About

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Benchmarks

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Subtasks

Datasets

Greatest papers with code

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

17 May 2019microsoft/interpret

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.

ACTIVE LEARNING

Sampling Bias in Deep Active Classification: An Empirical Study

IJCNLP 2019 Xtra-Computing/thundersvm

The exploding cost and time needed for data labeling and model training are bottlenecks for training DNN models on large datasets.

ACTIVE LEARNING TEXT CLASSIFICATION

modAL: A modular active learning framework for Python

2 May 2018cosmic-cortex/modAL

modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler.

ACTIVE LEARNING

libact: Pool-based Active Learning in Python

1 Oct 2017ntucllab/libact

libact is a Python package designed to make active learning easier for general users.

ACTIVE LEARNING

Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

23 Jan 2019shubhomoydas/ad_examples

In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered.

ACTIVE LEARNING ANOMALY DETECTION

Active Anomaly Detection via Ensembles

17 Sep 2018shubhomoydas/ad_examples

First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning.

ACTIVE LEARNING ANOMALY DETECTION

ALiPy: Active Learning in Python

12 Jan 2019NUAA-AL/ALiPy

Supervised machine learning methods usually require a large set of labeled examples for model training.

ACTIVE LEARNING

Active Learning for Interactive Neural Machine Translation of Data Streams

CONLL 2018 lvapeab/nmt-keras

We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation.

ACTIVE LEARNING MACHINE TRANSLATION

Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

NeurIPS 2019 google-research/meta-dataset

We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature.

ACTIVE LEARNING CONTINUAL LEARNING FEW-SHOT LEARNING IMAGE CLASSIFICATION TRANSFER LEARNING

Few-Shot Learning with Graph Neural Networks

ICLR 2018 vgsatorras/few-shot-gnn

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.

ACTIVE LEARNING FEW-SHOT LEARNING