In this paper, we present AGRoL, a novel conditional diffusion model specially purposed to track full bodies given sparse upper-body tracking signals. Our model uses a simple...
In this paper, we present a framework for exploration in large-scale recommender systems to address these challenges. It consists of three parts, first the user-creator...
In this work, we introduce CAM2, a conformity-aware multi-task ranking model to serve relevant items to users on one of the largest industrial recommendation platforms.
In this paper, we propose a novel method which introduces diversity by modeling the impact of low diversity on user’s engagement on individual items, thus being able to account...
In this paper we present Que2Engage, a search EBR system designed to bridge the gap between retrieval and ranking for better end-to-end optimization. Que2Engage takes a...
In this paper we present HierCat, the query categorization system at Facebook Marketplace. HierCat addresses these challenges by leveraging multi-task pre-training of...
In this paper, we present the Carbon Explorer framework to analyze the solution space. We use Carbon Explorer to balance tradeoffs between operational and embodied carbon...
This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in...
In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i.e., block) level. We first perform empirical...
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to...
