STAR improves 1-shot action recognition by up to 8.1% on SSv2-Full through semantic-temporal alignment and Mamba-based prototype refinement.
Model-agnostic meta-learning for fast adaptation of deep networks
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
years
2026 9representative citing papers
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
RFPrompt adapts the Large Wireless Model via deep prompt tokens to improve out-of-distribution robustness in modulation classification while training only a small number of parameters.
A surrogate for parametric nonconvex optimization is constructed as the minimum of convex-monotonic function compositions and solved via parallel convex optimization, with a proof-of-concept on path tracking.
A frozen LSTM backbone with FiLM-based few-shot context adaptation estimates tip-level contact forces for deformable swabbing tools across nine surface-tool regimes using only wrist-mounted proprioception.
LNN-Fly is a structured recurrent policy for continuous-time UAV obstacle avoidance trained with perturbed differentiable rollouts that shows improved tolerance to timing issues and zero-shot transfer to physical hardware with 100% success in real tests.
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
Placing one Hebbian fast-weight module after the final stage of Swin-Tiny achieves 96.2% accuracy on 5-way 1-shot Omniglot classification, outperforming the non-Hebbian baseline by 0.3 points.
citing papers explorer
-
STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition
STAR improves 1-shot action recognition by up to 8.1% on SSv2-Full through semantic-temporal alignment and Mamba-based prototype refinement.
-
Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
-
Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
-
RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification
RFPrompt adapts the Large Wireless Model via deep prompt tokens to improve out-of-distribution robustness in modulation classification while training only a small number of parameters.
-
Parametric Nonconvex Optimization via Convex Surrogates
A surrogate for parametric nonconvex optimization is constructed as the minimum of convex-monotonic function compositions and solved via parallel convex optimization, with a proof-of-concept on path tracking.
-
Context-Aware Force Estimation for Deformable Tool Manipulation in Robotic Environmental Swabbing via Few-Shot Continual Adaptation
A frozen LSTM backbone with FiLM-based few-shot context adaptation estimates tip-level contact forces for deformable swabbing tools across nine surface-tool regimes using only wrist-mounted proprioception.
-
LNN-Fly: Continuous-Time UAV Navigation for Robust Obstacle Avoidance under Timing Mismatch
LNN-Fly is a structured recurrent policy for continuous-time UAV obstacle avoidance trained with perturbed differentiable rollouts that shows improved tolerance to timing issues and zero-shot transfer to physical hardware with 100% success in real tests.
-
UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
-
Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition
Placing one Hebbian fast-weight module after the final stage of Swin-Tiny achieves 96.2% accuracy on 5-way 1-shot Omniglot classification, outperforming the non-Hebbian baseline by 0.3 points.