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Found 5 papers, 2 papers with code
Scalable Graph Convolutional Network Training on Distributed-Memory Systems
The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary.
RAGUEL: Recourse-Aware Group Unfairness Elimination
While machine learning and ranking-based systems are in widespread use for sensitive decision-making processes (e. g., determining job candidates, assigning credit scores), they are rife with concerns over unintended biases in their outcomes, which makes algorithmic fairness (e. g., demographic parity, equal opportunity) an objective of interest.
Characterizing the impact of geometric properties of word embeddings on task performance
Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors.
Hypothesis Only Baselines in Natural Language Inference
We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI).
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning.
