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Found 27 papers, 8 papers with code
Rule Based Event Extraction for Artificial Social Intelligence
Natural language (as opposed to structured communication modes such as Morse code) is by far the most common mode of communication between humans, and can thus provide significant insight into both individual mental states and interpersonal dynamics.
Privacy-Preserving Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI services without relying on remote servers.
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning
In response to this, inspired by the idea that the functions of the brain are shaped by its geometric structure, this paper integrates this idea into LoRA technology and proposes a new matrix transformation-based reparameterization method for efficient fine-tuning, named Matrix-Transformation based Low-Rank Adaptation (MTLoRA).
Poly Kernel Inception Network for Remote Sensing Detection
Object detection in remote sensing images (RSIs) often suffers from several increasing challenges, including the large variation in object scales and the diverse-ranging context.
Brain-inspired and Self-based Artificial Intelligence
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
A Brain-inspired Computational Model for Human-like Concept Learning
Concept learning is a fundamental aspect of human cognition and plays a critical role in mental processes such as categorization, reasoning, memory, and decision-making.
Logits Poisoning Attack in Federated Distillation
Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cross-device knowledge transfer in federated learning.
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data.
Improving Communication Efficiency of Federated Distillation via Accumulating Local Updates
ALU drastically decreases the frequency of communication in federated distillation, thereby significantly reducing the communication overhead during the training process.
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy.
Federated Skewed Label Learning with Logits Fusion
By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category.
Predictable Relative Forward Performance Processes: Multi-Agent and Mean Field Games for Portfolio Management
We consider a new framework of predictable relative forward performance processes (PRFPP) to study portfolio management within a competitive environment.
STREAM: Social data and knowledge collective intelligence platform for TRaining Ethical AI Models
This paper presents Social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models "follow good advice as naturally as a stream follows its course".
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout
In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss.
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation.
Knowledge Distillation in Federated Edge Learning: A Survey
The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data.
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC.
Towards Federated Learning against Noisy Labels via Local Self-Regularization
Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner.
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.
Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning
For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge.
Channel Pruned YOLOv5-based Deep Learning Approach for Rapid and Accurate Outdoor Obstacles Detection
To demonstrate the practicality of the pruning method, we select the YOLOv5 model for experiments and provide a data set of outdoor obstacles to show the effect of model.
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation
Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between the server and clients, supporting heterogeneous local models while significantly reducing communication overhead.
COVID-19 Detection Using CT Image Based On YOLOv5 Network
The representational model are Faster RCNN and YOLO series.
Predictable Forward Performance Processes: Infrequent Evaluation and Applications to Human-Machine Interactions
We study discrete-time predictable forward processes when trading times do not coincide with performance evaluation times in a binomial tree model for the financial market.
DR 21 South Filament: a Parsec-sized Dense Gas Accretion Flow onto the DR 21 Massive Young Cluster
DR21 south filament (DR21SF) is a unique component of the giant network of filamentary molecular clouds in the north region of Cygnus X complex.
Astrophysics of Galaxies
Constrained Non-Affine Alignment of Embeddings
Embeddings are already essential tools for large language models and image analysis, and their use is being extended to many other research domains.
A Data-Center FPGA Acceleration Platform for Convolutional Neural Networks
Intensive computation is entering data centers with multiple workloads of deep learning.
