Search Results for author: Esteban Real

Found 16 papers, 8 papers with code

Evolving Machine Learning Algorithms From Scratch

no code implementations ICML 2020 Esteban Real, Chen Liang, David So, Quoc Le

However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces.

AutoML BIG-bench Machine Learning

AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions

no code implementations13 Dec 2023 Esteban Real, Yao Chen, Mirko Rossini, Connal de Souza, Manav Garg, Akhil Verghese, Moritz Firsching, Quoc V. Le, Ekin Dogus Cubuk, David H. Park

Computers calculate transcendental functions by approximating them through the composition of a few limited-precision instructions.

Discovering Adaptable Symbolic Algorithms from Scratch

no code implementations31 Jul 2023 Stephen Kelly, Daniel S. Park, Xingyou Song, Mitchell McIntire, Pranav Nashikkar, Ritam Guha, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti, Jie Tan, Esteban Real

We evolve modular policies that tune their model parameters and alter their inference algorithm on-the-fly to adapt to sudden environmental changes.

AutoML

Unified Functional Hashing in Automatic Machine Learning

1 code implementation10 Feb 2023 Ryan Gillard, Stephen Jonany, Yingjie Miao, Michael Munn, Connal de Souza, Jonathan Dungay, Chen Liang, David R. So, Quoc V. Le, Esteban Real

In this paper, we show that large efficiency gains can be obtained by employing a fast unified functional hash, especially through the functional equivalence caching technique, which we also present.

Neural Architecture Search

PyGlove: Efficiently Exchanging ML Ideas as Code

1 code implementation3 Feb 2023 Daiyi Peng, Xuanyi Dong, Esteban Real, Yifeng Lu, Quoc V. Le

We also perform a case study of a large codebase where PyGlove led to an 80% reduction in the number of lines of code.

Evolving Reinforcement Learning Algorithms

5 code implementations ICLR 2021 John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Sergey Levine, Quoc V. Le, Honglak Lee, Aleksandra Faust

Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm.

Atari Games Meta-Learning +2

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

1 code implementation6 Mar 2020 Esteban Real, Chen Liang, David R. So, Quoc V. Le

However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces.

AutoML BIG-bench Machine Learning

NAS-Bench-101: Towards Reproducible Neural Architecture Search

4 code implementations25 Feb 2019 Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter

Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation.

Benchmarking Neural Architecture Search

Regularized Evolution for Image Classifier Architecture Search

4 code implementations5 Feb 2018 Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V. Le

The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically.

Evolutionary Algorithms Image Classification +1

Large-Scale Evolution of Image Classifiers

2 code implementations ICML 2017 Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin

Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.

Evolutionary Algorithms Hyperparameter Optimization +3

Attention for Fine-Grained Categorization

no code implementations22 Dec 2014 Pierre Sermanet, Andrea Frome, Esteban Real

This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained categorization on the Stanford Dogs data set.

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