118 citations · 229 across the 23 of their papers we have counts for
15 papers · 1 filter
Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels
Ke Wang, Guillermo Ortiz-Jimenez, Rodolphe Jenatton +3
Label noise is a pervasive problem in deep learning that often compromises the generalization performance of trained models. Recently, leveraging privileged information (PI) -- inf…
When does Privileged Information Explain Away Label Noise?
Guillermo Ortiz-Jimenez, Mark Collier, Anant Nawalgaria +4
Leveraging privileged information (PI), or features available during training but not at test time, has recently been shown to be an effective method for addressing label noise. Ho…
Massively Scaling Heteroscedastic Classifiers
Mark Collier, Rodolphe Jenatton, Basil Mustafa +3
Heteroscedastic classifiers, which learn a multivariate Gaussian distribution over prediction logits, have been shown to perform well on image classification problems with hundreds…
Plex: Towards Reliability using Pretrained Large Model Extensions
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry +23
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. P…
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
Mark Collier, Rodolphe Jenatton, Efi Kokiopoulou +1
Supervised learning datasets often have privileged information, in the form of features which are available at training time but are not available at test time e.g. the ID of the a…
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado, Neil Band, Mark Collier +23
High-quality estimates of uncertainty and robustness are crucial for numerous real-world applications, especially for deep learning which underlies many deployed ML systems. The ab…
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Mark Collier, Basil Mustafa, Efi Kokiopoulou +2
Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label…
Routing Networks with Co-training for Continual Learning
Mark Collier, Efi Kokiopoulou, Andrea Gesmundo +1
The core challenge with continual learning is catastrophic forgetting, the phenomenon that when neural networks are trained on a sequence of tasks they rapidly forget previously le…
VAEs in the Presence of Missing Data
Mark Collier, Alfredo Nazabal, Christopher K. I. Williams
Real world datasets often contain entries with missing elements e.g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests. Variational Autoencode…
A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise
Mark Collier, Basil Mustafa, Efi Kokiopoulou +2
Datasets with noisy labels are a common occurrence in practical applications of classification methods. We propose a simple probabilistic method for training deep classifiers under…
Scalable Deep Unsupervised Clustering with Concrete GMVAEs
Mark Collier, Hector Urdiales
Discrete random variables are natural components of probabilistic clustering models. A number of VAE variants with discrete latent variables have been developed. Training such meth…
Memory-Augmented Neural Networks for Machine Translation
Mark Collier, Joeran Beel
Memory-augmented neural networks (MANNs) have been shown to outperform other recurrent neural network architectures on a series of artificial sequence learning tasks, yet they have…
An Empirical Comparison of Syllabuses for Curriculum Learning
Mark Collier, Joeran Beel
Syllabuses for curriculum learning have been developed on an ad-hoc, per task basis and little is known about the relative performance of different syllabuses. We identify a number…
Implementing Neural Turing Machines
Mark Collier, Joeran Beel
Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural Networks, a new class of recurrent neural networks which decouple computation from memory by introducing an…
Deep Contextual Multi-armed Bandits
Mark Collier, Hector Urdiales Llorens
Contextual multi-armed bandit problems arise frequently in important industrial applications. Existing solutions model the context either linearly, which enables uncertainty driven…