1.7k citations
- Huawei Technologies (China)CN34 papers
- University of OttawaCA18 papers
- University of AlbertaCA13 papers
- Queen's UniversityCA12 papers
- McGill UniversityCA11 papers
- University of WaterlooCA11 papers
- University of TorontoCA10 papers
- University of British ColumbiaCA8 papers
- Carleton UniversityCA6 papers
- McMaster UniversityCA5 papers
- Simon Fraser UniversityCA5 papers
- York UniversityCA5 papers
20 papers · 1 filter
Reconstruction of SINR Maps from Sparse Measurements using Group Equivariant Non-Expansive Operators
Lorenzo Mario Amorosa, Francesco Conti, Nicola Quercioli +4
As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for effective resource management and…
Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning
Zijian Long, Haopeng Wang, Haiwei Dong +1
The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, pri…
Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUs
Morgan Lindsay Heisler, Linzi Xing, Ge Shi +7
Huawei Cloud users leverage LoRA (Low-Rank Adaptation) as an efficient and scalable method to fine-tune and customize large language models (LLMs) for application-specific needs. H…
MetaSel: A Test Selection Approach for Fine-tuned DNN Models
Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand +1
Deep Neural Networks (DNNs) face challenges during deployment due to covariate shift, i.e., data distribution shifts between development and deployment contexts. Fine-tuning adapts…
Distributed Learning and Inference Systems: A Networking Perspective
Hesham G. Moussa, Arashmid Akhavain, S. Maryam Hosseini +1
Machine learning models have achieved, and in some cases surpassed, human-level performance in various tasks, mainly through centralized training of static models and the use of la…
A General-Purpose Transferable Predictor for Neural Architecture Search
Fred X. Han, Keith G. Mills, Fabian Chudak +6
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors have seen widespread use in low-cost NAS and…