Genre
Towards a theory of how the structure of language is acquired by deep neural networks
How much data is required to learn the structure of a language via next-token prediction?
InsActor: Instruction-driven Physics-based Characters
Our framework empowers InsActor to capture complex relationships between high-level human instructions and character motions by employing diffusion policies for flexibly conditioned motion planning.
QT-ViT: Improving Linear Attention in ViT with Quadratic Taylor Expansion
In this paper, we propose QT -ViT models that improve the previous linear self-attention using quadratic Taylor expansion.
Equivariant flow matching
Our main contributions in this work are as follows: 1.
GACL: Exemplar-Free Generalized Analytic Continual Learning
Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks.
