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Found 13 papers, 6 papers with code
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning.
PAC-Bayes meta-learning with implicit task-specific posteriors
We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning.
Instance-Dependent Noisy Label Learning via Graphical Modelling
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them.
Ranked #1 on
Learning with noisy labels
on CIFAR-100
Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function.
Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories
We develop an Approximate Bayesian Computation approach that draws samples from the posterior distribution over the model's transition and duration parameters given aggregate counts from a specific location, thus adapting the model to a region or individual hospital site of interest.
Unsupervised Task Design to Meta-Train Medical Image Classifiers
Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i. e., classifiers modeled with small training sets).
Similarity of Classification Tasks
Recent advances in meta-learning has led to remarkable performances on several few-shot learning benchmarks.
Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean
Specifically, we describe adaptations for scenarios with 2D and 3D inputs, uni and multi-label classification, and class distribution mismatch between labeled and unlabeled portions of the training data.
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
In this paper, we analyse the meta-learning algorithm and propose new criteria to characterise the utility of the validation set, based on: 1) the informativeness of the validation set; 2) the class distribution balance of the set; and 3) the correctness of the labels of the set.
Task Weighting in Meta-learning with Trajectory Optimisation
Developing meta-learning algorithms that are un-biased toward a subset of training tasks often requires hand-designed criteria to weight tasks, potentially resulting in sub-optimal solutions.
Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach
To meet this requirement without relying on additional $2C - 2$ manual annotations per instance, we propose a method that automatically generates additional noisy labels by estimating the noisy label distribution based on nearest neighbours.
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
To address this limitation, we propose a novel peer-agreement based sample selection (PASS).
Noisy-label Learning with Sample Selection based on Noise Rate Estimate
Even though the estimated noise rate from the training set appears to be a natural signal to be used in the definition of this curriculum, previous approaches generally rely on arbitrary thresholds or pre-defined selection functions to the best of our knowledge.
