277 citations · 1.7k across the 70 of their papers we have counts for
20 papers · 1 filter
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.…
ChiroDiff: Modelling chirographic data with Diffusion Models
Ayan Das, Yongxin Yang, Timothy Hospedales +2
Generative modelling over continuous-time geometric constructs, a.k.a such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Suc…
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Qishi Dong, Awais Muhammad, Fengwei Zhou +5
Recent advances on large-scale pre-training have shown great potentials of leveraging a large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) generalizatio…
MEDFAIR: Benchmarking Fairness for Medical Imaging
Yongshuo Zong, Yongxin Yang, Timothy Hospedales
A multitude of work has shown that machine learning-based medical diagnosis systems can be biased against certain subgroups of people. This has motivated a growing number of bias m…
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection
Xiongjie Chen, Yunpeng Li, Yongxin Yang
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-worl…
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many h…
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error
Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
Calibration of neural networks is a topical problem that is becoming more and more important as neural networks increasingly underpin real-world applications. The problem is especi…
Flexible Dataset Distillation: Learn Labels Instead of Images
Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
We study the problem of dataset distillation - creating a small set of synthetic examples capable of training a good model. In particular, we study the problem of label distillatio…
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Wei Zhou, Yiying Li, Yongxin Yang +2
Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks. Normally, the critic's action-value function is updated using temporal-di…
Deep clustering with concrete k-means
Boyan Gao, Yongxin Yang, Henry Gouk +1
We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential of deep k-mea…
Feature-Critic Networks for Heterogeneous Domain Generalization
Yiying Li, Yongxin Yang, Wei Zhou +1
The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques allev…
Deep Neural Decision Trees
Yongxin Yang, Irene Garcia Morillo, Timothy M. Hospedales
Deep neural networks have been proven powerful at processing perceptual data, such as images and audio. However for tabular data, tree-based models are more popular. A nice propert…
Learning to Generalize: Meta-Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song +1
Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generaliz…
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning
Flood Sung, Li Zhang, Tao Xiang +2
We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can…
Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives
Yongxin Yang, Timothy M. Hospedales
Multi-domain learning aims to benefit from simultaneously learning across several different but related domains. In this chapter, we propose a single framework that unifies multi-d…
Trace Norm Regularised Deep Multi-Task Learning
Yongxin Yang, Timothy M. Hospedales
We propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that each neural network is…
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
Yongxin Yang, Timothy Hospedales
Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task…
Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian
Yongxin Yang, Timothy Hospedales
Most visual recognition methods implicitly assume the data distribution remains unchanged from training to testing. However, in practice domain shift often exists, where real-world…
Transductive Multi-class and Multi-label Zero-shot Learning
Yanwei Fu, Yongxin Yang, Timothy M. Hospedales +2
Recently, zero-shot learning (ZSL) has received increasing interest. The key idea underpinning existing ZSL approaches is to exploit knowledge transfer via an intermediate-level se…
Transductive Multi-label Zero-shot Learning
Yanwei Fu, Yongxin Yang, Tim Hospedales +2
Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These me…