pwc-archive/papers-with-abstracts huggingface dataset:
Spatial Pyramid Convolutional Neural Network for Social Event Detection in Static Image
Authors: Reza Fuad Rachmadi, Keiichi Uchimura, Gou Koutaki
Date: 2016-12-13 00:00:00
Tasks: Event Detection, Image Captioning, Recommendation Systems

Social event detection in a static image is a very challenging problem and
it's very useful for internet of things applications including automatic photo
organization, ads recommender system, or image captioning. Several publications
show that variety of objects, scene, and people can be very ambiguous for the
system to decide the event that occurs in the image. We proposed the spatial
pyramid configuration of convolutional neural network (CNN) classifier for
social event detection in a static image. By applying the spatial pyramid
configuration to the CNN classifier, the detail that occurs in the image can
observe more accurately by the classifier. USED dataset provided by Ahmad et
al. is used to evaluate our proposed method, which consists of two different
image sets, EiMM, and SED dataset. As a result, the average accuracy of our
system outperforms the baseline method by 15% and 2% respectively.
Learning to Hash-tag Videos with Tag2Vec
Authors: Aditya Singh, Saurabh Saini, Rajvi Shah, PJ Narayanan
Date: 2016-12-13 00:00:00
Tasks: Retrieval, TAG

User-given tags or labels are valuable resources for semantic understanding
of visual media such as images and videos. Recently, a new type of labeling
mechanism known as hash-tags have become increasingly popular on social media
sites. In this paper, we study the problem of generating relevant and useful
hash-tags for short video clips. Traditional data-driven approaches for tag
enrichment and recommendation use direct visual similarity for label transfer
and propagation. We attempt to learn a direct low-cost mapping from video to
hash-tags using a two step training process. We first employ a natural language
processing (NLP) technique, skip-gram models with neural network training to
learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a
corpus of 10 million hash-tags. We then train an embedding function to map
video features to the low-dimensional Tag2vec space. We learn this embedding
for 29 categories of short video clips with hash-tags. A query video without
any tag-information can then be directly mapped to the vector space of tags
using the learned embedding and relevant tags can be found by performing a
simple nearest-neighbor retrieval in the Tag2Vec space. We validate the
relevance of the tags suggested by our system qualitatively and quantitatively
with a user study.
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Authors: Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer
Date: 2016-12-13 00:00:00

Deep convolutional neural networks (CNNs) have shown great potential for
numerous real-world machine learning applications, but performing inference in
large CNNs in real-time remains a challenge. We have previously demonstrated
that traditional CNNs can be converted into deep spiking neural networks
(SNNs), which exhibit similar accuracy while reducing both latency and
computational load as a consequence of their data-driven, event-based style of
computing. Here we provide a novel theory that explains why this conversion is
successful, and derive from it several new tools to convert a larger and more
powerful class of deep networks into SNNs. We identify the main sources of
approximation errors in previous conversion methods, and propose simple
mechanisms to fix these issues. Furthermore, we develop spiking implementations
of common CNN operations such as max-pooling, softmax, and batch-normalization,
which allow almost loss-less conversion of arbitrary CNN architectures into the
spiking domain. Empirical evaluation of different network architectures on the
MNIST and CIFAR10 benchmarks leads to the best SNN results reported to date.
DizzyRNN: Reparameterizing Recurrent Neural Networks for Norm-Preserving Backpropagation
Authors: Victor Dorobantu, Per Andre Stromhaug, Jess Renteria
Date: 2016-12-13 00:00:00

The vanishing and exploding gradient problems are well-studied obstacles that
make it difficult for recurrent neural networks to learn long-term time
dependencies. We propose a reparameterization of standard recurrent neural
networks to update linear transformations in a provably norm-preserving way
through Givens rotations. Additionally, we use the absolute value function as
an element-wise non-linearity to preserve the norm of backpropagated signals
over the entire network. We show that this reparameterization reduces the
number of parameters and maintains the same algorithmic complexity as a
standard recurrent neural network, while outperforming standard recurrent
neural networks with orthogonal initializations and Long Short-Term Memory
networks on the copy problem.
Video Scene Parsing with Predictive Feature Learning
Authors: Xiaojie Jin, Xin Li, Huaxin Xiao, Xiaohui Shen, Zhe Lin, Jimei Yang, Yunpeng Chen, Jian Dong, Luoqi Liu, Zequn Jie, Jiashi Feng, Shuicheng Yan
Date: 2016-12-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Representation Learning, Scene Parsing

