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Found 20 papers, 8 papers with code
Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction
Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE task.
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Further, it embeds context and leverages inter-tile dependencies to lower transfer costs with negligible overhead.
Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data
The next point-of-interest (POI) prediction is a significant task in location-based services, yet its complexity arises from the consolidation of spatial and semantic intent.
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts.
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models.
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment.
Mobile Foundation Model as Firmware
Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks.
EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models
Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in machine intelligence, owing to their exceptional capabilities in a wide range of machine learning tasks.
FwdLLM: Efficient FedLLM using Forward Gradient
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine-tuning LLMs to downstream mobile tasks, an approach known as FedLLM.
Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services
However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models.
Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives
Our key idea is to employ non-learnable primitives to extract a diverse set of task-agnostic features and recombine them into a shared branch common to all tasks and explicit task-specific branches reserved for each task.
TFormer: A Transmission-Friendly ViT Model for IoT Devices
The high performance and small number of model parameters and FLOPs of TFormer are attributed to the proposed hybrid layer and the proposed partially connected feed-forward network (PCS-FFN).
Federated Few-Shot Learning for Mobile NLP
Such an inadequacy of data labels is known as a few-shot scenario; it becomes the key blocker for mobile NLP applications.
Towards Practical Few-shot Federated NLP
To address these challenges, we first introduce a data generator for federated few-shot learning tasks, which encompasses the quantity and skewness of scarce labeled data in a realistic setting.
FedAdapter: Efficient Federated Learning for Modern NLP
A key challenge is to properly configure the depth and width of adapters, to which the training speed and efficiency is highly sensitive.
Towards Sustainable Satellite Edge Computing
Recently, Low Earth Orbit (LEO) satellites experience rapid development and satellite edge computing emerges to address the limitation of bent-pipe architecture in existing satellite systems.
Benchmarking of DL Libraries and Models on Mobile Devices
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
Hierarchical Federated Learning through LAN-WAN Orchestration
Our proposed FL can accelerate the learning process and reduce the monetary cost with frequent local aggregation in the same LAN and infrequent global aggregation on a cloud across WAN.
Federated Neural Architecture Search
To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data.
