12 research outputs found
Algorithms to calculate the most reliable maximum flow in content delivery network
Funding Information: Funding Statement: This work was partly supported by Open Research Fund from State Key Laboratory of Smart Grid Protection and Control, China (Zhang B, www.byqsc.net/com/nrjt/), Rapid Support Project (61406190120, Zhang B), the Fundamental Research Funds for the Central Universities (2242021k10011, Zhang B, www.seu.edu.cn) and the National Key R&D Program of China (2018YFC0830200, Zhang B, www.most.gov.cn).Peer reviewedPublisher PD
BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions
Text classification is a well-studied and versatile building block for many
NLP applications. Yet, existing approaches require either large annotated
corpora to train a model with or, when using large language models as a base,
require carefully crafting the prompt as well as using a long context that can
fit many examples. As a result, it is not possible for end-users to build
classifiers for themselves. To address this issue, we propose a novel approach
to few-shot text classification using an LLM. Rather than few-shot examples,
the LLM is prompted with descriptions of the salient features of each class.
These descriptions are coauthored by the user and the LLM interactively: while
the user annotates each few-shot example, the LLM asks relevant questions that
the user answers. Examples, questions, and answers are summarized to form the
classification prompt. Our experiments show that our approach yields high
accuracy classifiers, within 82% of the performance of models trained with
significantly larger datasets while using only 1% of their training sets.
Additionally, in a study with 30 participants, we show that end-users are able
to build classifiers to suit their specific needs. The personalized classifiers
show an average accuracy of 90%, which is 15% higher than the state-of-the-art
approach.Comment: Accepted at EMNLP 2023 (Findings
Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning
Visual-based defect detection is a crucial but challenging task in industrial
quality control. Most mainstream methods rely on large amounts of existing or
related domain data as auxiliary information. However, in actual industrial
production, there are often multi-batch, low-volume manufacturing scenarios
with rapidly changing task demands, making it difficult to obtain sufficient
and diverse defect data. This paper proposes a parallel solution that uses a
human-machine knowledge hybrid augmentation method to help the model extract
unknown important features. Specifically, by incorporating experts' knowledge
of abnormality to create data with rich features, positions, sizes, and
backgrounds, we can quickly accumulate an amount of data from scratch and
provide it to the model as prior knowledge for few-data learning. The proposed
method was evaluated on the magnetic tile dataset and achieved F1-scores of
60.73%, 70.82%, 77.09%, and 82.81% when using 2, 5, 10, and 15 training images,
respectively. Compared to the traditional augmentation method's F1-score of
64.59%, the proposed method achieved an 18.22% increase in the best result,
demonstrating its feasibility and effectiveness in few-data industrial defect
detection.Comment: 24 pages, 15 figure
Learning from Very Few Samples: A Survey
Few sample learning (FSL) is significant and challenging in the field of
machine learning. The capability of learning and generalizing from very few
samples successfully is a noticeable demarcation separating artificial
intelligence and human intelligence since humans can readily establish their
cognition to novelty from just a single or a handful of examples whereas
machine learning algorithms typically entail hundreds or thousands of
supervised samples to guarantee generalization ability. Despite the long
history dated back to the early 2000s and the widespread attention in recent
years with booming deep learning technologies, little surveys or reviews for
FSL are available until now. In this context, we extensively review 300+ papers
of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive
survey for FSL. In this survey, we review the evolution history as well as the
current progress on FSL, categorize FSL approaches into the generative model
based and discriminative model based kinds in principle, and emphasize
particularly on the meta learning based FSL approaches. We also summarize
several recently emerging extensional topics of FSL and review the latest
advances on these topics. Furthermore, we highlight the important FSL
applications covering many research hotspots in computer vision, natural
language processing, audio and speech, reinforcement learning and robotic, data
analysis, etc. Finally, we conclude the survey with a discussion on promising
trends in the hope of providing guidance and insights to follow-up researches.Comment: 30 page