We consider the problem of monitoring and controlling a partially-observed dynamic process that spreads over a graph. This problem naturally arises in contexts such as scheduling virus tests or quarantining individuals to curb a spreading epidemic; …
Video Abstract Videos of actions are complex signals, containing rich compositional structure. Current video generation models are limited in their ability to generate such videos. To address this challenge, we introduce a generative model (AG2Vid) that can be conditioned on an Action Graph, a structure that naturally represents the dynamics of actions and interactions between objects.
Video Abstract Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning is especially compelling due to the strong expressive power induced by the network.
Video Abstract Cite the paper If you use the contents of this project, please cite our paper. @article{hagrawal2021unknown, title={Known unknowns: Learning novel concepts using exploratory reasoning-by-elimination}, author={Harsh Agrawal, Eli Meirom, Yuval Atzmon, Shie Mannor, Gal Chechik}, journal={Uncertainty in artificial intelligence}, year={2021} }
Video Abstract Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models in a collaborative way while accounting for data disparities across clients and reducing communication costs.
Video Abstract People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domains like vision and language because the long tail of new combinations dominates the distribution.
Video Abstract Training with multiple auxiliary tasks is a common practice used in deep learning for improving the performance on the main task of interest. Two main challenges arise in this multi-task learning setting: (i) Designing useful auxiliary tasks; and (ii) Combining auxiliary tasks into a single coherent loss.
Video Abstract Multi-objective optimization problems are prevalent in machine learning. These problems have a set of optimal solutions, called the Pareto front, where each point on the front represents a different trade-off between possibly conflicting objectives.
Video Abstract Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data. It has been applied effectively to domain adaptation (DA) on images and videos. It is still unknown if and how it can be leveraged for domain adaptation in 3D perception problems.
Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are represented by feature vectors, and far less emphasis has been given …