8 citations · 10 across the 5 of their papers we have counts for
5 papers
LogitBoost autoregressive networks
Marc Goessling
Multivariate binary distributions can be decomposed into products of univariate conditional distributions. Recently popular approaches have modeled these conditionals through neura…
Dynamic Partition Models
Marc Goessling, Yali Amit
We present a new approach for learning compact and intuitive distributed representations with binary encoding. Rather than summing up expert votes as in products of experts, we emp…
Mixtures of Sparse Autoregressive Networks
Marc Goessling, Yali Amit
We consider high-dimensional distribution estimation through autoregressive networks. By combining the concepts of sparsity, mixtures and parameter sharing we obtain a simple model…
Directional Decision Lists
Marc Goessling, Shan Kang
In this paper we introduce a novel family of decision lists consisting of highly interpretable models which can be learned efficiently in a greedy manner. The defining property is…
Compact Compositional Models
Marc Goessling, Yali Amit
Learning compact and interpretable representations is a very natural task, which has not been solved satisfactorily even for simple binary datasets. In this paper, we review variou…