#transfer learning

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24 papers match

cs.GT2026

Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games

Joshua Caiata, Sreepriya Pulyassary, Xiang Li +1

The paper introduces a lightweight behavioural embedding for normal-form games, using Nash equilibrium entropy and response sensitivity, to predict how fine‑tuning large language m…

#game theory#language models#transfer learning#embeddings
cs.LG2026

Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors

Neelam Akula, Surbhi Kumar, Murat Kantarcioglu +1

The paper defines a clean evaluation protocol for transferring knowledge between node classification and link prediction on the same graph, shows that transfer is directionally dep…

#graph neural networks#node classification#link prediction#transfer learning
cs.AI2026

PerturbMap: Cross-Context Transfer of Single-Cell Perturbation Responses

Panpan Cui, Yiqi Liu, Wenhao Sun

PerturbMap predicts missing single‑cell perturbation effects in a new cellular context by combining a low‑rank local model with ridge‑based transfer of measured responses from sour…

#single-cell perturbation#transfer learning#cross-context prediction#low-rank modeling
quant-ph2026

Quantum machine learning interatomic potential: Application of variational quantum algorithm

Kohei Numata, Wataru Mizukami, Kosuke Mitarai +2

The paper integrates a variational quantum circuit into a classical neural network for interatomic potentials, retraining the ANI model via quantum transfer learning and showing mo…

#quantum machine learning#interatomic potentials#variational quantum algorithms#transfer learning
cs.CV2026

Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures

Sweta Banerjee, Alireza Teimoury, Nils Porsche +11

The paper evaluates whether pathology foundation models can serve as effective backbones for dense detection of mitotic figures, comparing several self‑supervised models to a ResNe…

#pathology foundation models#mitotic figure detection#dense object detection#self-supervised learning
cs.CV2026

Interpretable Image-Level Acne Severity Grading via EfficientNet-B0 Transfer Learning and Grad-CAM

Sophie Zeng, Sean Kalaycioglu, Collin Hong +1

The paper introduces a four‑class acne severity grading model that uses transfer learning with an EfficientNet‑B0 backbone and Grad‑CAM visualizations to achieve high accuracy and…

#acne severity grading#transfer learning#efficientnet#grad-cam