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Found 5 papers, 1 papers with code
Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance
Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability.
From Local to Global: Spectral-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data.
Learning To Learn by Jointly Optimizing Neural Architecture and Weights
Existing NAS-based meta-learning methods apply a two-stage strategy, i. e., first searching architectures and then re-training meta-weights on the searched architecture.
Differentiable Graph Optimization for Neural Architecture Search
To this end, we learn a differentiable graph neural network as a surrogate model to rank candidate architectures, which enable us to obtain gradient w. r. t the input architectures.
Connection-Adaptive Meta-Learning
In this paper, we aim to obtain better meta-learners by co-optimizing the architecture and meta-weights simultaneously.
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