Search Results for author:
Found 11 papers, 2 papers with code
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
To address these limitations, we propose federated full-parameter tuning at scale for LLMs (Ferret), the first first-order method with shared randomness to enable scalable full-parameter tuning of LLMs across decentralized data sources while maintaining competitive model accuracy.
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting
Extensive experimental results on generating million-level point cloud data validate the effectiveness of our method, substantially improving the quality of colored point clouds and demonstrating significant potential for applications involving large-scale point clouds in autonomous robotics and human-robot interaction scenarios.
Flexora: Flexible Low Rank Adaptation for Large Language Models
Thus, fine-tuning techniques, especially the widely used Low-Rank Adaptation (LoRA) method, have been introduced to expand the boundaries on these tasks, whereas LoRA would underperform on certain tasks owing to its potential overfitting on these tasks.
Learn To Learn More Precisely
Then in the outer loop, MSD utilizes the same query data to optimize the consistency of learned knowledge, enhancing the model's ability to learn more precisely.
Generative Technology for Human Emotion Recognition: A Scope Review
Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems.
VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models
Visual Language Models (VLMs) have rapidly progressed with the recent success of large language models.
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations
First-order optimization (FOO) algorithms are pivotal in numerous computational domains such as machine learning and signal denoising.
Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence
In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence.
A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability
Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation.
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation
The main idea of multimodal recommendation is the rational utilization of the item's multimodal information to improve the recommendation performance.
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis
A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels.
