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Found 6 papers, 4 papers with code
Transformer-based Models for Long-Form Document Matching: Challenges and Empirical Analysis
Firstly, the performance gain provided by transformer-based models comes at a steep cost - both in terms of the required training time and the resource (memory and energy) consumption.
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction
Graph neural networks have achieved significant success in representation learning.
Supervised Contrastive Learning for Interpretable Long-Form Document Matching
When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of "similarity"), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content.
Efficacy of Bayesian Neural Networks in Active Learning
By performing a comprehensive set of experiments, we show that Bayesian neural networks are more efficient than ensemble based techniques in capturing uncertainty.
Matrix Completion in the Unit Hypercube via Structured Matrix Factorization
We tackle this challenge by using a two-fold approach: first, we transform this task into a constrained matrix completion problem with entries bounded in the unit interval [0, 1]; second, we propose two novel matrix factorization models that leverage our knowledge of the VFX environment.
Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research.
