What is One-Shot Learning? One-Shot Learning is a task where the support set has just one data sample for each class. The task becomes more complicated with lesser information to support it. Smartphones using the face recognition technology use One-Shot Learning. It is the paradigm that solv...
Few-Shot Learning (FSL) is a Machine Learning framework that enables a pre-trained model to generalize over new categories of data (that the pre-trained model has not seen during training) using only a few labeled samples per class. It falls under the paradigm of meta-learning (meta-learnin...
In order to learn from a limited number of examples with supervised information, a new machine learning paradigm called Few-Shot Learning (FSL) [35, 36] is proposed. 当然,FSL还可以推进机器人技术[26],后者开发出可以复制人类行为的机器。 例子包括一杆模仿[147],多臂匪[33],视觉导航[37]和连续控...
The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applicationsLANGUAGE modelsDRUG discoveryTRANSFORMER modelsMACHINE learningIMAGE analysisRecent advances in machine learning (ML) have led to newer model architectures including ...
Machine learning has been highly successful in data-intensive applications, but is often hampered when thedata set is small. In order to learn from alimitednumber of examples with supervised information, a new machine learning paradigm called Few-Shot Learning (FSL) [35, 36] is proposed. ...
Here, we extend this previous work by proposing and evaluating similar techniques but adapted to the few-shot learning paradigm with imbalanced data. In particular, we use a metric-based FSL method based on Siamese networks [44] in which a series of proposals are integrated to mitigate the ...
This study introduces Ice Finder, a novel tool for quantifying crystalline ice in cryo-electron tomography, addressing a critical gap in existing methodologies. We present the first application of the meta-learning paradigm to this field, demonstrating t
The few-shot learning (FSL) paradigm is an alternative attempt that aims to improve model performance under data constraints. The goal of FSL is to efficiently learn from a small number of shots (ie, data samples or instances). The number of samples usually ranges from 1 to 100 per class...
In this work, we introduce FSFP, a paradigm for effectively training PLMs to predict protein fitness using only a small number (tens) of labeled mutants. FSFP integrates the techniques of LTR, LoRA, and MTL, where LTR meets the intrinsic needs of directed evolutions to rank the protein fitn...
To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a ...