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Found 10 papers, 1 papers with code
Towards More Efficient Depression Risk Recognition via Gait
Gait is a complex form of motion, and hand-crafted gait features often only capture a fraction of the intricate associations between gait and depression risk.
Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortex
While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains underexplored.
A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge
However, many edge devices struggle to boost inference throughput of various quantized DNNs due to the varying quantization levels, and these devices lack floating-point (FP) support for on-device learning, which prevents them from improving model accuracy while ensuring data privacy.
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT
A tip selection algorithm based on the cosine similarity of model parameters is then designed to aggregate models from clients with similar distributions.
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication
Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new federated learning framework which updates model from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT).
Knowledge Distillation via Instance-level Sequence Learning
Recently, distillation approaches are suggested to extract general knowledge from a teacher network to guide a student network.
Auction Based Clustered Federated Learning in Mobile Edge Computing System
In recent years, mobile clients' computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally.
High performance reconciliation for practical quantum key distribution systems
Quantum key distribution (QKD) is a promising technique for secure communication based on quantum mechanical principles.
Quantum Physics
Multi-level Similarity Learning for Low-Shot Recognition
Low-shot learning indicates the ability to recognize unseen objects based on very limited labeled training samples, which simulates human visual intelligence.
Few-shot Learning for Domain-specific Fine-grained Image Classification
Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications.
