介 - 双曲空間でのMachine Learningの最近の進展 - より良い機械学習のためのアノテーションの機械学習 「異空間散歩!双曲空間を歩いてみよう。」
  35. 35. Hyperbolic Embeddings 35 [M. Nickel+]
 Poincaré Embeddings ~ 17’ 05 18’ 04 [C. D. Sa+]
 Representation Tradeoff ~
 (Near-exact tree embs., h-MDS) [O. Ganea+]
 Hyperbolic Entailment Cones
 (Poincare embs. + Order embs.) 18’ 05 [C. Gulcehre+]
 Hyperbolic Attention Networks
 (Einstein Mid. Point) [O. Ganea+]
 Hyperbolic Neural Networks [M. Nickel+]
 Learning Continuous Hierarchies
 in the Lorentz Model~ [A. Tifrea+]
 Poincaré Glove ~ (Poincaré Glove) 18’ 06 18’ 10 …19’ 2 [R. Suzuki+]
 Hyperbolic Disk Embeddings [A. Gu+]
 Mixed-Curvature 
 Representations 18’ 9
  36. 36. Mixed-Curvature Representations A. Gu+, “Learning Mixed-Curvature Representations in Products of model Spaces”, ICLR2019 ユークリッド空間、球⾯、双曲空間の積空間への埋め込みを構成することで、 様々な(断⾯)曲率の空間への埋込を可能にした。 36
  37. 37. 37 データの構造が
 ⾒えた!
  38. 38. Discovery 2.0 — Seeing by Training 1. Seeing Predictability / Correlation
 2. Representation Learning / Embeddings ※ Off course, domain/scientific knowledge is crucial for efficient/meaningful exploration 38
  39. 39. What can AI (I) do? 39 00110110110
 11010101010 11010111011
 10110110100 01010111011 Coding Learn Software Discovery Dom ain Know ledge Learn Software 1.0 Software 2.0 Discovery 1.0 Discovery 2.0
  40. 40. What can AI (I) do? 40 Psychology 
 x
 AI
  41. 41. ⼈格⼼理学(Personality Psychology) Personality Psychology is a scientific study which aims to show how people are individually different due to psychological forces (wikipedia). 41 Personality Traits(特性)
 Features Personality Types(類型) Clustering / Classification あなたは◯◯タイプ! ex) ex)
  42. 42. Big 5(Five Factor Model, FFM) 42 1. Openness(経験への開放性)
 is a general appreciation for art, emotion, adventure, 
 unusual ideas, imagination, curiosity, and variety of experience 2. Conscientiousness(誠実性)
 is a tendency to display self-discipline, act dutifully, and strive 
 for achievement against measures or outside expectations 3. Extraversion(外向性)
 is characterized by breadth of activities (as opposed to depth), surgency 
 from external activity/situations, and energy creation from external means 4. Agreeableness(協調性)
 trait reflects individual differences in general concern for social harmony 5. Neuroticism(神経症的傾向)
 is the tendency to experience negative emotions, such as anger, anxiety, or depression (wikipedia)
  43. 43. Big5はすごい! • さまざまな研究で提案されたパーソナリティ特性との相関性が⾼い(事実上の デファクトスタンダード)
 • 英語辞書中のパーソナリティに関する単語と既存パーソナリティテストの結果 を総合して作られた、けっこうデータ駆動な作られ⽅ • 双⼦の研究によると、Big5の変動のだいたい50%は遺伝で、残りの50%は環境 で決まっている(分散分析) • Big5のうち、Agreeablenessをのぞいた4つは、年をとってもあまり変化しない 43
  44. 44. What can I do? 1. Personality embeddings (in hyperbolic spaces?) 2. Multimodal analysis (facial expressions, psychological measures, ) 3. More efficient assessor (like Akinator?)
 
 
 脳科学、遺伝学、進化論、⼼理学、…、機械学習の融合領域! 44
  45. 45. 45 Annotation Driven AI

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