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Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities
The knowledge discovery process uses a probabilistic program defining the process of generating the data item from a set of unknown typed entities.
Learning Direct Optimization for Scene Understanding
We develop a Learning Direct Optimization (LiDO) method for the refinement of a latent variable model that describes input image x.
Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program
The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
We propose the Gaussian attention model for content-based neural memory access.
Just-In-Time Learning for Fast and Flexible Inference
Much of research in machine learning has centered around the search for inference algorithms that are both general-purpose and efficient.
Consensus Message Passing for Layered Graphical Models
Generative models provide a powerful framework for probabilistic reasoning.
Decision Jungles: Compact and Rich Models for Classification
Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision.
Learning to Pass Expectation Propagation Messages
Expectation Propagation (EP) is a popular approximate posterior inference algorithm that often provides a fast and accurate alternative to sampling-based methods.
