Self-supervised learning

Yann LeCun

**Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:**

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Main[Self-supervised learning] --> A[Learning Approaches]
Main --> B[World Models]
Main --> C[Techniques and Methods]
Main --> D[Challenges and Limitations]
Main --> E[Future Directions]
A --> A1[Self-supervised learning: predict unlabeled input

parts 1] A --> A2[Supervised learning needs large labeled

datasets 2] A --> A3[Reinforcement learning requires many trials 3] A --> A4[Humans learn efficiently with few

samples 4] A --> A5[Babies learn concepts through world

interaction 6] A --> A6[Networks predict tasks on unlabeled

data 7] B --> B1[World models key to improving

AI 5] B --> B2[Model-based RL accelerates skill acquisition 16] B --> B3[Optimal control uses differentiable world

models 17] B --> B4[Driving simulation uses real data

model 18] B --> B5[Video prediction handles uncertainty with

latents 19] B --> B6[World model trains policy through

backpropagation 22] C --> C1[Energy functions capture data dependencies 10] C --> C2[Contrastive methods push data energy

down 11] C --> C3[Regularizing latents limits low-energy space 12] C --> C4[Sparse coding learns data representations 13] C --> C5[Predictor estimates optimal sparse codes 14] C --> C6[Dropout prevents latents capturing too

much 20] D --> D1[Continuous, high-dimensional data prediction challenge 8] D --> D2[Latent variables model uncertainty, multiple

outputs 9] D --> D3[Multi-layer sparse codes ongoing research 15] D --> D4[Inverse curiosity avoids unreliable predictions 23] D --> D5[Uncertainty in continuous spaces key

challenge 24] D --> D6[GANs useful but difficult to

train 25] E --> E1[Adding noise limits information, like

VAEs 21] E --> E2[Seeking reliable uncertainty learning methods 26] E --> E3[Model-based RL interest renewed despite

limitations 27] E --> E4[Classification avoids uncertainty in self-supervision 28] E --> E5[Jigsaw puzzle predicts image patch

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parts 1] A --> A2[Supervised learning needs large labeled

datasets 2] A --> A3[Reinforcement learning requires many trials 3] A --> A4[Humans learn efficiently with few

samples 4] A --> A5[Babies learn concepts through world

interaction 6] A --> A6[Networks predict tasks on unlabeled

data 7] B --> B1[World models key to improving

AI 5] B --> B2[Model-based RL accelerates skill acquisition 16] B --> B3[Optimal control uses differentiable world

models 17] B --> B4[Driving simulation uses real data

model 18] B --> B5[Video prediction handles uncertainty with

latents 19] B --> B6[World model trains policy through

backpropagation 22] C --> C1[Energy functions capture data dependencies 10] C --> C2[Contrastive methods push data energy

down 11] C --> C3[Regularizing latents limits low-energy space 12] C --> C4[Sparse coding learns data representations 13] C --> C5[Predictor estimates optimal sparse codes 14] C --> C6[Dropout prevents latents capturing too

much 20] D --> D1[Continuous, high-dimensional data prediction challenge 8] D --> D2[Latent variables model uncertainty, multiple

outputs 9] D --> D3[Multi-layer sparse codes ongoing research 15] D --> D4[Inverse curiosity avoids unreliable predictions 23] D --> D5[Uncertainty in continuous spaces key

challenge 24] D --> D6[GANs useful but difficult to

train 25] E --> E1[Adding noise limits information, like

VAEs 21] E --> E2[Seeking reliable uncertainty learning methods 26] E --> E3[Model-based RL interest renewed despite

limitations 27] E --> E4[Classification avoids uncertainty in self-supervision 28] E --> E5[Jigsaw puzzle predicts image patch

positions 29] E --> E6[Address continuous, high-dimensional uncertainty learning 30] class Main main class A,A1,A2,A3,A4,A5,A6 learning class B,B1,B2,B3,B4,B5,B6 models class C,C1,C2,C3,C4,C5,C6 techniques class D,D1,D2,D3,D4,D5,D6 challenges class E,E1,E2,E3,E4,E5,E6 future

**Resume: **

**1.-** Self-supervised learning: Learning from unlabeled data by predicting parts of the input from other parts, without human-curated labels.

**2.-** Supervised learning limitations: Requires large labeled datasets, which aren't always available for all problems.

**3.-** Reinforcement learning inefficiency: Requires many trials, impractical for real-world applications like self-driving cars.

**4.-** Human learning efficiency: Humans learn quickly with few samples, suggesting we're missing something in machine learning approaches.

**5.-** Learning world models: Key to improving AI is learning models of how the world works to enable efficient learning.

**6.-** Baby learning stages: Babies learn basic concepts like object permanence and gravity over time through world interaction.

**7.-** Self-supervised learning definition: Training large networks to understand the world through prediction tasks on unlabeled data.

**8.-** Prediction under uncertainty: Challenge in self-supervised learning for continuous, high-dimensional data like images or video.

**9.-** Latent variable models: Using additional variables to model uncertainty and generate multiple possible outputs.

**10.-** Energy-based learning: Formulating self-supervised learning as learning energy functions that capture data dependencies.

**11.-** Contrastive methods: Pushing down energy of data points while pushing up energy of points outside the data manifold.

**12.-** Regularized latent variables: Limiting the volume of low-energy space by regularizing latent variables.

**13.-** Sparse coding: Early example of regularized latent variable systems for learning data representations.

**14.-** Predictive sparse decomposition: Training a predictor to estimate optimal sparse codes, avoiding expensive optimization.

**15.-** Hierarchical representations: Learning multi-layer sparse codes for complex data like images, an ongoing research area.

**16.-** Model-based reinforcement learning: Using learned world models to accelerate skill acquisition, especially in motor tasks.

**17.-** Optimal control theory: Classical approach to control using differentiable world models, basis for some modern AI techniques.

**18.-** Autonomous driving example: Learning to drive in simulation using a world model trained on real driving data.

**19.-** Video prediction model: Neural network trained to predict future frames in driving videos, handling uncertainty with latent variables.

**20.-** Regularizing latent variables: Using techniques like dropout to prevent latent variables from capturing too much information.

**21.-** Variational autoencoder similarity: Adding noise to encoder output to limit information content, similar to VAEs.

**22.-** Policy network training: Using the learned world model to train a driving policy through backpropagation, without real-world interaction.

**23.-** Inverse curiosity: Encouraging the agent to stay in areas where its world model is accurate, avoiding unreliable predictions.

**24.-** Handling uncertainty in continuous spaces: Key technical challenge in self-supervised learning for high-dimensional data.

**25.-** GAN limitations: While useful for handling uncertainty, GANs are difficult to train reliably.

**26.-** Alternatives to GANs: Seeking more reliable methods for learning under uncertainty in high-dimensional spaces.

**27.-** Model-based RL resurgence: Renewed interest in model-based reinforcement learning, despite previous theoretical limitations.

**28.-** Classification workaround: Some self-supervised methods avoid uncertainty by turning prediction into classification tasks.

**29.-** Jigsaw puzzle example: Self-supervised task of predicting relative positions of image patches, avoiding pixel-level prediction.

**30.-** Future direction: Need to directly address the problem of learning under uncertainty in continuous, high-dimensional spaces.

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