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TensorFlow: A system for large-scale machine learning
The TensorFlow dataflow model is described and the compelling performance that Tensor Flow achieves for several real-world applications is demonstrated. Expand
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields. Expand
Object retrieval with large vocabularies and fast spatial matching
To improve query performance, this work adds an efficient spatial verification stage to re-rank the results returned from the bag-of-words model and shows that this consistently improves search quality, though by less of a margin when the visual vocabulary is large. Expand
CONDENSATION—Conditional Density Propagation for Visual Tracking
  • M. Isard, A. Blake
  • Mathematics, Computer Science
  • International Journal of Computer Vision
  • 1 August 1998
The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Expand
Dryad: distributed data-parallel programs from sequential building blocks
The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between vertices. Expand
Lost in quantization: Improving particular object retrieval in large scale image databases
The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local regions of imagesExpand
Contour Tracking by Stochastic Propagation of Conditional Density
The Condensation algorithm combines factored sampling with learned dynamical models to propagate an entire probability distribution for object position and shape, over time, and is markedly superior to what has previously been attainable from Kalman filtering. Expand
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
This paper brings query expansion into the visual domain via two novel contributions: strong spatial constraints between the query image and each result allow us to accurately verify each return, suppressing the false positives which typically ruin text-based query expansion. Expand
Naiad: a timely dataflow system
It is shown that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining. Expand
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