1
Matching Networks for One Shot Learning
Source ↗Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
Jun 13, 2016·471 checkouts·arxiv.org
Best Few Shot Learning Papers
The highest-signal papers on Few Shot Learning, ranked by community reviews and momentum.
Canonical intent: topic=few-shot-learning|type=paper|year=evergreen
Top Picks
2
Flamingo: a Visual Language Model for Few-Shot Learning
Source ↗Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Karen Simonyan
Apr 28, 2022·406 checkouts·arxiv.org
3
Language Models are Few-Shot Learners
Source ↗Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei
May 28, 2020·211 checkouts·arxiv.org
FAQ
How is this “best Few Shot Learning Papers” collection ranked?
This page ranks Few Shot Learning Papers using topic relevance, checkout momentum, source diversity, and freshness signals. Rankings are recalculated as new items and engagement arrive, so readers see resources that are both high quality and currently useful for implementation, research, and practical decision making. Canonical intent key: topic=few-shot-learning|type=paper|year=evergreen.
How do you prevent duplicate collection pages?
Attendemia maps each slug variant, including best-of and year forms, to one canonical intent key. If two URLs describe the same topic, type, and timeframe, non-canonical versions permanently redirect. This consolidates crawl signals, avoids duplicate content dilution, and helps search engines index the strongest single page.
When does a year page stay separate from evergreen?
A year-specific page stays separate only when its item set is materially different from evergreen and has enough ranking depth. When overlap is high, the year URL redirects to the evergreen canonical page. This avoids thin duplication while preserving genuinely distinct annual collections for search users.
Are these paid recommendations?
No. These recommendations are not paid placements. Attendemia ranks items from public metadata, source quality coverage, and user engagement signals, then orders them by practical usefulness. Sponsorship does not buy rank position, so this page should be interpreted as editorial curation rather than advertising inventory.