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Found 13 papers, 5 papers with code
Dirichlet Active Learning
This work introduces Dirichlet Active Learning (DiAL), a Bayesian-inspired approach to the design of active learning algorithms.
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets
Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance.
Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images
We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images.
A Unified Theory of Dual-Process Control
We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning
We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning, as long as the measure of uncertainty properly aligns with the underlying model and the model properly reflects uncertainty in unexplored regions.
Replay and compositional computation
Replay in the brain has been viewed as rehearsal, or, more recently, as sampling from a transition model.
Graph-based Active Learning for Semi-supervised Classification of SAR Data
We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to embed SAR data into a feature space, and then construct a similarity graph from the embedded data and apply graph-based semi-supervised learning techniques.
Efficient and Reliable Overlay Networks for Decentralized Federated Learning
As such, our proposed overlay networks accelerate convergence, improve generalization, and enhance robustness to clients failures in DFL with theoretical guarantees.
Model-Change Active Learning in Graph-Based Semi-Supervised Learning
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.
Posterior Consistency of Semi-Supervised Regression on Graphs
Graph-based semi-supervised regression (SSR) is the problem of estimating the value of a function on a weighted graph from its values (labels) on a small subset of the vertices.
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations
We present a novel adaptation of active learning to graph-based semi-supervised learning (SSL) under non-Gaussian Bayesian models.
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class.
Forward Thinking: Building Deep Random Forests
We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results.
