4 citations · 4 across the 4 of their papers we have counts for
4 papers
When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks
Stefano Ferraro, Akihiro Nakano, Masahiro Suzuki +1
Object-centric world models (OCWM) aim to decompose visual scenes into object-level representations, providing structured abstractions that could improve compositional generalizati…
Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases
Cristian Meo, Akihiro Nakano, Mircea Lică +7
Unsupervised object-centric learning from videos is a promising approach towards learning compositional representations that can be applied to various downstream tasks, such as pre…
Masked Generative Priors Improve World Models Sequence Modelling Capabilities
Cristian Meo, Mircea Lica, Zarif Ikram +6
Deep Reinforcement Learning (RL) has become the leading approach for creating artificial agents in complex environments. Model-based approaches, which are RL methods with world mod…
Cross-Task Consistency Learning Framework for Multi-Task Learning
Akihiro Nakano, Shi Chen, Kazuyuki Demachi
Multi-task learning (MTL) is an active field in deep learning in which we train a model to jointly learn multiple tasks by exploiting relationships between the tasks. It has been s…