Academic papers
The following paper provides background on the algorithms behind hyperparameter optimization techniques used in Sweeps. Li, Lisha, et al. “Hyperband: A novel bandit-based approach to hyperparameter optimization.” The Journal of Machine Learning Research 18.1 (2017): 6765-6816.Sweeps experiments
The following W&B Reports showcase projects that explore hyperparameter optimization with Sweeps.- Drought Watch Benchmark Progress
- Description: Developing the baseline and exploring submissions to the Drought Watch benchmark.
- Tuning Safety Penalties in Reinforcement Learning
- Description: Examines agents trained with different side effect penalties on three different tasks: pattern creation, pattern removal, and navigation.
- Meaning and Noise in Hyperparameter Search with W&B Stacey Svetlichnaya
- Description: How do you distinguish signal from pareidolia (imaginary patterns)? This article showcases what’s possible with W&B and aims to inspire further exploration.
- Who is Them? Text Disambiguation with Transformers
- Description: Using Hugging Face to explore models for natural language understanding.
- DeepChem: Molecular Solubility
- Description: Predict chemical properties from molecular structure with random forests and deep nets.
- Intro to MLOps: Hyperparameter Tuning
- Description: Explore why hyperparameter optimization matters and examine three algorithms to automate hyperparameter tuning for your machine learning models.
How-to guide
The following how-to guide demonstrates how to solve real-world problems with W&B:- Sweeps with XGBoost
- Description: How to use Sweeps for hyperparameter tuning using XGBoost.