On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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4 results for “ProtoNet”

LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learningcs.LG

A new method improves few-shot learning by combining ProtoNet with LFD.

problem Few-shot learning struggles with high variance support sets.

method Combines ProtoNet with Local Fisher Discriminant Analysis.

result Superior classification accuracy on miniImageNet and tieredImageNet.

few-shot learning ProtoNet Local Fisher Discriminant Analysis few-shot classification support set variance

2020-06-15abs ↗pdf ↗

Few-shot Learning with Noisy Labelscs.CV

New method improves few-shot learning with noisy labels.

problem Robustness to label noise in few-shot learning.

method Feature aggregation and Transformer model for noisy samples.

result TraNFS outperforms other methods in noisy conditions.

few-shot learning noisy labels Transformer feature aggregation robustness

2022-04-12abs ↗pdf ↗

On Data Efficiency of Meta-learningcs.LG

Meta-learning algorithms are studied for their data efficiency in personalized federated learning.

problem Data efficiency in meta-learning algorithms.

method Algorithmic stability techniques to derive bounds on transfer risk and a new evaluation framework.

result Different meta-learning methods require varying amounts of supervision for effective generalization.

meta-learning federated learning algorithmic stability data efficiency few-shot learning

2021-01-30abs ↗pdf ↗

Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Predictioncs.LG

Model learns to select relevant clinical variables for disease subtype prediction from small data.

problem Few-shot disease subtype prediction from small genomic data.

method Meta learning Prototypical Network with feature selection and sample reweighting.

result Superior performance in predicting disease subtypes and identifying genes.

meta learning Prototypical Network feature selection sample reweighting few-shot learning

2020-09-02abs ↗pdf ↗