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05/17/2023
Variational Classification
We present a novel extension of the traditional neural network approach ...
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10/24/2022
Adapters for Enhanced Modeling of Multilingual Knowledge and Text
Large language models appear to learn facts from the large text corpora ...
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09/26/2022
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential dat...
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02/01/2022
Towards a Theoretical Understanding of Word and Relation Representation
Representing words by vectors, or embeddings, enables computational reas...
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07/06/2020
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
As data volumes continue to grow, the labelling process increasingly bec...
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06/24/2020
Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
Much of biomedical and healthcare data is encoded in discrete, symbolic ...
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06/10/2020
A Probabilistic Framework for Discriminative and Neuro-Symbolic Semi-Supervised Learning
In semi-supervised learning (SSL), a rule to predict labels y for data x...
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09/28/2019
Multi-scale Attributed Node Embedding
We present network embedding algorithms that capture information about a...
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09/25/2019
On Understanding Knowledge Graph Representation
Many methods have been developed to represent knowledge graph data, whic...
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05/23/2019
Multi-relational Poincaré Graph Embeddings
Hyperbolic embeddings have recently gained attention in machine learning...
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01/28/2019
Analogies Explained: Towards Understanding Word Embeddings
Word embeddings generated by neural network methods such as word2vec (W2...
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01/28/2019
TuckER: Tensor Factorization for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts. How...
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08/21/2018
Hypernetwork Knowledge Graph Embeddings
Knowledge graphs are large graph-structured databases of facts, which ty...
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05/30/2018
