Now showing items 11-14 of 14
Parsing Occluded People by Flexible Compositions
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-06-01)
This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior ...
Holographic Embeddings of Knowledge Graphs
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-11-16)
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn ...
Building machines that learn and think like people
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-04-01)
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object ...
Deep Convolutional Networks are Hierarchical Kernel Machines
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-08-05)
We extend i-theory to incorporate not only pooling but also rectifying nonlinearities in an extended HW module (eHW) designed for supervised learning. The two operations roughly correspond to invariance and selectivity, ...