Follow
Florian Lalande
Title
Cited by
Cited by
Year
Distinguishing standard and modified gravity cosmologies with machine learning
A Peel, F Lalande, JL Starck, V Pettorino, J Merten, C Giocoli, ...
Physical Review D 100 (2), 023508, 2019
612019
On the dissection of degenerate cosmologies with machine learning
J Merten, C Giocoli, M Baldi, M Meneghetti, A Peel, F Lalande, JL Starck, ...
Monthly Notices of the Royal Astronomical Society 487 (1), 104-122, 2019
522019
Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
F Lalande, AA Trani
The Astrophysical Journal 938 (1), 18, 2022
152022
Numerical data imputation: Choose kNN over deep learning
F Lalande, K Doya
International Conference on Similarity Search and Applications, 3-10, 2022
82022
Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach
F Lalande, K Doya
arXiv preprint arXiv:2306.16906, 2023
32023
A Transformer Model for Symbolic Regression towards Scientific Discovery
F Lalande, Y Matsubara, N Chiba, T Taniai, R Igarashi, Y Ushiku
NeurIPS 2023 AI for Science Workshop, 2023
12023
mgcnn: Standard and modified gravity (MG) cosmological models classifier
A Peel, F Lalande
Astrophysics Source Code Library, ascl: 2211.007, 2022
2022
欠測データ補完アルゴリズムと機械学習による太陽系外惑星の全容解明
F Lalande
Okinawa Institute of Science and Technology Graduate University, 0
Tabular data imputation: quality over quantity
F Lalande, K Doya
The system can't perform the operation now. Try again later.
Articles 1–9