Learning To Learn

Learning To Learn - train learned optimizers… | Awesome Repos

Features

Meta-Optimization - Uses gradient-based meta-optimization to refine the learning process of target neural networks.
Deep Learning Toolkits - Provides a set of utilities for configuring problem architectures and evaluating convergence on benchmark datasets.
Problem Ensembling - Manages the combination of multiple distinct optimization tasks into a single weighted loss function.
Learned Optimizers - Implements a library of tools for building and training neural networks that act as optimizers.
Loss Graph Definitions - Provides a way to define mathematical tasks and loss graphs as targets for learned optimization algorithms.
Custom Loss Functions - Implements custom objective functions and weighted problem ensembles to calculate error during training.
Meta Learning Frameworks - Provides a TensorFlow-based framework for training neural networks to optimize other models.
Learned Optimizer Training - Implements training loops with configurable epochs and unroll lengths to improve the performance of learned optimizers.
Meta-Optimizer Training - Executes training loops that optimize a learning algorithm based on cumulative evaluation costs over specified epochs.
Problem Ensembling - Combines multiple distinct optimization tasks into a single weighted loss function for simultaneous training.
Meta Learning Libraries - Provides a toolkit for developing and training neural networks that learn how to optimize other models.
Problem Ensemble Composition - Combines multiple distinct optimization tasks into a single weighted loss function for simultaneous training.
Data Preprocessing - Transforms input gradients using logarithmic scaling and sign extraction to prepare them for model consumption.
Gradient Distribution Transformations - Applies logarithmic transformations and preserves input signs to reshape the distribution of processing data.
Meta-Learning Problem Definitions - Enables the creation of loss graphs for mathematical tasks to serve as training targets for learning algorithms.
Gradient Flow Stabilizers - Applies logarithmic scaling and sign extraction to gradients to ensure numerical stability during training.
Optimizer Convergence Evaluators - Provides utilities to test optimizer effectiveness by measuring convergence and performance on target problems.
Model Execution Benchmarks - Evaluates the execution cost and performance of optimization algorithms across diverse mathematical problem sets.
Benchmark Problem Configurations - Provides tools for setting up network architectures and hyperparameters for consistent testing across benchmark tasks.
Problem Modeling - Provides a framework for defining optimization problem structures, including custom objective functions and loss operations.
Unroll-Length Optimizations - Executes training iterations over specified sequence lengths to optimize the learning algorithm's performance.

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Star history

Open-source alternatives to Learning To Learn

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