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Found 8 papers, 4 papers with code
FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs
This paper introduces FedSecurity, an end-to-end benchmark designed to simulate adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
Demonstration-guided Deep Reinforcement Learning for Coordinated Ramp Metering and Perimeter Control in Large Scale Networks
This study considers two representative control approaches: ramp metering for freeways and perimeter control for homogeneous urban roads, and we aim to develop a deep reinforcement learning (DRL)-based coordinated control framework for large-scale networks.
Heterogeneous Line Graph Transformer for Math Word Problems
We originally planned to employ existing models but realized that they processed a math word problem as a sequence or a homogeneous graph of tokens.
Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments
Unmanned aerial vehicles (UAVs) have been widely used in military warfare.
Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimation
The proposed framework consists of two major components: camera calibration and vehicle detection.
SimMER: Simple Maximization of Entropy and Rank for Self-supervised Representation Learning
With both entropy and rank maximization, our method surpasses the state-of-the-art on CIFAR-10 and Mini-ImageNet under the standard linear evaluation protocol.
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification
Combining the Pair Loss with the techniques developed by the MixMatch family, our proposed SimPLE algorithm shows significant performance gains over previous algorithms on CIFAR-100 and Mini-ImageNet, and is on par with the state-of-the-art methods on CIFAR-10 and SVHN.
