5 papers
Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design
Manisha Dubey, Rimvydas Rubavicius, N. Siddharth +1
Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior. Bayesian inverse planning provides a principled framework for such inferen…
AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
Magdalena Proszewska, N. Siddharth
Graph Neural Networks (GNNs) have advanced significantly in handling graph-structured data, but a comprehensive framework for evaluating explainability remains lacking. Existing ev…
Towards Human Motion World Models via Executable Behaviour Representations
Rimvydas Rubavicius, Manisha Dubey, N. Siddharth +1
Human motion world models should capture motion's intentionality by being executable: adaptable to different actions and capable of assessing motion quality. To achieve this, we in…
Banyan: Improved Representation Learning with Explicit Structure
Mattia Opper, N. Siddharth
We present Banyan, a model that efficiently learns semantic representations by leveraging explicit hierarchical structure. While transformers excel at scale, they struggle in low-r…
On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning
Magdalena Proszewska, Nikolay Malkin, N. Siddharth
Diffusion autoencoders (DAs) are variants of diffusion generative models that use an input-dependent latent variable to capture representations alongside the diffusion process. The…