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Found 7 papers, 6 papers with code
Large-Scale Riemannian Meta-Optimization via Subspace Adaptation
However, existing Riemannian meta-optimization methods take up huge memory footprints in large-scale optimization settings, as the learned optimizer can only adapt gradients of a fixed size and thus cannot be shared across different Riemannian parameters.
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision
We propose a Structure Refining Module, a simple yet effective first approach based on the similarities of the prompts by taking advantage of the intrinsic structure in the embedding space.
Alfred: A System for Prompted Weak Supervision
Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting.
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
Large-scale neural network models combining text and images have made incredible progress in recent years.
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
We perform additional experiments to show that CSP improves generalization to higher-order attribute-attribute-object compositions (e. g., old white cat) and combinations of pretrained attributes and fine-tuned objects.
Learning from Multiple Noisy Partial Labelers
We evaluate our framework on three text classification and six object classification tasks.
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.
