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Found 11 papers, 10 papers with code
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection
Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL).
NameGuess: Column Name Expansion for Tabular Data
Recent advances in large language models have revolutionized many sectors, including the database industry.
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
Recent advances in tabular data generation have greatly enhanced synthetic data quality.
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks.
Equivariant Subgraph Aggregation Networks
Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture.
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks
In this work, we exploit the incidence structure to develop a hypergraph neural network to learn provably expressive representations of variable sized hyperedges which preserve local-isomorphism in the line graph of the hypergraph, while also being invariant to permutations of its constituent vertices.
On the Equivalence between Positional Node Embeddings and Structural Graph Representations
This work provides the first unifying theoretical framework for node (positional) embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks.
Relational Pooling for Graph Representations
This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions.
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Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
We consider a simple and overarching representation for permutation-invariant functions of sequences (or multiset functions).
