automl / zero-shot-automl-with-pretrained-models
Official repository for the paper "Zero-Shot AutoML with Pretrained Models"
☆47Updated last year
Alternatives and similar repositories for zero-shot-automl-with-pretrained-models
Users that are interested in zero-shot-automl-with-pretrained-models are comparing it to the libraries listed below
Sorting:
- A learning curve benchmark on OpenML data☆30Updated 5 months ago
- ☆79Updated 3 weeks ago
- [NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations☆13Updated 2 years ago
- The official implementation of PFNs4BO: In-Context Learning for Bayesian Optimization☆26Updated last year
- Launching and monitoring Slurm experiments in Python☆18Updated last month
- Code for "Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?" [ICML 2023]☆32Updated 8 months ago
- Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective (https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi …☆65Updated 4 years ago
- ☆22Updated 2 years ago
- Optimization algorithm which fits a ResNet to CIFAR-10 5x faster than SGD / Adam (with terrible generalization)☆14Updated last year
- Easy-to-use AdaHessian optimizer (PyTorch)☆78Updated 4 years ago
- ModelDiff: A Framework for Comparing Learning Algorithms☆56Updated last year
- Introducing diverse tasks for NAS☆50Updated 2 years ago
- ☆28Updated 11 months ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated last year
- ☆17Updated 7 months ago
- [AutoML'22] Bayesian Generational Population-based Training (BG-PBT)☆28Updated 2 years ago
- ☆34Updated 5 months ago
- ☆49Updated last year
- The first collection of surrogate benchmarks for Joint Architecture and Hyperparameter Search.☆15Updated 2 years ago
- Code accompanying our paper "Feature Learning in Infinite-Width Neural Networks" (https://arxiv.org/abs/2011.14522)☆62Updated 4 years ago
- ☆15Updated 5 years ago
- An interactive framework to visualize and analyze your AutoML process in real-time.☆87Updated last week
- We propose an evolution-based approach to meta-learn synthetic neural environments and reward neural networks for reinforcement learning.☆21Updated 2 years ago
- Code for "Supermasks in Superposition"☆123Updated last year
- Code for "Accelerating Training with Neuron Interaction and Nowcasting Networks" [to appear at ICLR 2025]☆19Updated 2 months ago
- Code accompanying paper: Meta-Learning to Improve Pre-Training☆37Updated 3 years ago
- Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"☆59Updated 3 years ago
- Code for the Population-Based Bandits Algorithm, presented at NeurIPS 2020.☆20Updated 4 years ago
- A system for automating selection and optimization of pre-trained models from the TAO Model Zoo☆25Updated 10 months ago
- PyHopper is a hyperparameter optimizer, made specifically for high-dimensional problems arising in machine learning research.☆86Updated last year