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Found 18 papers, 6 papers with code
AIArena: A Blockchain-Based Decentralized AI Training Platform
The rapid advancement of AI has underscored critical challenges in its development and implementation, largely due to centralized control by a few major corporations.
SoK: Decentralized AI (DeAI)
The centralization of Artificial Intelligence (AI) poses significant challenges, including single points of failure, inherent biases, data privacy concerns, and scalability issues.
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning
One of the biggest challenges of building artificial intelligence (AI) model in healthcare area is the data sharing.
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach
Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous Glucose Monitoring (CGM), presenting a significant "cold start" problem in patient care.
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments.
From Sora What We Can See: A Survey of Text-to-Video Generation
With impressive achievements made, artificial intelligence is on the path forward to artificial general intelligence.
CT-NeRF: Incremental Optimizing Neural Radiance Field and Poses with Complex Trajectory
In this pipeline, we first propose a local-global bundle adjustment under a pose graph connecting neighboring frames to enforce the consistency between poses to escape the local minima caused by only pose consistency with the scene structure.
Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration
In the paper, we propose to 1) incorporate frontier-based exploration tasks for global coverage with implicit surface uncertainty-based reconstruction tasks to achieve high-quality reconstruction.
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks.
GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series
Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality of life.
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling
The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services (only one passenger can be assigned to a car at a time like UberX and Lyft).
Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs
In this paper, we propose Graphene, an advanced system designed to provide a detailed analysis of the security posture of computing infrastructures.
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning
Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator.
Defending Against Poisoning Attacks in Federated Learning with Blockchain
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy.
Collective Knowledge Graph Completion with Mutual Knowledge Distillation
Knowledge graph completion (KGC), the task of predicting missing information based on the existing relational data inside a knowledge graph (KG), has drawn significant attention in recent years.
Superpoint Transformer for 3D Scene Instance Segmentation
The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances.
Ranked #6 on
3D Instance Segmentation
on ScanNet(v2)
FLock: Defending Malicious Behaviors in Federated Learning with Blockchain
Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy.
Edge Security: Challenges and Issues
Edge computing is a paradigm that shifts data processing services to the network edge, where data are generated.
