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Found 12 papers, 6 papers with code
Causally-guided Regularization of Graph Attention Improves Generalizability
Graph attention networks estimate the relational importance of node neighbors to aggregate relevant information over local neighborhoods for a prediction task.
Graph Neural Networks for Link Prediction with Subgraph Sketching
Our experiments show that BUDDY also outperforms SGNNs on standard LP benchmarks while being highly scalable and faster than ELPH.
Towards more patient friendly clinical notes through language models and ontologies
Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art.
Biomedical Concept Relatedness -- A large EHR-based benchmark
We open-source a novel concept relatedness benchmark overcoming these issues: it is six times larger than existing datasets and concept pairs are chosen based on co-occurrence in EHRs, ensuring their relevance for the application of interest.
Estimating Mutual Information Between Dense Word Embeddings
In this work we go through a vast literature on estimating MI in such cases and single out the most promising methods, yielding a simple and elegant similarity measure for word embeddings.
Correlations between Word Vector Sets
Just like cosine similarity is used to compare individual word vectors, we introduce a novel application of the centered kernel alignment (CKA) as a natural generalisation of squared cosine similarity for sets of word vectors.
Neural Language Priors
The choice of sentence encoder architecture reflects assumptions about how a sentence's meaning is composed from its constituent words.
Explaining Deep Learning Models with Constrained Adversarial Examples
Machine learning algorithms generally suffer from a problem of explainability.
Multilingual Factor Analysis
In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary.
Model Comparison for Semantic Grouping
We introduce a probabilistic framework for quantifying the semantic similarity between two groups of embeddings.
Sorting out symptoms: design and evaluation of the 'babylon check' automated triage system
Prior to seeking professional medical care it is increasingly common for patients to use online resources such as automated symptom checkers.
Towards Using Unlabeled Data in a Sparse-coding Framework for Human Activity Recognition
We propose a sparse-coding framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches.
