Timothy Hospedales

26

Papers

14

Total Citations

Papers (26)

SketchINR: A First Look into Sketches as Implicit Neural Representations

A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning

Fool Your (Vision and) Language Model with Embarrassingly Simple Permutations

Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models

Recurrent Early Exits for Federated Learning with Heterogeneous Clients

Making Better Use of Edges via Perceptual Grouping

Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style

An Erudite Fine-Grained Visual Classification Model

Meta Omnium: A Benchmark for General-Purpose Learning-To-Learn

On-the-Fly Category Discovery

Quality Diversity for Visual Pre-Training

Task-aware Adaptive Learning for Cross-domain Few-shot Learning

Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation

Learning to Generate Novel Domains for Domain Generalization

DADA: Differentiable Automatic Data Augmentation

BézierSketch: A generative model for scalable vector sketches

FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization

DemoFusion: Democratising High-Resolution Image Generation With No $$$

What the Vec? Towards Probabilistically Grounded Embeddings

Multi-relational Poincaré Graph Embeddings

Online Meta-Critic Learning for Off-Policy Actor-Critic Methods

EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

FedL2P: Federated Learning to Personalize

BayesTune: Bayesian Sparse Deep Model Fine-tuning

Analogies Explained: Towards Understanding Word Embeddings

Feature-Critic Networks for Heterogeneous Domain Generalization