Krzysztof Wolk, Krzysztof Marasek
The multilingual nature of the world makes translation a crucial requirement today. Parallel dictionaries constructed by humans are a widely-available resource, but they are limited and do not provide enough coverage for good quality translation purposes, due to out-of-vocabulary words and neologisms. This motivates the use of statistical translation systems, which are unfortunately dependent on […]
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Andrew Lavin
We derive a new class of fast algorithms for convolutional neural networks using Winograd’s minimal filtering algorithms. Specifically we derive algorithms for network layers with 3×3 kernels, which are the preferred kernel size for image recognition tasks. The best of our algorithms reduces arithmetic complexity up to 4X compared with direct convolution, while using small […]
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Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kim, Hadi Esmaeilzadeh
A growing number of commercial and enterprise systems increasingly rely on compute-intensive machine learning algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from general-purpose platforms are diminishing. To accommodate the needs of machine learning algorithms, Field Programmable Gate Arrays (FPGAs) provide a promising path forward and represent an intermediate point […]
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John-Alexander M. Assael
Data-efficient learning in continuous state-action spaces using high-dimensional observations remains an elusive challenge in developing fully autonomous systems. An instance of this challenge is the pixels to torques problem, which identifies key elements of an autonomous agent: autonomous thinking and decision making using sensor measurements only, learning from mistakes, and applying past experiences to novel […]
Hasmik Osipyan, Martin Krulis, Stephane Marchand-Maillet
The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction which is defined as a process of mapping data from high-dimensional space into low-dimensional. One of the most popular methods for handling this problem is multidimensional scaling. Due to the technological advances, the dimensionality of the input data as […]
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Siddharth Mohanty
Manual tuning of applications for heterogeneous parallel systems is tedious and complex. Optimizations are often not portable, and the whole process must be repeated when moving to a new system, or sometimes even to a different problem size. Pattern based parallel programming models were originally designed to provide programmers with an abstract layer, hiding tedious […]
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Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Sparks, Alexander Ulanov, Matei Zaharia
We describe matrix computations available in the cluster programming framework, Apache Spark. Out of the box, Spark comes with the mllib.linalg library, which provides abstractions and implementations for distributed matrices. Using these abstractions, we highlight the computations that were more challenging to distribute. When translating single-node algorithms to run on a distributed cluster, we observe […]
David Hall
Syntactic parsing is one of the core tasks of natural language processing, with many appli- cations in downstream NLP tasks, from machine translation and summarization to relation extraction and coreference resolution. Parsing performance on English texts, particularly well-edited newswire text, is generally regarded as quite good. However, state-of-the-art constituency parsers produce incorrect parses for more […]
Fabian Tschopp
This work presents and analyzes three convolutional neural network (CNN) models for efficient pixelwise classification of images. When using convolutional neural networks to classify single pixels in patches of a whole image, a lot of redundant computations are carried out when using sliding window networks. This set of new architectures solve this issue by either […]
Yahui Chen
The goal of a Knowledge Base-supported Question Answering (KB-supported QA) system is to answer a query natural language by obtaining the answer from a knowledge database, which stores knowledge in the form of (entity, relation, value) triples. QA systems understand questions by extracting entity and relation pairs. This thesis aims at recognizing the relation candidates […]
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Yanping Huang, Sai Zhang
Deep learning methods have shown great promise in many practical applications, ranging from speech recognition, visual object recognition, to text processing. However, most of the current deep learning methods suffer from scalability problems for large-scale applications, forcing researchers or users to focus on small-scale problems with fewer parameters. In this paper, we consider a well-known […]
Tom Runia
In this thesis we design, implement and study a high-speed object detection framework. Our baseline detector uses integral channel features as object representation and AdaBoost as supervised learning algorithm. We suggest the implementation of two approximation techniques for speeding up the baseline detector and show their effectiveness by performing experiments on both detection quality and […]
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