139,490 research outputs found
A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks
We present a demonstration of a neural interactive-predictive system for
tackling multimodal sequence to sequence tasks. The system generates text
predictions to different sequence to sequence tasks: machine translation, image
and video captioning. These predictions are revised by a human agent, who
introduces corrections in the form of characters. The system reacts to each
correction, providing alternative hypotheses, compelling with the feedback
provided by the user. The final objective is to reduce the human effort
required during this correction process.
This system is implemented following a client-server architecture. For
accessing the system, we developed a website, which communicates with the
neural model, hosted in a local server. From this website, the different tasks
can be tackled following the interactive-predictive framework. We open-source
all the code developed for building this system. The demonstration in hosted in
http://casmacat.prhlt.upv.es/interactive-seq2seq.Comment: ACL 2019 - System demonstration
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs
The number of linked data sources and the size of the linked open data graph
keep growing every day. As a consequence, semantic RDF services are more and
more confronted with various "big data" problems. Query processing in the
presence of inferences is one them. For instance, to complete the answer set of
SPARQL queries, RDF database systems evaluate semantic RDFS relationships
(subPropertyOf, subClassOf) through time-consuming query rewriting algorithms
or space-consuming data materialization solutions. To reduce the memory
footprint and ease the exchange of large datasets, these systems generally
apply a dictionary approach for compressing triple data sizes by replacing
resource identifiers (IRIs), blank nodes and literals with integer values. In
this article, we present a structured resource identification scheme using a
clever encoding of concepts and property hierarchies for efficiently evaluating
the main common RDFS entailment rules while minimizing triple materialization
and query rewriting. We will show how this encoding can be computed by a
scalable parallel algorithm and directly be implemented over the Apache Spark
framework. The efficiency of our encoding scheme is emphasized by an evaluation
conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur
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