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Found 10 papers, 2 papers with code
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models.
Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach
Then, we develop a two-pathway data-driven model based on Bayesian Gaussian mixture models that predicts the solution to a typical problem by two different pre-fault and post-fault prediction pathways.
Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics
Then, the performance of the reward function is assessed, and the results are presented back to the LLM for guiding its self-refinement process.
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
Motivated by the substantial achievements observed in Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges in robotics.
Data-Driven Leader-following Consensus for Nonlinear Multi-Agent Systems against Composite Attacks: A Twins Layer Approach
Consequently, the resilient control task against CAs can be divided into two parts: One is distributed estimation against DoS attacks on the TL and the other is resilient decentralized tracking control against actuation attacks on the CPL.
Learning Optimal Topology for Ad-hoc Robot Networks
This model is an stacked ensemble whose output is the topology prediction for a particular robot.
Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
In this paper, we develop a safe decision-making method for self-driving cars in a multi-lane, single-agent setting.
Distributed Time- and Event-Triggered Observers for Linear Systems: Non-Pathological Sampling and Inter-Event Dynamics
For an autonomous linear time-invariant (LTI) system, a distributed observer with time-triggered periodic observations and event-triggered communication is proposed to estimate the state of the system.
Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Model
At the SP, The MoG model estimates the noise added to perturbed ratings and the MF algorithm predicts missing ratings.
Model-free optimal control of discrete-time systems with additive and multiplicative noises
This paper investigates the optimal control problem for a class of discrete-time stochastic systems subject to additive and multiplicative noises.
