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Active Learning

73 papers with code · Methodology

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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

modAL: A modular active learning framework for Python

2 May 2018modAL-python/modAL

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

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

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 GRAPH NEURAL NETWORK

Few-Shot Learning with Graph Neural Networks

10 Nov 2017vgsatorras/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 GRAPH NEURAL NETWORK

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

17 Feb 2019xialeiliu/RankIQA

Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.

ACTIVE LEARNING CROWD COUNTING IMAGE QUALITY ASSESSMENT LEARNING-TO-RANK

Self-Regulated Interactive Sequence-to-Sequence Learning

ACL 2019 joeynmt/joeynmt

Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning.

ACTIVE LEARNING MACHINE TRANSLATION

A Tutorial on Thompson Sampling

7 Jul 2017iosband/ts_tutorial

Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance.

ACTIVE LEARNING