Search Results for author: William Cohen

Found 23 papers, 3 papers with code

Investigating the Effect of Background Knowledge on Natural Questions

no code implementations NAACL (DeeLIO) 2021 Vidhisha Balachandran, Bhuwan Dhingra, Haitian Sun, Michael Collins, William Cohen

We create a subset of the NQ data, Factual Questions (FQ), where the questions have evidence in the KB in the form of paths that link question entities to answer entities but still must be answered using text, to facilitate further research into KB integration methods.

Natural Questions Retrieval

Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge

no code implementations NAACL 2021 Pat Verga, Haitian Sun, Livio Baldini Soares, William Cohen

Past research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive.

Question Answering

From genome to phenome: Predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer

1 code implementation31 Jan 2019 Yifeng Tao, Chunhui Cai, William Cohen, Xinghua Lu

Here, we present a deep neural network model with encoder-decoder architecture, referred to as genomic impact transformer (GIT), to infer the functional impact of SGAs on cellular signaling systems through modeling the statistical relationships between SGA events and differentially expressed genes (DEGs) in tumors.

TransNets: Learning to Transform for Recommendation

2 code implementations7 Apr 2017 Rose Catherine, William Cohen

For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks.

Recommendation Systems

Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs

no code implementations4 Aug 2015 Zhilin Yang, Jie Tang, William Cohen

GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities---i. e., social network users and knowledge concepts---in a shared latent topic space.

Knowledge Graphs

Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch

no code implementations10 Oct 2013 Partha Pratim Talukdar, William Cohen

Graph-based Semi-supervised learning (SSL) algorithms have been successfully used in a large number of applications.

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