It's hard to keep up with the latest and greatest in machine learning. Here's a selection of survey papers summarizing the advances in the field.
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Table of Contents
- Recommendation
- Deep Learning
- Natural Language Processing
- Computer Vision
- Vision and Language
- Reinforcement Learning
- Graph
- Embeddings
- Meta-learning and Few-shot Learning
- Others
- Algorithms: Recommender systems survey (2013)
- Algorithms: Deep Learning based Recommender System: A Survey and New Perspectives (2019)
- Algorithms: Are We Really Making Progress? An Analysis of Neural Recommendation Approaches (2019)
- Serendipity: A Survey of Serendipity in Recommender Systems (2016)
- Diversity: Diversity in Recommender Systems – A survey (2017)
- Explanations: A Survey of Explanations in Recommender Systems (2007)
- Architecture: A State-of-the-Art Survey on Deep Learning Theory and Architectures (2019)
- Knowledge distillation: Knowledge Distillation: A Survey (2021)
- Model compression: Compression of Deep Learning Models for Text: A Survey (2020)
- Transfer learning: A Survey on Deep Transfer Learning (2018)
- Neural architecture search: A Comprehensive Survey of Neural Architecture Search (2021)
- Neural architecture search: Neural Architecture Search: A Survey (2019)
- Deep Learning: Recent Trends in Deep Learning Based Natural Language Processing (2018)
- Classification: Deep Learning Based Text Classification: A Comprehensive Review (2021)
- Generation: Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation (2018)
- Generation: Neural Language Generation: Formulation, Methods, and Evaluation (2020)
- Transfer learning: Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (2020)
- Transformers: Efficient Transformers: A Survey (2020)
- Metrics: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (2020)
- Metrics: Evaluation of Text Generation: A Survey (2020)
- Object detection: Object Detection in 20 Years (2019)
- Adversarial attacks: Threat of Adversarial Attacks on Deep Learning in Computer Vision (2018)
- Autonomous vehicles: Computer Vision for Autonomous Vehicles: Problems, Datasets and SOTA (2021)
- Image Captioning: A Comprehensive Survey of Deep Learning for Image Captioning (2018)
- Instance Segmentation: A Survey on Instance Segmentation: State of the art
- Vision Transformer: A Survey on Vision Transformer
- Architectures: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
- Transformers: Transformers in Vision: A Survey
- Trends: Trends in Integration of Vision and Language Research: Tasks, Datasets, and Methods (2021)
- Trends: Multimodal Research in Vision and Language: Current and Emerging Trends (2020)
- Algorithms: A Brief Survey of Deep Reinforcement Learning (2017)
- Transfer learning: Transfer Learning for Reinforcement Learning Domains (2009)
- Economics: Review of Deep Reinforcement Learning Methods and Applications in Economics (2020)
- Discovery: Deep Reinforcement Learning for Search, Recommendation, and Online Advertising (2018)
- Survey: A Comprehensive Survey on Graph Neural Networks (2019)
- Survey: A Practical Guide to Graph Neural Networks (2020)
- Fraud detection: A systematic literature review of graph-based anomaly detection approaches (2020)
- Knowledge graphs: A Comprehensive Introduction to Knowledge Graphs (2021)
- Text: From Word to Sense Embeddings:A Survey on Vector Representations of Meaning (2018)
- Text: Diachronic Word Embeddings and Semantic Shifts (2018)
- Text: Word Embeddings: A Survey (2019)
- Text: A Reproducible Survey on Word Embeddings and Ontology-based Methods for Word Similarity (2019)
- Graph: A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications (2017)
- NLP: Meta-learning for Few-shot Natural Language Processing: A Survey (2020)
- Domain Agnostic: Learning from Few Samples: A Survey (2020)
- Neural Networks: Meta-Learning in Neural Networks: A Survey (2020)
- Domain Agnostic: A Comprehensive Overview and Survey of Recent Advances in Meta-Learning (2020)
- Domain Agnostic: Baby steps towards few-shot learning with multiple semantics (2020)
- Domain Agnostic: Meta-Learning: A Survey (2018)
- Domain Agnostic: A Perspective View And Survey Of Meta-learning (2002)
- Transfer learning: A Survey on Transfer Learning (2009)