Self-driving laboratories for chemistry and materials science

G Tom, SP Schmid, SG Baird, Y Cao, K Darvish… - Chemical …, 2024 - ACS Publications
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

B Bischl, M Binder, M Lang, T Pielok… - … : Data Mining and …, 2023 - Wiley Online Library
Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

Will affective computing emerge from foundation models and general artificial intelligence? A first evaluation of ChatGPT

MM Amin, E Cambria, BW Schuller - IEEE Intelligent Systems, 2023 - ieeexplore.ieee.org
ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it
has demonstrated competent performance across many natural language processing tasks …

Neural architecture search: Insights from 1000 papers

C White, M Safari, R Sukthanker, B Ru, T Elsken… - arXiv preprint arXiv …, 2023 - arxiv.org
In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …

Auto-sklearn 2.0: Hands-free automl via meta-learning

M Feurer, K Eggensperger, S Falkner… - Journal of Machine …, 2022 - jmlr.org
Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes

P Saves, R Lafage, N Bartoli, Y Diouane… - … in Engineering Software, 2024 - Elsevier
Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …

Efficient methods for natural language processing: A survey

M Treviso, JU Lee, T Ji, B Aken, Q Cao… - Transactions of the …, 2023 - direct.mit.edu
Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance

S Watanabe - arXiv preprint arXiv:2304.11127, 2023 - arxiv.org
Recent advances in many domains require more and more complicated experiment design.
Such complicated experiments often have many parameters, which necessitate parameter …

HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO

K Eggensperger, P Müller, N Mallik, M Feurer… - arXiv preprint arXiv …, 2021 - arxiv.org
To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial
component of machine learning and its applications. Over the last years, the number of …

Hyperparameters in reinforcement learning and how to tune them

T Eimer, M Lindauer… - … Conference on Machine …, 2023 - proceedings.mlr.press
In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …