The End Of Knowledge - Vault 6/86 - CVPR - 2023 - Learning-Rate-Free Learning by D-Adaptation

graph LR classDef adaptation fill:#d4f9d4, font-weight:bold, font-size:14px classDef optimization fill:#f9d4d4, font-weight:bold, font-size:14px classDef application fill:#d4d4f9, font-weight:bold, font-size:14px classDef evaluation fill:#f9f9d4, font-weight:bold, font-size:14px A[Learning-Rate-Free Learning by
D-Adaptation] --> B[D-Adaptation:
auto-setting
learning rates. 1] A --> C[Convex Lipschitz:
optimal convergence
rates. 2] A --> D[Subgradient
method:
minimizes convex
functions. 3] A --> E[Learning rate:
parameter controlling
updates. 4] A --> F[AdaGrad-Norm:
adaptive learning
rate method. 5] A --> G[Dual averaging:
optimization
framework. 6] B --> H[Lower bound:
maintains optimal
solution distance. 7] H --> I[Asymptotic
convergence:
optimal rate
at infinity. 8] I --> J[Non-asymptotic:
fixed iteration
performance. 9] I --> K[Coordinate-wise:
different rates
per dimension. 10] H --> L[Stochastic
optimization:
for noisy
gradients. 11] B --> M[SGD:
modified with
D-Adaptation. 12] M --> N[Adam:
integrates
D-Adaptation. 13] N --> O[Momentum:
accelerates
convergence. 14] O --> P[Learning rate
schedules:
predefined adjustment
patterns. 15] C --> Q[Convex
problems:
experimental
evaluations. 16] Q --> R[Image
classification:
training neural
networks. 17] R --> S[LSTM:
training
sequence models. 18] S --> T[Masked Language:
train BERT
models. 19] T --> U[Auto-regressive:
train GPT
models. 20] U --> V[Object Detection:
train identification
models. 21] A --> W[Vision Transformers:
train vision
tasks. 22] W --> X[fastMRI:
accelerating MRI
reconstruction. 23] X --> Y[Recommendation
Systems:
train personalized
models. 24] Y --> Z[Sensitivity
analysis:
performance
variations. 25] Z --> AA[Observed rates:
compared to
hand-tuned. 26] AA --> AB[Gradient Descent:
D-Adaptation
variant. 27] A --> AC[EMA:
technique in
Adam variant. 28] AC --> AD[Theoretical
guarantees:
convergence
proofs. 29] AD --> AE[Experimental
results:
evaluation across
tasks. 30] class B,C,D,E,F,G adaptation class H,I,J,K,L,M,N,O,P optimization class Q,R,S,T,U,V,W,X,Y,Z,AA,AB application class AC,AD,AE evaluation

Resume:

1.- D-Adaptation: A technique for automatically setting learning rates in optimization algorithms without requiring hyperparameter tuning.

2.- Convex Lipschitz functions: A class of mathematical functions for which D-Adaptation is proven to achieve optimal convergence rates.

3.- Subgradient method: An optimization algorithm that uses subgradients to minimize convex functions.

4.- Learning rate/step size: A parameter controlling how much an optimization algorithm updates parameters at each step.

5.- AdaGrad-Norm: An adaptive learning rate method that D-Adaptation builds upon.

6.- Dual averaging: An optimization framework that D-Adaptation uses as its foundation.

7.- Lower bound estimation: D-Adaptation maintains and updates a lower bound on the distance to the optimal solution.

8.- Asymptotic convergence: D-Adaptation achieves the optimal convergence rate as the number of iterations approaches infinity.

9.- Non-asymptotic analysis: Examination of D-Adaptation's performance for a fixed number of iterations.

10.- Coordinate-wise scaling: An extension of D-Adaptation to handle different learning rates for each parameter dimension.

11.- Stochastic optimization: Applying D-Adaptation to problems with noisy or sampled gradients.

12.- SGD with D-Adaptation: Modification of Stochastic Gradient Descent to incorporate D-Adaptation.

13.- Adam with D-Adaptation: Integration of D-Adaptation into the Adam optimizer.

14.- Momentum: A technique incorporated into D-Adaptation to accelerate convergence in certain scenarios.

15.- Learning rate schedules: Predefined patterns for adjusting learning rates, which can be combined with D-Adaptation.

16.- Convex problems: Experimental evaluation of D-Adaptation on various convex optimization tasks.

17.- Convolutional image classification: Application of D-Adaptation to training neural networks for image recognition.

18.- LSTM Recurrent Neural Networks: Using D-Adaptation for training sequence models in machine translation.

19.- Masked Language Modelling: Applying D-Adaptation to train BERT-like models for natural language processing.

20.- Auto-regressive Language Modelling: Using D-Adaptation to train GPT-like models for text generation.

21.- Object Detection: Applying D-Adaptation to train models for identifying objects in images.

22.- Vision Transformers: Using D-Adaptation to train transformer-based models for computer vision tasks.

23.- fastMRI: Application of D-Adaptation to train models for accelerating MRI image reconstruction.

24.- Recommendation Systems: Using D-Adaptation to train models for personalized content recommendations.

25.- Sensitivity analysis: Examining how D-Adaptation's performance varies with different initial parameter settings.

26.- Observed learning rates: Comparison of D-Adaptation's automatically chosen learning rates to hand-tuned values.

27.- Gradient Descent variant: A version of D-Adaptation applied to standard gradient descent optimization.

28.- Exponential Moving Average (EMA): A technique used in the Adam variant of D-Adaptation.

29.- Theoretical guarantees: Mathematical proofs of D-Adaptation's convergence properties and performance bounds.

30.- Experimental results: Comprehensive evaluation of D-Adaptation across various machine learning tasks and model architectures.

Knowledge Vault built byDavid Vivancos 2024