Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Grinsztajn, Léo"'
For classification and regression on tabular data, the dominance of gradient-boosted decision trees (GBDTs) has recently been challenged by often much slower deep learning methods with extensive hyperparameter tuning. We address this discrepancy by i
Externí odkaz:
http://arxiv.org/abs/2407.04491
Pretrained deep-learning models are the go-to solution for images or text. However, for tabular data the standard is still to train tree-based models. Indeed, transfer learning on tables hits the challenge of data integration: finding correspondences
Externí odkaz:
http://arxiv.org/abs/2402.16785
There are increasingly efficient data processing pipelines that work on vectors of numbers, for instance most machine learning models, or vector databases for fast similarity search. These require converting the data to numbers. While this conversion
Externí odkaz:
http://arxiv.org/abs/2312.09634
Autor:
Black, Sid, Sharkey, Lee, Grinsztajn, Leo, Winsor, Eric, Braun, Dan, Merizian, Jacob, Parker, Kip, Guevara, Carlos Ramón, Millidge, Beren, Alfour, Gabriel, Leahy, Connor
Mechanistic interpretability aims to explain what a neural network has learned at a nuts-and-bolts level. What are the fundamental primitives of neural network representations? Previous mechanistic descriptions have used individual neurons or their l
Externí odkaz:
http://arxiv.org/abs/2211.12312
While deep learning has enabled tremendous progress on text and image datasets, its superiority on tabular data is not clear. We contribute extensive benchmarks of standard and novel deep learning methods as well as tree-based models such as XGBoost
Externí odkaz:
http://arxiv.org/abs/2207.08815
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the SARS-CoV-2 pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling prov
Externí odkaz:
http://arxiv.org/abs/2006.02985
Autor:
Thin, Achille, Kotelevskii, Nikita, Denain, Jean-Stanislas, Grinsztajn, Leo, Durmus, Alain, Panov, Maxim, Moulines, Eric
In this contribution, we propose a new computationally efficient method to combine Variational Inference (VI) with Markov Chain Monte Carlo (MCMC). This approach can be used with generic MCMC kernels, but is especially well suited to \textit{MetFlow}
Externí odkaz:
http://arxiv.org/abs/2002.12253
Publikováno v:
36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks
36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, Nov 2022, New Orleans, United States
36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, Nov 2022, New Orleans, United States
International audience; While deep learning has enabled tremendous progress on text and image datasets, its superiority on tabular data is not clear. We contribute extensive benchmarks of standard and novel deep learning methods as well as tree-based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______165::9be4b1c90d12f17516fbdba26c662f58
https://hal.science/hal-03723551
https://hal.science/hal-03723551
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.