no code implementations • 21 Aug 2024 • Félix Chavelli, Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low, Stéphane Bressan
The modeling of dynamical systems is a pervasive concern for not only describing but also predicting and controlling natural phenomena and engineered systems.
1 code implementation • 12 Jan 2024 • Pratik Karmakar, Mikaël Monet, Pierre Senellart, Stéphane Bressan
Shapley values, originating in game theory and increasingly prominent in explainable AI, have been proposed to assess the contribution of facts in query answering over databases, along with other similar power indices such as Banzhaf values.
1 code implementation • 19 Dec 2023 • Zi-Yu Khoo, Gokul Rajiv, Abel Yang, Jonathan Sze Choong Low, Stéphane Bressan
Can a machine or algorithm discover or learn the elliptical orbit of Mars from astronomical sightings alone?
no code implementations • 15 Dec 2023 • Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
We present and comparatively and empirically evaluate the eight methods to compute the mixed partial derivative of a surrogate function.
1 code implementation • 15 Dec 2023 • Zi-Yu Khoo, Abel Yang, Jonathan Sze Choong Low, Stéphane Bressan
Can a machine or algorithm discover or learn Kepler's first law from astronomical sightings alone?
no code implementations • 14 Dec 2023 • Zi-Yu Khoo, Delong Zhang, Stéphane Bressan
We present several methods for predicting the dynamics of Hamiltonian systems from discrete observations of their vector field.
1 code implementation • 3 Sep 2023 • Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low, Stéphane Bressan
Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations.
no code implementations • 1 Apr 2022 • Fangyi Zhu, Lok You Tan, See-Kiong Ng, Stéphane Bressan
Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks.
no code implementations • 1 Apr 2022 • Fangyi Zhu, See-Kiong Ng, Stéphane Bressan
We present an outlook attention mechanism, COOL, for natural language processing.
1 code implementation • 4 May 2018 • Debabrota Basu, Pierre Senellart, Stéphane Bressan
BelMan alternates \emph{information projection} and \emph{reverse information projection}, i. e., projection of the pseudobelief-reward onto beliefs-rewards to choose the arm to play, and projection of the resulting beliefs-rewards onto the pseudobelief-reward.