most citedReconciling the Discrete-Continuous Divide: Towards a Mathematical Theory of Sparse Communication

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2021

SPECTRA: Sparse Structured Text Rationalization

Nuno Miguel Guerreiro, André F. T. Martins

Selective rationalization aims to produce decisions along with rationales (e.g., text highlights or word alignments between two sentences). Commonly, rationales are modeled as stoc…

cs.CV2021

Multimodal Continuous Visual Attention Mechanisms

António Farinhas, André F. T. Martins, Pedro M. Q. Aguiar

Visual attention mechanisms are a key component of neural network models for computer vision. By focusing on a discrete set of objects or image regions, these mechanisms identify t…

cs.LG20211 cited

Reconciling the Discrete-Continuous Divide: Towards a Mathematical Theory of Sparse Communication

André F. T. Martins

Neural networks and other machine learning models compute continuous representations, while humans communicate with discrete symbols. Reconciling these two forms of communication i…

cs.CL2020

Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning

Tsvetomila Mihaylova, Vlad Niculae, André F. T. Martins

Latent structure models are a powerful tool for modeling language data: they can mitigate the error propagation and annotation bottleneck in pipeline systems, while simultaneously…

cs.LG2020

Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity

Gonçalo M. Correia, Vlad Niculae, Wilker Aziz +1

Training neural network models with discrete (categorical or structured) latent variables can be computationally challenging, due to the need for marginalization over large or comb…