Papers
Browse academic papers referenced in production code
Distributed Representations of Words and Phrases and their Compositionality
The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several ...
Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Bec...
On the correct and complete enumeration of the core search space
Reordering more than traditional joins (e.g. outerjoins, antijoins) requires some care, since not all reorderings are valid. To prevent invalid plans, two approaches have been described in the literature. We show that both approaches still produce in...
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input ...
Rectifier Nonlinearities Improve Neural Network Acoustic Models
YouTube is a highly visited video sharing website where over one billion people watch six billion hours of video every month. Improving accessibility to these videos for the hearing impaired and for search and indexing purposes is an excellent applic...
Scalable Object Detection using Deep Neural Networks
Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the localization s...
Scalable statistics counters
Statistics counters are important for purposes such as detecting excessively high rates of various system events, or for mechanisms that adapt based on event frequency. As systems grow and become increasingly NUMA, commonly used naive counters impose...
Stochastic variational inference.
AbstractTools for estimating population structure from genetic data are now used in a wide variety of applications in population genetics. However, inferring population structure in large modern data sets imposes severe computational challenges. Here...
A Read-Copy Update based parallel server for distributed crowd simulations
The Read-Copy Update (RCU) synchronization method was designed to cope with multiprocessor scalability some years ago, and it was included in the Linux kernel October of 2002. Recently, libraries providing user-space access to this method have been r...
Left Recursion in Parsing Expression Grammars
Parsing Expression Grammars (PEGs) are a formalism that can describe all deterministic context-free languages through a set of rules that specify a top-down parser for some language. PEGs are easy to use, and there are efficient implementations of PE...
On the difficulty of training Recurrent Neural Networks
There are two widely known issues with properly training Recurrent Neural Networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to improve the understanding of the underlying issues by ...
Taming the wildcards: combining definition- and use-site variance
Variance allows the safe integration of parametric and subtype polymorphism. Two flavors of variance, definition-site versus use-site variance, have been studied and have had their merits hotly debated. Definition-site variance (as in Scala and C#) o...
Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent
For large scale learning problems, it is desirable if we can obtain the optimal model parameters by going through the data in only one pass. Polyak and Juditsky (1992) showed that asymptotically the test performance of the simple average of the param...
A contextual-bandit approach to personalized news article recommendation
Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two re...
Generalized RBF feature maps for Efficient Detection
Kernel methods yield state-of-the-art performance in certain applications such as image classification and object detection. However, large scale problems require machine learning techniques of at most linear complexity and these are usually limited ...
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models.
We address the problem of articulated 2D human pose estimation in natural images. A well-known person detector - the Implicit Shape Model (ISM) approach introduced by Leibe et al. - is shown not only to be well suited to detect persons, but can also ...