no code implementations • 20 Oct 2024 • Tianyi Xu, Patrick Zheng, Shiyan Liu, Sicheng Lyu, Isabeau Prémont-Schwarz
Artificial Neural Networks (ANNs) suffer from catastrophic forgetting, where the learning of new tasks causes the catastrophic forgetting of old tasks.
no code implementations • 11 Oct 2024 • Jeremy Lucas, Isabeau Prémont-Schwarz
This paper presents the Articulated Animal AI Environment for Animal Cognition, an enhanced version of the previous AnimalAI Environment.
no code implementations • 11 Oct 2024 • Taahaa Mir, Peipei Yao, Kateri Duranceau, Isabeau Prémont-Schwarz
This paper investigates whether the hexagonal structure of grid cells provides any performance benefits or if it merely represents a biologically convenient configuration.
no code implementations • 28 Jan 2022 • Isabeau Prémont-Schwarz, Jaroslav Vítků, Jan Feyereisl
Even if learned optimizers (L2Os) eventually outpace hand-crafted ones in practice however, they are still not provably convergent and might fail out of distribution.
1 code implementation • 2 Jun 2020 • Quentin Cappart, Thierry Moisan, Louis-Martin Rousseau, Isabeau Prémont-Schwarz, Andre Cire
In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems.
no code implementations • NeurIPS 2017 • Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hotloo Hao, Antti Rasmus, Rinu Boney, Harri Valpola
We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models.