4,009 research outputs found
Data-Driven Vehicle Trajectory Forecasting
An active area of research is to increase the safety of self-driving
vehicles. Although safety cannot be guarenteed completely, the capability of a
vehicle to predict the future trajectories of its surrounding vehicles could
help ensure this notion of safety to a greater deal. We cast the trajectory
forecast problem in a multi-time step forecasting problem and develop a
Convolutional Neural Network based approach to learn from trajectory sequences
generated from completely raw dataset in real-time. Results show improvement
over baselines.Comment: Published in ECML KNOWMe: 2nd International Workshop on Knowledge
Discovery from Mobility and Transportation Systems 201
A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions
Stochastic microstructure reconstruction has become an indispensable part of
computational materials science, but ongoing developments are specific to
particular material systems. In this paper, we address this generality problem
by presenting a transfer learning-based approach for microstructure
reconstruction and structure-property predictions that is applicable to a wide
range of material systems. The proposed approach incorporates an
encoder-decoder process and feature-matching optimization using a deep
convolutional network. For microstructure reconstruction, model pruning is
implemented in order to study the correlation between the microstructural
features and hierarchical layers within the deep convolutional network.
Knowledge obtained in model pruning is then leveraged in the development of a
structure-property predictive model to determine the network architecture and
initialization conditions. The generality of the approach is demonstrated
numerically for a wide range of material microstructures with geometrical
characteristics of varying complexity. Unlike previous approaches that only
apply to specific material systems or require a significant amount of prior
knowledge in model selection and hyper-parameter tuning, the present approach
provides an off-the-shelf solution to handle complex microstructures, and has
the potential of expediting the discovery of new materials.Comment: Submitted to Scientific Reports, under revie
Mapping Auto-context Decision Forests to Deep ConvNets for Semantic Segmentation
We consider the task of pixel-wise semantic segmentation given a small set of
labeled training images. Among two of the most popular techniques to address
this task are Decision Forests (DF) and Neural Networks (NN). In this work, we
explore the relationship between two special forms of these techniques: stacked
DFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our
main contribution is to show that Auto-context can be mapped to a deep ConvNet
with novel architecture, and thereby trained end-to-end. This mapping can be
used as an initialization of a deep ConvNet, enabling training even in the face
of very limited amounts of training data. We also demonstrate an approximate
mapping back from the refined ConvNet to a second stacked DF, with improved
performance over the original. We experimentally verify that these mappings
outperform stacked DFs for two different applications in computer vision and
biology: Kinect-based body part labeling from depth images, and somite
segmentation in microscopy images of developing zebrafish. Finally, we revisit
the core mapping from a Decision Tree (DT) to a NN, and show that it is also
possible to map a fuzzy DT, with sigmoidal split decisions, to a NN. This
addresses multiple limitations of the previous mapping, and yields new insights
into the popular Rectified Linear Unit (ReLU), and more recently proposed
concatenated ReLU (CReLU), activation functions
Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is expected to require more effective and
efficient wireless communications than ever before. For this reason, techniques
such as spectrum sharing, dynamic spectrum access, extraction of signal
intelligence and optimized routing will soon become essential components of the
IoT wireless communication paradigm. Given that the majority of the IoT will be
composed of tiny, mobile, and energy-constrained devices, traditional
techniques based on a priori network optimization may not be suitable, since
(i) an accurate model of the environment may not be readily available in
practical scenarios; (ii) the computational requirements of traditional
optimization techniques may prove unbearable for IoT devices. To address the
above challenges, much research has been devoted to exploring the use of
machine learning to address problems in the IoT wireless communications domain.
This work provides a comprehensive survey of the state of the art in the
application of machine learning techniques to address key problems in IoT
wireless communications with an emphasis on its ad hoc networking aspect.
First, we present extensive background notions of machine learning techniques.
Then, by adopting a bottom-up approach, we examine existing work on machine
learning for the IoT at the physical, data-link and network layer of the
protocol stack. Thereafter, we discuss directions taken by the community
towards hardware implementation to ensure the feasibility of these techniques.
