the archive

#meta-learning

5 results

quant-ph2019

Optimizing quantum heuristics with meta-learning

Max Wilson, Sam Stromswold, Filip Wudarski +3

The paper investigates using meta‑learning techniques to optimize variational quantum algorithms, showing that meta‑learners can more reliably find near‑optimal parameters and are…

#variational quantum algorithms#meta-learning#optimization#noise resilience

cs.CV2019

MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks

Chen Ma, Chenxu Zhao, Hailin Shi +3

The paper introduces a meta‑learning based detection framework that can learn to recognize newly evolved adversarial image attacks from only a few examples, using a double‑network…

#adversarial attacks#few-shot detection#meta-learning#robustness

cs.LG2019

Learning to Generalize to Unseen Tasks with Bilevel Optimization

Hayeon Lee, Donghyun Na, Hae Beom Lee +1

The paper introduces L2G, a bilevel optimization framework that explicitly encourages metric‑based meta‑learning models to generalize to unseen classification tasks, leading to imp…

#meta-learning#few-shot classification#metric learning#bilevel optimization

cs.CV2019

Few-Shot Viewpoint Estimation

Hung-Yu Tseng, Shalini De Mello, Jonathan Tremblay +4

The paper introduces a meta-learning framework called MetaView for category-level few-shot viewpoint estimation, enabling accurate pose prediction for new object categories using t…

#few-shot learning#viewpoint estimation#meta-learning#3d shape estimation

cs.IR2019

MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

Hoyeop Lee, Jinbae Im, Seongwon Jang +2

The paper introduces MeLU, a meta-learning based recommender system that can estimate a new user's preferences from only a few consumed items by selecting informative evidence cand…

#cold-start recommendation#meta-learning#few-shot learning#user preference estimation