4 citations · 14 across the 21 of their papers we have counts for
9 papers · 1 filter
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…
Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning
Taehoon Kim, Henry Gouk, Minyoung Kim +1
Certifying the IID generalisation ability of deep networks is the first of many requirements for trusting AI in high-stakes applications from medicine to security. However, when in…
FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization
Hao Mark Chen, Shell Xu Hu, Wayne Luk +2
Model merging has emerged as a promising approach for multi-task learning (MTL), offering a data-efficient alternative to conventional fine-tuning. However, with the rapid developm…
Weight Space Learning for Certifiable Few-shot Transfer Learning
Fady Rezk, Royson Lee, Henry Gouk +2
In contemporary deep learning, a prevalent and effective workflow for solving low-data problems is adapting powerful pre-trained foundation models (FMs) to new tasks via parameter-…
A Bayesian Approach to Data Point Selection
Xinnuo Xu, Minyoung Kim, Royson Lee +2
Data point selection (DPS) is becoming a critical topic in deep learning due to the ease of acquiring uncurated training data compared to the difficulty of obtaining curated or pro…
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…
VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning
Yongshuo Zong, Ondrej Bohdal, Timothy Hospedales
Large language models (LLMs) famously exhibit emergent in-context learning (ICL) -- the ability to rapidly adapt to new tasks using few-shot examples provided as a prompt, without…
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
Yongshuo Zong, Ondrej Bohdal, Tingyang Yu +2
Current vision large language models (VLLMs) exhibit remarkable capabilities yet are prone to generate harmful content and are vulnerable to even the simplest jailbreaking attacks.…
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
Yongshuo Zong, Tingyang Yu, Ruchika Chavhan +2
Large language and vision-language models are rapidly being deployed in practice thanks to their impressive capabilities in instruction following, in-context learning, and so on. T…