# Machine Learning and Algorithms

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/machine-learning-and-algorithms/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers revolves around the topic of active learning in machine learning research. It covers various aspects such as semi-supervised learning, deep learning, Gaussian processes, image classification, text categorization, batch mode active learning, statistical guarantees, and human-in-the-loop approaches. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t12072 |
| Works | 29 |

## Topic papers all

Showing 15 of 29.

- [An analysis of active learning strategies for sequence labeling tasks](https://scholariq.org/papers/an-analysis-of-active-learning-strategies-for-sequence-labeling-tasks/)
- [Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models](https://scholariq.org/papers/learning-stable-nonlinear-dynamical-systems-with-gaussian-mixture-models/)
- [Recent Advances in Robot Learning from Demonstration](https://scholariq.org/papers/recent-advances-in-robot-learning-from-demonstration/)
- [Multiple-Instance Active Learning](https://scholariq.org/papers/multiple-instance-active-learning/)
- [Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally](https://scholariq.org/papers/fine-tuning-convolutional-neural-networks-for-biomedical-image-analysis-actively/)
- [Dynamic Task Prioritization for Multitask Learning](https://scholariq.org/papers/dynamic-task-prioritization-for-multitask-learning/)
- [Using Sampling and Queries to Extract Rules from Trained Neural Networks](https://scholariq.org/papers/using-sampling-and-queries-to-extract-rules-from-trained-neural-networks/)
- [The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition](https://scholariq.org/papers/the-unreasonable-effectiveness-of-noisy-data-for-fine-grained-recognition/)
- [Using neural networks for data mining](https://scholariq.org/papers/using-neural-networks-for-data-mining/)
- [Q-Learning for robust satisfaction of signal temporal logic specifications](https://scholariq.org/papers/q-learning-for-robust-satisfaction-of-signal-temporal-logic-specifications/)
- [Approximating minimum bounded degree spanning trees to within one of optimal](https://scholariq.org/papers/approximating-minimum-bounded-degree-spanning-trees-to-within-one-of-optimal-2/)
- [Feature-Induced Partial Multi-label Learning](https://scholariq.org/papers/feature-induced-partial-multi-label-learning/)
- [Temporal logic inference for classification and prediction from data](https://scholariq.org/papers/temporal-logic-inference-for-classification-and-prediction-from-data/)
- [Real time identification of discrete event systems using Petri nets](https://scholariq.org/papers/real-time-identification-of-discrete-event-systems-using-petri-nets/)
- [First Order Constrained Optimization in Policy Space](https://scholariq.org/papers/first-order-constrained-optimization-in-policy-space/)

## Topic primary papers

- [An analysis of active learning strategies for sequence labeling tasks](https://scholariq.org/papers/an-analysis-of-active-learning-strategies-for-sequence-labeling-tasks/)
- [Multiple-Instance Active Learning](https://scholariq.org/papers/multiple-instance-active-learning/)
- [Using Sampling and Queries to Extract Rules from Trained Neural Networks](https://scholariq.org/papers/using-sampling-and-queries-to-extract-rules-from-trained-neural-networks/)
- [The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition](https://scholariq.org/papers/the-unreasonable-effectiveness-of-noisy-data-for-fine-grained-recognition/)
- [Temporal logic inference for classification and prediction from data](https://scholariq.org/papers/temporal-logic-inference-for-classification-and-prediction-from-data/)
- [An incremental learning algorithm for constructing Boolean functions from positive and negative examples](https://scholariq.org/papers/an-incremental-learning-algorithm-for-constructing-boolean-functions-from/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.