5 citations · 5 across the 2 of their papers we have counts for
5 papers
Dynamic Expert Sharing: Decoupling Memory from Parallelism in Mixture-of-Experts Diffusion LLMs
Hao Mark Chen, Zhiwen Mo, Royson Lee +6
Among parallel decoding paradigms, diffusion large language models (dLLMs) have emerged as a promising candidate that balances generation quality and throughput. However, their int…
Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted
Ruchika Chavhan, Ondrej Bohdal, Yongshuo Zong +2
Large-scale text-to-image diffusion models excel in generating high-quality images from textual inputs, yet concerns arise as research indicates their tendency to memorize and repl…
ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning
Ruchika Chavhan, Da Li, Timothy Hospedales
While large-scale text-to-image diffusion models have demonstrated impressive image-generation capabilities, there are significant concerns about their potential misuse for generat…
Recurrent Early Exits for Federated Learning with Heterogeneous Clients
Royson Lee, Javier Fernandez-Marques, Shell Xu Hu +6
Federated learning (FL) has enabled distributed learning of a model across multiple clients in a privacy-preserving manner. One of the main challenges of FL is to accommodate clien…
FedL2P: Federated Learning to Personalize
Royson Lee, Minyoung Kim, Da Li +4
Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those comm…