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Learning User Representations with Hypercuboids for Recommender Systems
(2021)WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data MiningModeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifically, the key novelty behind our model is that it explicitly models user interests as a hypercuboid instead of a point in the space. In our approach, the recommendation score is learned by calculating a compositional distance between the user hypercuboid and the ...Conference Paper -
A Practical Federated Learning Framework for Small Number of Stakeholders
(2021)WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data MiningFederated Learning (FL) allows to collaboratively build machine learning models between different entities without the need for sharing or gathering the data. In FL, typically there is a global server and a set of clients (stakeholders) to build shared machine learning models. In contrast to distributed machine learning, the controller of the training process (here the global server) never sees the data of the stakeholders participating ...Conference Paper -
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LEAP: Learning Articulated Occupancy of People
(2021)Substantial progress has been made on modeling rigid 3D objects using deep implicit representations. Yet, extending these methods to learn neural models of human shape is still in its infancy. Human bodies are complex and the key challenge is to learn a representation that generalizes such that it can express body shape deformations for unseen subjects in unseen, highly-articulated, poses. To address this challenge, we introduce LEAP ...Conference Paper -
We are More than Our Joints: Predicting how 3D Bodies Move
(2021)A key step towards understanding human behavior is the prediction of 3D human motion. Successful solutions have many applications in human tracking, HCI, and graphics. Most previous work focuses on predicting a time series of future 3D joint locations given a sequence 3D joints from the past. This Euclidean formulation generally works better than predicting pose in terms of joint rotations. Body joint locations, however, do not fully ...Conference Paper -
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SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements
(2021)Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. To enable learning, the choice of representation is the key. Recent work uses neural networks to parameterize local surface elements. This approach captures locally coherent geometry and non-planar details, can deal with varying topology, and does not require registered training data. ...Conference Paper -
Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis
(2021)Designing cyber-physical systems is a complex task which requires insights at multiple abstraction levels. The choices of single components are deeply interconnected and need to be jointly studied. In this work, we consider the problem of co-designing the control algorithm as well as the platform around it. In particular, we leverage a monotone theory of codesign to formalize variations of the LQG control problem as monotone ...Conference Paper -
Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration
(2021)Registering point clouds of dressed humans to parametric human models is a challenging task in computer vision. Traditional approaches often rely on heavily engineered pipelines that require accurate manual initialization of human poses and tedious post-processing. More recently, learning-based methods are proposed in hope to automate this process. We observe that pose initialization is key to accurate registration but existing methods ...Conference Paper