Automated Discovery of Powerful Deep Learning Optimizers, Decay Functions, and Learning Rate Schedules

Concetti Chiave

The authors propose a new dual-joint search space for neural optimizer search (NOS) that simultaneously optimizes the weight update equation, internal decay functions, and learning rate schedules. They discover multiple optimizers, learning rate schedules, and Adam variants that outperform standard deep learning optimizers across image classification tasks.

Sintesi

The authors present a new approach for neural optimizer search (NOS) that expands on previous work by simultaneously optimizing the weight update equation, internal decay functions, and learning rate schedules.
Key highlights:

Proposed a new dual-joint search space for NOS that incorporates the latest research on deep learning optimizers, including concepts like quasi-hyperbolic momentum, AdaBelief, AMSGrad, and more.
Developed an integrity check to efficiently eliminate degenerate optimizers and a problem-specific, mutation-only genetic algorithm that can be massively parallelized.
Discovered multiple optimizers, learning rate schedules, and Adam variants that outperformed standard deep learning optimizers like Adam, SGD, and RMSProp across image classification tasks on CIFAR-10, CIFAR-100, TinyImageNet, Flowers102, Cars196, and Caltech101.
The discovered optimizers leverage concepts like quasi-hyperbolic momentum, adaptive learning rates, and custom decay functions to achieve superior performance.
Conducted supplementary experiments to obtain Adam variants and new learning rate schedules for Adam, further expanding the set of powerful optimizers.
Demonstrated the importance of the jointly learned decay functions and learning rate schedules in the discovered optimizers, as removing them degraded performance.
The authors' comprehensive approach to NOS, incorporating the latest advancements in deep learning optimizers, has led to the discovery of highly effective optimizers that can serve as drop-in replacements for standard optimizers across a variety of image recognition tasks.

Statistiche

The authors report the following key metrics:

CIFAR-10 test accuracy up to 96.23%
CIFAR-100 test accuracy up to 79.80%
Flowers102 test accuracy up to 97.76%
Cars196 test accuracy up to 91.79%
Caltech101 test accuracy up to 92.76%
TinyImageNet test accuracy up to 48.82%

Citazioni

"The success of Opt6 is heavily dependent upon the inherently learned LR2 schedule, as Opt61 always under-performed Opt6 when training from scratch."
"Opt101 always out-performed Nesterov's momentum when training from scratch. We empirically noticed that Opt101 liked large learning rates around 10. We believe that the double scaling of the gradients clips larger gradient values, allowing for larger learning rates to scale gradients near zero to have more of an effect, which empirically seems beneficial."

Approfondimenti chiave tratti da

Neural Optimizer Equation, Decay Function, and Learning Rate Schedule Joint Evolution

by Brandon Morg... alle arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06679.pdf

Domande più approfondite

How can the discovered optimizers, decay functions, and learning rate schedules be further analyzed to gain insights into their strengths and weaknesses for different types of deep learning tasks and architectures

The discovered optimizers, decay functions, and learning rate schedules can be further analyzed to gain insights into their strengths and weaknesses for different types of deep learning tasks and architectures by conducting the following analyses:

Performance on Various Datasets: Evaluate the optimizers, decay functions, and learning rate schedules on a diverse set of datasets beyond the ones mentioned in the study. This will help understand their generalization capabilities and robustness across different data distributions.

Architectural Compatibility: Analyze how the discovered components interact with different neural network architectures. Assess their performance on various architectures such as CNNs, RNNs, Transformers, etc., to determine if they exhibit consistent improvements across different model types.

Hyperparameter Sensitivity: Investigate how sensitive the discovered components are to hyperparameter settings such as batch size, initialization methods, and regularization techniques. Understanding their sensitivity can provide insights into their adaptability to different training scenarios.

Transfer Learning Performance: Evaluate the discovered components in transfer learning scenarios across a wide range of pre-trained models and downstream tasks. This analysis can reveal their effectiveness in leveraging pre-trained representations for new tasks.

Robustness Analysis: Conduct robustness tests to assess how the optimizers, decay functions, and learning rate schedules perform under noisy or adversarial conditions. This analysis can shed light on their stability and reliability in challenging environments.

Scalability Testing: Test the scalability of the discovered components by analyzing their performance on larger datasets and more complex models. Understanding how well they scale can provide insights into their applicability to real-world, large-scale deep learning tasks.

What other deep learning components, beyond just optimizers, could benefit from a similar joint optimization approach as proposed in this work

Other deep learning components that could benefit from a similar joint optimization approach include:

Activation Functions: Optimizing activation functions along with optimizers, decay functions, and learning rate schedules can lead to improved convergence and generalization in deep learning models.

Regularization Techniques: Jointly optimizing regularization methods such as dropout, batch normalization, and weight decay with other components can enhance model performance and prevent overfitting.

Loss Functions: Incorporating the optimization of loss functions into the joint evolution process can help tailor the loss function to specific tasks, leading to better model performance.

Network Architectures: Jointly optimizing network architectures, including layer configurations, skip connections, and attention mechanisms, can result in more efficient and effective deep learning models.

Data Augmentation Strategies: Optimizing data augmentation techniques in conjunction with other components can improve model robustness and generalization capabilities.

Could the authors' methodology be extended to automatically discover other hyperparameters or architectural components of deep learning models in an end-to-end fashion

The authors' methodology could be extended to automatically discover other hyperparameters or architectural components of deep learning models in an end-to-end fashion by:

Automated Architecture Search: Integrate the joint evolution approach with neural architecture search (NAS) techniques to automatically discover optimal network architectures, hyperparameters, optimizers, and other components simultaneously.

Dynamic Hyperparameter Tuning: Develop a dynamic hyperparameter tuning mechanism that adapts the discovered optimizers, decay functions, and learning rate schedules based on the model's performance during training, leading to adaptive and efficient optimization.

Multi-Objective Optimization: Extend the methodology to perform multi-objective optimization, considering multiple performance metrics simultaneously to find a balance between different aspects of model performance.

Meta-Learning Techniques: Incorporate meta-learning techniques to enable the discovered components to adapt to new tasks and datasets quickly, enhancing the model's transfer learning capabilities.

Interpretability Analysis: Include interpretability analysis tools to understand the impact of the discovered components on the model's decision-making process, providing insights into the inner workings of the optimized models.