In this work, we address the challenging video scene parsing problem by
developing effective representation learning methods given limited parsing
annotations. In particular, we contribute two novel methods that constitute a
unified parsing framework. (1) \textbf{Predictive feature learning}} from
nearly unlimited unlabeled video data. Different from existing methods learning
features from single frame parsing, we learn spatiotemporal discriminative
features by enforcing a parsing network to predict future frames and their
parsing maps (if available) given only historical frames. In this way, the
network can effectively learn to capture video dynamics and temporal context,
which are critical clues for video scene parsing, without requiring extra
manual annotations. (2) \textbf{Prediction steering parsing}} architecture that
effectively adapts the learned spatiotemporal features to scene parsing tasks
and provides strong guidance for any off-the-shelf parsing model to achieve
better video scene parsing performance. Extensive experiments over two
challenging datasets, Cityscapes and Camvid, have demonstrated the
effectiveness of our methods by showing significant improvement over
well-established baselines.
Knowledge Graph Representation with Jointly Structural and Textual Encoding
Authors: Jiacheng Xu, Kan Chen, Xipeng Qiu, Xuanjing Huang
Date: 2016-11-26 00:00:00
Tasks: General Classification, Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Triplet

The objective of knowledge graph embedding is to encode both entities and
relations of knowledge graphs into continuous low-dimensional vector spaces.
Previously, most works focused on symbolic representation of knowledge graph
with structure information, which can not handle new entities or entities with
few facts well. In this paper, we propose a novel deep architecture to utilize
both structural and textual information of entities. Specifically, we introduce
three neural models to encode the valuable information from text description of
entity, among which an attentive model can select related information as
needed. Then, a gating mechanism is applied to integrate representations of
structure and text into a unified architecture. Experiments show that our
models outperform baseline by margin on link prediction and triplet
classification tasks. Source codes of this paper will be available on Github.
Proceedings of the The First Workshop on Verification and Validation of Cyber-Physical Systems
Authors: Mehdi Kargahi, Ashutosh Trivedi
Date: 2016-12-13 00:00:00

The first International Workshop on Verification and Validation of
Cyber-Physical Systems (V2CPS-16) was held in conjunction with the 12th
International Conference on integration of Formal Methods (iFM 2016) in
Reykjavik, Iceland. The purpose of V2CPS-16 was to bring together researchers
and experts of the fields of formal verification and cyber-physical systems
(CPS) to cover the theme of this workshop, namely a wide spectrum of
verification and validation methods including (but not limited to) control,
simulation, formal methods, etc.
  A CPS is an integration of networked computational and physical processes
with meaningful inter-effects; the former monitors, controls, and affects the
latter, while the latter also impacts the former. CPSs have applications in a
wide-range of systems spanning robotics, transportation, communication,
infrastructure, energy, and manufacturing. Many safety-critical systems such as
chemical processes, medical devices, aircraft flight control, and automotive
systems, are indeed CPS. The advanced capabilities of CPS require complex
software and synthesis algorithms, which are hard to verify. In fact, many
problems in this area are undecidable. Thus, a major step is to find particular
abstractions of such systems which might be algorithmically verifiable
regarding specific properties of such systems, describing the partial/overall
behaviors of CPSs.
Generative Adversarial Parallelization
Authors: Daniel Jiwoong Im, He Ma, Chris Dongjoo Kim, Graham Taylor
Date: 2016-12-13 00:00:00

Generative Adversarial Networks have become one of the most studied
frameworks for unsupervised learning due to their intuitive formulation. They
have also been shown to be capable of generating convincing examples in limited
domains, such as low-resolution images. However, they still prove difficult to
train in practice and tend to ignore modes of the data generating distribution.
Quantitatively capturing effects such as mode coverage and more generally the
quality of the generative model still remain elusive. We propose Generative
Adversarial Parallelization, a framework in which many GANs or their variants
are trained simultaneously, exchanging their discriminators. This eliminates
the tight coupling between a generator and discriminator, leading to improved
convergence and improved coverage of modes. We also propose an improved variant
of the recently proposed Generative Adversarial Metric and show how it can
score individual GANs or their collections under the GAP model.
Generating images with recurrent adversarial networks
Authors: Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, Roland Memisevic
Date: 2016-02-16 00:00:00
Tasks: Rolling Shutter Correction

