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Found 9 papers, 3 papers with code
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things
With the continuous improvement in the computational capabilities of edge devices such as intelligent sensors in the Industrial Internet of Things, these sensors are no longer limited to mere data collection but are increasingly capable of performing complex computational tasks.
Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering
Multi-hop question answering (MHQA) poses a significant challenge for large language models (LLMs) due to the extensive knowledge demands involved.
Impromptu Cybercrime Euphemism Detection
Moreover, we propose a detection framework tailored to this problem, which employs context augmentation modeling and multi-round iterative training.
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
The labeled data is used to train a reward model that simulates human judgment and serves as an automatic evaluator of the proactiveness of LLM agents.
Small Language Models: Survey, Measurements, and Insights
Small language models (SLMs), despite their widespread adoption in modern smart devices, have received significantly less academic attention compared to their large language model (LLM) counterparts, which are predominantly deployed in data centers and cloud environments.
OMEGA: Efficient Occlusion-Aware Navigation for Air-Ground Robot in Dynamic Environments via State Space Model
These blocks efficiently extract semantic and geometric features in 3D environments with linear complexity, ensuring that the network can learn long-distance dependencies to improve prediction accuracy.
TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks
To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency.
Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUs
GPUs have become the \emph{defacto} hardware devices for accelerating Deep Neural Network (DNN) inference workloads.
On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation
We then show that task allocation with task importance for MTL (TATIM) is a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts.
