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07/06/2023
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?
Numerous benchmarks for Few-Shot Learning have been proposed in the last...
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05/12/2023
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Meta-learning and other approaches to few-shot learning are widely studi...
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04/17/2023
Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
An indigenous perspective on the effectiveness of debiasing techniques f...
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02/24/2023
Amortised Invariance Learning for Contrastive Self-Supervision
Contrastive self-supervised learning methods famously produce high quali...
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08/01/2022
Attacking Adversarial Defences by Smoothing the Loss Landscape
This paper investigates a family of methods for defending against advers...
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07/17/2022
HyperInvariances: Amortizing Invariance Learning
Providing invariances in a given learning task conveys a key inductive b...
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06/15/2022
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Although deep neural networks are capable of achieving performance super...
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03/05/2022
Meta Mirror Descent: Optimiser Learning for Fast Convergence
Optimisers are an essential component for training machine learning mode...
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02/01/2022
Finding lost DG: Explaining domain generalization via model complexity
The domain generalization (DG) problem setting challenges a model traine...
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11/22/2021
Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks
Self-supervised learning is a powerful paradigm for representation learn...
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10/18/2021
Self-Supervised Representation Learning: Introduction, Advances and Challenges
Self-supervised representation learning methods aim to provide powerful ...
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10/09/2021
Active Altruism Learning and Information Sufficiency for Autonomous Driving
Safe interaction between vehicles requires the ability to choose actions...
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02/27/2021
Searching for Robustness: Loss Learning for Noisy Classification Tasks
We present a "learning to learn" approach for automatically constructing...
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01/08/2021
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
Current state-of-the-art few-shot learners focus on developing effective...
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11/26/2020
How Well Do Self-Supervised Models Transfer?
Self-supervised visual representation learning has seen huge progress in...
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10/17/2020
A Stochastic Neural Network for Attack-Agnostic Adversarial Robustness
Stochastic Neural Networks (SNNs) that inject noise into their hidden la...
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09/10/2020
Resolving Conflict in Decision-Making for Autonomous Driving
Recent work on decision making and planning for autonomous driving has m...
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07/14/2020
Altruistic Decision-Making for Autonomous Driving with Sparse Rewards
In order to drive effectively, a driver must be aware of how they can ex...
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06/22/2020
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights
In the absence of large labelled datasets, self-supervised learning tech...
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02/19/2020
Distance-Based Regularisation of Deep Networks for Fine-Tuning
We investigate approaches to regularisation during fine-tuning of deep n...
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10/17/2019
Deep clustering with concrete k-means
We address the problem of simultaneously learning a k-means clustering a...
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01/23/2019
Stochastic Gradient Trees
We present an online algorithm that induces decision trees using gradien...
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04/16/2018
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes
Effective regularisation of neural networks is essential to combat overf...
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04/12/2018
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
We investigate the effect of explicitly enforcing the Lipschitz continui...
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11/19/2015