Additionally, before concluding, we also provide a brief discussion of the
application of machine learning in IoT beyond wireless communication. Finally,
each of these discussions is accompanied by a detailed analysis of the related
open problems and challenges.Comment: Ad Hoc Networks Journa
Machine learning in acoustics: theory and applications
Acoustic data provide scientific and engineering insights in fields ranging
from biology and communications to ocean and Earth science. We survey the
recent advances and transformative potential of machine learning (ML),
including deep learning, in the field of acoustics. ML is a broad family of
techniques, which are often based in statistics, for automatically detecting
and utilizing patterns in data. Relative to conventional acoustics and signal
processing, ML is data-driven. Given sufficient training data, ML can discover
complex relationships between features and desired labels or actions, or
between features themselves. With large volumes of training data, ML can
discover models describing complex acoustic phenomena such as human speech and
reverberation. ML in acoustics is rapidly developing with compelling results
and significant future promise. We first introduce ML, then highlight ML
developments in four acoustics research areas: source localization in speech
processing, source localization in ocean acoustics, bioacoustics, and
environmental sounds in everyday scenes.Comment: Published with free access in Journal of the Acoustical Society of
America, 27 Nov. 201
Learning Sparse Deep Feedforward Networks via Tree Skeleton Expansion
Despite the popularity of deep learning, structure learning for deep models
remains a relatively under-explored area. In contrast, structure learning has
been studied extensively for probabilistic graphical models (PGMs). In
particular, an efficient algorithm has been developed for learning a class of
tree-structured PGMs called hierarchical latent tree models (HLTMs), where
there is a layer of observed variables at the bottom and multiple layers of
latent variables on top. In this paper, we propose a simple method for learning
the structures of feedforward neural networks (FNNs) based on HLTMs. The idea
is to expand the connections in the tree skeletons from HLTMs and to use the
resulting structures for FNNs. An important characteristic of FNN structures
learned this way is that they are sparse. We present extensive empirical
results to show that, compared with standard FNNs tuned-manually, sparse FNNs
learned by our method achieve better or comparable classification performance
with much fewer parameters. They are also more interpretable.Comment: 7 page
MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an
ecosystem of enhancements that expand the Apache Spark distributed computing
library to tackle problems in Deep Learning, Micro-Service Orchestration,
Gradient Boosting, Model Interpretability, and other areas of modern
computation. Furthermore, we present a novel system called Spark Serving that
allows users to run any Apache Spark program as a distributed, sub-millisecond
latency web service backed by their existing Spark Cluster. All MMLSpark
contributions have the same API to enable simple composition across frameworks
and usage across batch, streaming, and RESTful web serving scenarios on static,
elastic, or serverless clusters. We showcase MMLSpark by creating a method for
deep object detection capable of learning without human labeled data and
demonstrate its effectiveness for Snow Leopard conservation
Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation
Recently there has been an increasing trend to use deep learning frameworks
for both 2D consumer images and for 3D medical images. However, there has been
little effort to use deep frameworks for volumetric vascular segmentation. We
wanted to address this by providing a freely available dataset of 12 annotated
two-photon vasculature microscopy stacks. We demonstrated the use of deep
learning framework consisting both 2D and 3D convolutional filters (ConvNet).
Our hybrid 2D-3D architecture produced promising segmentation result. We
derived the architectures from Lee et al. who used the ZNN framework initially
designed for electron microscope image segmentation. We hope that by sharing
our volumetric vasculature datasets, we will inspire other researchers to
experiment with vasculature dataset and improve the used network architectures.Comment: 23 pages, 10 figure
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
We introduce multigrid Predictive Filter Flow (mgPFF), a framework for
unsupervised learning on videos. The mgPFF takes as input a pair of frames and
outputs per-pixel filters to warp one frame to the other. Compared to optical
flow used for warping frames, mgPFF is more powerful in modeling sub-pixel
movement and dealing with corruption (e.g., motion blur). We develop a
multigrid coarse-to-fine modeling strategy that avoids the requirement of
learning large filters to capture large displacement. This allows us to train
an extremely compact model (4.6MB) which operates in a progressive way over
multiple resolutions with shared weights. We train mgPFF on unsupervised,
free-form videos and show that mgPFF is able to not only estimate long-range
flow for frame reconstruction and detect video shot transitions, but also
readily amendable for video object segmentation and pose tracking, where it
substantially outperforms the published state-of-the-art without bells and
whistles. Moreover, owing to mgPFF's nature of per-pixel filter prediction, we
have the unique opportunity to visualize how each pixel is evolving during
solving these tasks, thus gaining better interpretability.Comment: webpage (https://www.ics.uci.edu/~skong2/mgpff.html
Deep Learning and its Application to LHC Physics
Machine learning has played an important role in the analysis of high-energy
physics data for decades. The emergence of deep learning in 2012 allowed for
machine learning tools which could adeptly handle higher-dimensional and more
complex problems than previously feasible. This review is aimed at the reader
who is familiar with high energy physics but not machine learning. The
connections between machine learning and high energy physics data analysis are
explored, followed by an introduction to the core concepts of neural networks,
examples of the key results demonstrating the power of deep learning for
analysis of LHC data, and discussion of future prospects and concerns.Comment: Posted with permission from the Annual Review of Nuclear and Particle
Science, Volume 68. (c) 2018 by Annual Reviews, http://www.annualreviews.or
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