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Natural Language Processing (Almost) from Scratch
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entityExpand
A unified architecture for natural language processing: deep neural networks with multitask learning
We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semanticExpand
Curriculum learning
TLDR
It is hypothesized that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of non-convex functions). Expand
Torch7: A Matlab-like Environment for Machine Learning
TLDR
Torch7 is a versatile numeric computing framework and machine learning library that extends Lua that can easily be interfaced to third-party software thanks to Lua’s light interface. Expand
Large Scale Transductive SVMs
We show how the concave-convex procedure can be applied to transductive SVMs, which traditionally require solving a combinatorial search problem. This provides for the first time a highly scalableExpand
Learning Structured Embeddings of Knowledge Bases
TLDR
A learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced would allow data from any KB to be easily used in recent machine learning methods for prediction and information retrieval. Expand
Learning to Refine Object Segments
TLDR
This work proposes to augment feedforward nets for object segmentation with a novel top-down refinement approach that is capable of efficiently generating high-fidelity object masks and is 50 % faster than the original DeepMask network. Expand
From image-level to pixel-level labeling with Convolutional Networks
TLDR
A Convolutional Neural Network-based model is proposed, which is constrained during training to put more weight on pixels which are important for classifying the image, and which beats the state of the art results in weakly supervised object segmentation task by a large margin. Expand
SVMTorch: Support Vector Machines for Large-Scale Regression Problems
Keywords: learning Reference EPFL-REPORT-82604 URL: http://publications.idiap.ch/downloads/reports/2000/rr00-17.pdf Record created on 2006-03-10, modified on 2017-05-10
Learning to Segment Object Candidates
TLDR
A new way to generate object proposals is proposed, introducing an approach based on a discriminative convolutional network that obtains substantially higher object recall using fewer proposals and is able to generalize to unseen categories it has not seen during training. Expand
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