46 citations · 167 across the 19 of their papers we have counts for
7 papers · 1 filter
Exploiting Problem Structure in Deep Declarative Networks: Two Case Studies
Stephen Gould, Dylan Campbell, Itzik Ben-Shabat +2
Deep declarative networks and other recent related works have shown how to differentiate the solution map of a (continuous) parametrized optimization problem, opening up the possib…
A Regularized Wasserstein Framework for Graph Kernels
Asiri Wijesinghe, Qing Wang, Stephen Gould
We propose a learning framework for graph kernels, which is theoretically grounded on regularizing optimal transport. This framework provides a novel optimal transport distance met…
Conditional Generative Modeling via Learning the Latent Space
Sameera Ramasinghe, Kanchana Ranasinghe, Salman Khan +2
Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-mod…
Contextually Plausible and Diverse 3D Human Motion Prediction
Sadegh Aliakbarian, Fatemeh Sadat Saleh, Lars Petersson +2
We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular…
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes
Sameera Ramasinghe, Salman Khan, Nick Barnes +1
Existing networks directly learn feature representations on 3D point clouds for shape analysis. We argue that 3D point clouds are highly redundant and hold irregular (permutation-i…
Learning Variations in Human Motion via Mix-and-Match Perturbation
Mohammad Sadegh Aliakbarian, Fatemeh Sadat Saleh, Mathieu Salzmann +3
Human motion prediction is a stochastic process: Given an observed sequence of poses, multiple future motions are plausible. Existing approaches to modeling this stochasticity typi…