Towards Dynamic Computation Graphs via Sparse Latent Structure
Deep NLP models benefit from underlying structures in the data---e.g., parse trees---typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Using the recently proposed SparseMAP inference, which retrieves a sparse distribution over latent structures, we propose a novel approach for end-to-end learning of latent structure predictors jointly with a downstream predictor. To the best of our knowledge, our method is the first to enable unrestricted dynamic computation graph construction from the global latent structure, while maintaining differentiability.
Code (1)
Tasks
graph constructionSimilar Papers 제목 키워드 기반
Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs
Dynamic knowledge graphs are ubiquitous in today's AI applications, as we represent molecular structures, social relationships, and language information using these graph models. As knowledge graphs evolve over time and …
Knowledge GraphsLink PredictionDeep Sparse Latent Feature Models for Knowledge Graph Completion
Recent progress in knowledge graph completion (KGC) has focused on text-based approaches to address the challenges of large-scale knowledge graphs (KGs). Despite their achievements, these methods often overlook the intri…
Knowledge Graph CompletionKnowledge GraphsLink PredictionLearning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements
Cardiac blood flow patterns contain rich information about disease severity and clinical interventions, yet current imaging and computational methods fail to capture underlying relational structures of coherent flow feat…
Learning Sparse Graphs with a Core-periphery Structure
In this paper, we focus on learning sparse graphs with a core-periphery structure. We propose a generative model for data associated with core-periphery structured networks to model the dependence of node attributes on c…
Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities
We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process, and naturally generalizes existing probabilist…