3.7k citations
- Meta (United States)US158 papers
- New York UniversityUS11 papers
- Carnegie Mellon UniversityUS10 papers
- University of California, BerkeleyUS8 papers
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- University of WashingtonUS7 papers
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- Massachusetts Institute of TechnologyUS5 papers
- Meta (Israel)IL5 papers
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- Google (United States)US4 papers
179 papers
LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation
Egemen Erbayat, Luis Duque, Sohini Roychowdhury +2
Industrial ad recommendation models rely heavily on sparse, high-cardinality ID-list features that encode user histories and contextual identifiers. Each is backed by a dedicated e…
CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs
A. Chervov, M. Obozov, A. Soibelman +31
This paper is the second in a series of studies on developing efficient artificial intelligence-based approaches to pathfinding on extremely large graphs (e.g. nodes) wit…
WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds
Peizhuo Li, Sebastian Starke, Yuting Ye +1
We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike…
Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media
Jorge Condor, Sebastien Speierer, Lukas Bode +4
Efficient scene representations are essential for many computer graphics applications. A general unified representation that can handle both surfaces and volumes simultaneously, re…
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection
Ivo Verhoeven, Pushkar Mishra, Rahel Beloch +2
Community models for malicious content detection, which take into account the context from a social graph alongside the content itself, have shown remarkable performance on benchma…
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations
Filippo Betello, Federico Siciliano, Pushkar Mishra +1
Sequential Recommender Systems (SRSs) are widely employed to model user behavior over time. However, their robustness in the face of perturbations in training data remains a largel…