Search Results for author:
Found 4 papers, 4 papers with code
Simulation-Free Training of Neural ODEs on Paired Data
In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data.
Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
Learning compositional representation is a key aspect of object-centric learning as it enables flexible systematic generalization and supports complex visual reasoning.
Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers
However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregressive process.
Ranked #35 on
Video Generation
on UCF-101
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales.
Ranked #3 on
Point Cloud Generation
on ShapeNet Car
