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few-shot learning

topic10 events
papersTODAY 04:00 UTC

arXiv paper reports in-context learning emerges similarly across modalities

A new arXiv preprint examines few-shot in-context learning, the ability of a model to pick up abstract patterns from examples in its prompt and apply them to new inputs. The authors note this behavior has been studied mainly in large language models trained on next-token prediction, and report that it arises in a convergent way across different modalities. The announcement provides only the abstract, so methodological details are not yet available.

in-context-learning/multimodal/research/arxiv
papersTODAY 04:00 UTC

Study compares Complement Naive Bayes with zero- and few-shot LLMs

A new arXiv paper benchmarks Complement Naive Bayes against large language models used in zero-shot and few-shot settings, spanning four model families and a much larger classical baseline dataset. The authors ask whether classical methods like Naive Bayes should be retired as LLMs become more common in research computing. The results offer an empirical comparison of accuracy and cost between the two approaches.

benchmarks/naive-bayes/llm/research
papersTODAY 04:00 UTC

Data-Efficient Sample Selection for In-Context Learning

A new arXiv paper tackles the problem of choosing which demonstration examples to include in a prompt when using in-context learning with large language models. Because the space of possible example subsets is combinatorially large, the authors propose a data-efficient approach to selecting good combinations without exhaustive search. The work aims to improve how LLMs adapt to new tasks without fine-tuning.

in-context-learning/llm/research/sample-selection
papersTODAY 04:00 UTC

Synthetic Data Method Targets Few-Shot Cryo-ET Subtomogram Classification

A new arXiv paper addresses the limited availability of labeled data for subtomogram classification in cryo-electron tomography. The authors propose a method to close the gap between simulated cryo-ET data and real experimental images, aiming to improve classification when only a few labeled examples exist. The approach is categorized under machine learning research.

cryo-et/few-shot-learning/synthetic-data/research
papersTODAY 04:00 UTC

ProtoCAM: Few-Shot Prototypical Learning for Breast Lesion Classification in Ultrasound

Researchers propose ProtoCAM, a method that combines prototypical networks with mask guidance to classify breast lesions in ultrasound images using only a small number of labeled examples. The approach is designed to be interpretable, addressing the difficulty of building reliable deep learning models for ultrasound analysis, especially in patients with dense breast tissue. The work is published as an arXiv preprint.

medical-imaging/few-shot-learning/interpretability/arxiv
papersTODAY 04:00 UTC

TwinICL Benchmark Tests Multimodal In-Context Learning With Paired Counterfactuals

Researchers released TwinICL, a procedurally generated benchmark that provides matched text and image versions of the same tasks, allowing direct comparison of in-context learning across modalities. The paired counterfactual design is intended to isolate how much a model's few-shot performance depends on the input format rather than the task itself. The work appears on arXiv under cs.LG.

benchmark/multimodal/in-context-learning/research
papersSEP 12 04:00 UTC

arXiv paper proposes iterative sequential transfer for few-shot multiobjective multitask optimization

A new preprint on arXiv describes a method that applies iterative sequential knowledge transfer to few-shot multiobjective multitask optimization. The work targets the challenge of sharing useful information across related optimization tasks when only limited data is available. It focuses on improving transfer mechanisms, a key bottleneck in multitask optimization research.

research/optimization/transfer-learning/arxiv
papersSEP 12 04:00 UTC

Paper Questions Whether Few-Shot Learning Protocols Reflect True Few-Shot Conditions

A new arXiv paper argues that standard few-shot learning benchmarks may not measure what they claim. In typical setups, models are first trained on a large auxiliary dataset whose categories differ from the evaluation episodes but come from the same visual domain, so the target task is not genuinely novel. The authors call for closer scrutiny of these pre-training assumptions when interpreting few-shot results.

few-shot/pre-training/benchmarks/research
papersSEP 10 04:00 UTC

Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification

A newly updated arXiv paper introduces a classification approach that combines spatial and frequency-domain representations, with particular emphasis on phase information, to better capture structural relationships between visually similar images. The method targets few-shot fine-grained classification, where a model must distinguish closely related categories using only a small number of labeled examples.

few-shot-learning/computer-vision/image-classification
papersSEP 10 04:00 UTC

ProMeta: few-shot learning predicts PROTAC degradation across E3 ligases

Researchers introduce ProMeta, a few-shot machine learning framework that forecasts how effectively PROTAC molecules degrade target proteins across different E3 ligases. PROTACs are bifunctional compounds that hijack the ubiquitin-proteasome system to eliminate disease-linked proteins long considered out of reach for conventional drugs. The method targets the scarcity of labeled data that has limited prior computational predictors for targeted degradation.

machine-learning/drug-discovery/few-shot-learning/biotech