183 citations · 432 across the 25 of their papers we have counts for
6 papers · 1 filter
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…
Learning Binary Decision Trees by Argmin Differentiation
Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
We address the problem of learning binary decision trees that partition data for some downstream task. We propose to learn discrete parameters (i.e., for tree traversals and node p…
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…
Sparse and Continuous Attention Mechanisms
André F. T. Martins, António Farinhas, Marcos Treviso +3
Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical di…
Sparse and Structured Visual Attention
Pedro Henrique Martins, Vlad Niculae, Zita Marinho +1
Visual attention mechanisms are widely used in multimodal tasks, as visual question answering (VQA). One drawback of softmax-based attention mechanisms is that they assign some pro…
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Vlad Niculae, André F. T. Martins
Structured prediction requires manipulating a large number of combinatorial structures, e.g., dependency trees or alignments, either as latent or output variables. Recently, the Sp…