14,858 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Enhanced free space detection in multiple lanes based on single CNN with scene identification
Many systems for autonomous vehicles' navigation rely on lane detection.
Traditional algorithms usually estimate only the position of the lanes on the
road, but an autonomous control system may also need to know if a lane marking
can be crossed or not, and what portion of space inside the lane is free from
obstacles, to make safer control decisions. On the other hand, free space
detection algorithms only detect navigable areas, without information about
lanes. State-of-the-art algorithms use CNNs for both tasks, with significant
consumption of computing resources. We propose a novel approach that estimates
the free space inside each lane, with a single CNN. Additionally, adding only a
small requirement concerning GPU RAM, we infer the road type, that will be
useful for path planning. To achieve this result, we train a multi-task CNN.
Then, we further elaborate the output of the network, to extract polygons that
can be effectively used in navigation control. Finally, we provide a
computationally efficient implementation, based on ROS, that can be executed in
real time. Our code and trained models are available online.Comment: Will appear in the 2019 IEEE Intelligent Vehicles Symposium (IV 2019
Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition
Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Deep Learning Approaches in Pavement Distress Identification: A Review
This paper presents a comprehensive review of recent advancements in image
processing and deep learning techniques for pavement distress detection and
classification, a critical aspect in modern pavement management systems. The
conventional manual inspection process conducted by human experts is gradually
being superseded by automated solutions, leveraging machine learning and deep
learning algorithms to enhance efficiency and accuracy. The ability of these
algorithms to discern patterns and make predictions based on extensive datasets
has revolutionized the domain of pavement distress identification. The paper
investigates the integration of unmanned aerial vehicles (UAVs) for data
collection, offering unique advantages such as aerial perspectives and
efficient coverage of large areas. By capturing high-resolution images, UAVs
provide valuable data that can be processed using deep learning algorithms to
detect and classify various pavement distresses effectively. While the primary
focus is on 2D image processing, the paper also acknowledges the challenges
associated with 3D images, such as sensor limitations and computational
requirements. Understanding these challenges is crucial for further
advancements in the field. The findings of this review significantly contribute
to the evolution of pavement distress detection, fostering the development of
efficient pavement management systems. As automated approaches continue to
mature, the implementation of deep learning techniques holds great promise in
ensuring safer and more durable road infrastructure for the benefit of society
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