Preprint2025
A survey of world models for autonomous driving
Unknown
A survey of recent advances in world models for autonomous driving, covering data augmentation and scene evaluation.
0Jan 1, 2025
arXiv
AI Research
AI & ML Research Papers
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
All Papers
15 papersPreprint2026
Test-Time Augmentation for LLMs: When Input Diversity Beats Output Diversity at Matched Compute
Unknown
The paper compares input-level test-time augmentation against output-level sampling for LLMs at matched compute, finding input diversity yields better accuracy.
0Aug 1, 2026Scaling Laws
arXiv
PreprintAI Open2022
Data augmentation approaches in natural language processing: A survey
Bohan Li, Yutai Hou, Wanxiang Che
This survey categorizes data augmentation methods in NLP into paraphrasing, noising, and sampling based on the diversity of augmented data, and reviews their applications and challenges.
391Jan 1, 2022Computer Vision
arXiv
PreprintarXiv.org2024
Recurrent Memory Finds What LLMs Miss
Yuri Kuratov, A. Bulatov, Petr Anokhin, et al.
Recurrent memory augmentation enables GPT-2 to process sequences up to 11 million elements, far exceeding standard methods limited to 10,000 elements.
52Feb 16, 2024TransformersFine Tuning
arXiv
PreprintJournal Of Big Data2021
A survey on data‐efficient algorithms in big data era
Amina Adadi
This survey systematically categorizes data-efficient ML methods into four strategies: non-supervised learning, data augmentation, transfer learning, and algorithm modification.
322Jan 26, 2021Computer Vision
PreprintSensors2018
SECOND: Sparsely Embedded Convolutional Detection
Yan Yan, Yuxing Mao, Bo Li
Proposes SECOND, a sparse convolutional network with angle loss regression and data augmentation for fast and accurate 3D object detection from LiDAR.
3.6kOct 6, 2018BenchmarksComputer Vision
PreprintarXiv.org2024
A Survey on Data Synthesis and Augmentation for LLMs
Ke Wang, Jiahui Zhu, Minjie Ren, et al.
This survey comprehensively reviews data generation techniques for LLMs, covering data augmentation and synthesis across the entire LLM lifecycle.
51Oct 16, 2024Large Language ModelsFine Tuning
arXiv
Preprint2020
SimCLR
Unknown
SimCLR simplifies contrastive learning by optimizing data augmentation, adding learnable nonlinear transformations, and scaling batch size and training steps.
0Feb 1, 2020
PreprintarXiv.org2024
Skywork-Math
Liang Zeng, Liangjun Zhong, Liang Zhao, et al.
A 7B LLM fine-tuned on a new 2.5M instance math QA dataset generated via a two-stage pipeline with diverse seeds and hard problem augmentation.
24Jul 1, 2024Fine Tuning
arXiv
JournalJournal of the Academy of Marketing Science2024
How generative AI Is shaping the future of marketing
Dhruv Grewal, Cinthia B. Satornino, Thomas H. Davenport, et al.
This paper presents a four-quadrant framework for marketers to evaluate trade-offs in generative AI inputs and human augmentation needed for outputs.
174Dec 14, 2024
PreprintInformation2020
Albumentations: Fast and Flexible Image Augmentations
Alexander Buslaev, Vladimir I. Iglovikov, Eugene Khvedchenya, et al.
Albumentations is a fast, flexible open-source image augmentation library offering many transform operations and serving as an easy-to-use wrapper around other libraries.
2.6kFeb 24, 2020Scaling LawsComputer Vision
arXiv
PreprintAcademy of Management Review2021
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Sebastian Raisch, Sebastian Krakowski
This paper argues that automation and augmentation in AI are not separable but paradoxical, requiring a balanced perspective to avoid negative outcomes.
2.0kJan 1, 2021