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Found 10 papers, 1 papers with code
Uncertainty in Graph Neural Networks: A Survey
Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propose CPFT, a versatile framework that enhances recommendation confidence by integrating Conformal Prediction (CP)-based losses with CE loss during fine-tuning.
Large Language Model Adaptation for Networking
In this paper, we present NetLLM, the first LLM adaptation framework that efficiently adapts LLMs to solve networking problems.
LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication
In the first step, we optimize the seller's pricing decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes the prices of large models iteratively by reasoning customers' future rental decisions, which is able to achieve a near-optimal pricing solution.
Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models
One of the fundamental challenges confronting supervised graph outlier detection algorithms is the prevalent issue of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance.
FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models
In this paper, we propose a novel two-stage federated learning algorithm called FedMS.
ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration
The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to resource-rich edge/cloud servers for DNN inference.
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
It includes a noise-resilient local solver and a robust global aggregator.
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation
Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement.
FSVVD: A Dataset of Full Scene Volumetric Video
Recent years have witnessed a rapid development of immersive multimedia which bridges the gap between the real world and virtual space.
