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Found 16 papers, 2 papers with code
A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation
Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions.
PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks
Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs).
Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning
Federated continual learning (FCL) learns incremental tasks over time from confidential datasets distributed across clients.
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory- and computing-constrained devices.
Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning
Moreover, to improve data efficiency and provide better generalization performance, we train the policy model with augmented data (e. g., noisy BSM and noisy surveillance images).
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer
Further, the proposed FSSDA can be effectively generalized to multi-source domain adaptation scenarios.
Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection
Specifically, GLA integrates an early-stage fusion via a local attention network and a late-stage fusion via a global attention network to deal with both local and global information, which automatically allocates higher weights to the modality with better detection features at the late-stage fusion to cope with the specific weather condition adaptively.
Membership Inference Attacks and Defenses in Neural Network Pruning
We first explore the impact of neural network pruning on prediction divergence, where the pruning process disproportionately affects the pruned model's behavior for members and non-members.
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models
To achieve knowledge transfer across these heterogeneous on-device models, a zero-shot distillation approach is designed without any prerequisites for private on-device data, which is contrary to certain prior research based on a public dataset or a pre-trained data generator.
A Vertical Federated Learning Framework for Horizontally Partitioned Labels
To address these challenges, we propose a novel vertical federated learning framework named Cascade Vertical Federated Learning (CVFL) to fully utilize all horizontally partitioned labels to train neural networks with privacy-preservation.
ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles
The experimental results reveal the severity of ES Attack: i) ES Attack successfully steals the victim model without data hurdles, and ES Attack even outperforms most existing model stealing attacks using auxiliary data in terms of model accuracy; ii) most countermeasures are ineffective in defending ES Attack; iii) ES Attack facilitates further attacks relying on the stolen model.
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks
In this paper, we present a web event forecasting approach, DeepEvent, in enterprise web applications for better anomaly detection.
Generalized Batch Normalization: Towards Accelerating Deep Neural Networks
Utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of Batch Normalization (BN) that offers accelerated convergence of Neural Network training compared to conventional BN.
Adaptive Adversarial Attack on Scene Text Recognition
We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e. g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks.
Adversarial Examples: Attacks and Defenses for Deep Learning
In this paper, we review recent findings on adversarial examples for deep neural networks, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods.
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead.
