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Found 12 papers, 5 papers with code
Graph-based Simulation Framework for Power Resilience Estimation and Enhancement
The increasing frequency of extreme weather events poses significant risks to power distribution systems, leading to widespread outages and severe economic and social consequences.
Deep Uncertainty-Based Explore for Index Construction and Retrieval in Recommendation System
In recommendation systems, the relevance and novelty of the final results are selected through a cascade system of Matching -> Ranking -> Strategy.
Contingency Detection in Modern Power Systems: A Stochastic Hybrid System Method
The framework uses stochastic hybrid system representations in state space models to expand and facilitate capability of contingency detection.
Stochastic Hybrid System Modeling and State Estimation of Modern Power Systems under Contingency
This paper formulates stochastic hybrid system models for MPSs, introduces coordinated observer design algorithms for state estimation, and establishes their convergence and reliability properties.
Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising
These methods typically involve the training of calibrators using a validation set and subsequently applying these calibrators to correct the original estimated values during online inference.
A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model
2) Prompt model is designed to generate individualized creatives for different user groups, which can further improve the diversity and quality.
Joint Network Function Placement and Routing Optimization in Dynamic Software-defined Satellite-Terrestrial Integrated Networks
However, low latency service provisioning is still challenging due to the fast variation of network topology and limited onboard resource at low earth orbit satellites.
Exploring Model Dynamics for Accumulative Poisoning Discovery
In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information.
Tracking performance of PID for nonlinear stochastic systems
In this paper, we will consider a class of continuous-time stochastic control systems with both unknown nonlinear structure and unknown disturbances, and investigate the capability of the classical proportional-integral-derivative(PID) controller in tracking time-varying reference signals.
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems.
Secure and Efficient Federated Learning Through Layering and Sharding Blockchain
Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has attracted widespread attention as a new decentralized learning pattern.
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