Gatys et al. (2015) showed that optimizing pixels to match features in a
convolutional network with respect reference image features is a way to render
images of high visual quality. We show that unrolling this gradient-based
optimization yields a recurrent computation that creates images by
incrementally adding onto a visual "canvas". We propose a recurrent generative
model inspired by this view, and show that it can be trained using adversarial
training to generate very good image samples. We also propose a way to
quantitatively compare adversarial networks by having the generators and
discriminators of these networks compete against each other.
Performance Improvements of Probabilistic Transcript-adapted ASR with Recurrent Neural Network and Language-specific Constraints
Authors: Xiang Kong, Preethi Jyothi, Mark Hasegawa-Johnson
Date: 2016-12-13 00:00:00
Tasks: Cross-Lingual ASR, TAR

Mismatched transcriptions have been proposed as a mean to acquire
probabilistic transcriptions from non-native speakers of a language.Prior work
has demonstrated the value of these transcriptions by successfully adapting
cross-lingual ASR systems for different tar-get languages. In this work, we
describe two techniques to refine these probabilistic transcriptions: a
noisy-channel model of non-native phone misperception is trained using a
recurrent neural net-work, and decoded using minimally-resourced
language-dependent pronunciation constraints. Both innovations improve quality
of the transcript, and both innovations reduce phone error rate of a
trainedASR, by 7% and 9% respectively
Evaluating Automatic Speech Recognition Systems in Comparison With Human Perception Results Using Distinctive Feature Measures
Authors: Xiang Kong, Jeung-Yoon Choi, Stefanie Shattuck-Hufnagel
Date: 2016-12-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper describes methods for evaluating automatic speech recognition
(ASR) systems in comparison with human perception results, using measures
derived from linguistic distinctive features. Error patterns in terms of
manner, place and voicing are presented, along with an examination of confusion
matrices via a distinctive-feature-distance metric. These evaluation methods
contrast with conventional performance criteria that focus on the phone or word
level, and are intended to provide a more detailed profile of ASR system
performance,as well as a means for direct comparison with human perception
results at the sub-phonemic level.
Models, networks and algorithmic complexity
Authors: Giulio Ruffini
Date: 2016-12-13 00:00:00
Tasks: Descriptive, General Classification

I aim to show that models, classification or generating functions,
invariances and datasets are algorithmically equivalent concepts once properly
defined, and provide some concrete examples of them. I then show that a) neural
networks (NNs) of different kinds can be seen to implement models, b) that
perturbations of inputs and nodes in NNs trained to optimally implement simple
models propagate strongly, c) that there is a framework in which recurrent,
deep and shallow networks can be seen to fall into a descriptive power
hierarchy in agreement with notions from the theory of recursive functions. The
motivation for these definitions and following analysis lies in the context of
cognitive neuroscience, and in particular in Ruffini (2016), where the concept
of model is used extensively, as is the concept of algorithmic complexity.
Deep Convolutional Poses for Human Interaction Recognition in Monocular Videos
Authors: Marcel Sheeny de Moraes, Sankha Mukherjee, Neil M. Robertson
Date: 2016-12-13 00:00:00
Tasks: Human Interaction Recognition, Pose Estimation

Human interaction recognition is a challenging problem in computer vision and
has been researched over the years due to its important applications. With the
development of deep models for the human pose estimation problem, this work
aims to verify the effectiveness of using the human pose in order to recognize
the human interaction in monocular videos. This paper developed a method based
on 5 steps: detect each person in the scene, track them, retrieve the human
pose, extract features based on the pose and finally recognize the interaction
using a classifier. The Two-Person interaction dataset was used for the
development of this methodology. Using a whole sequence evaluation approach it
achieved 87.56% of average accuracy of all interaction. Yun, et at achieved
91.10% using the same dataset, however their methodology used the depth sensor
to recognize the interaction. The methodology developed in this paper shows
that an RGB camera can be as effective as depth cameras to recognize the
interaction between two persons using the recent development of deep models to
estimate the human pose.
Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective Functions
Authors: Brett Israelsen, Nisar Ahmed
Date: 2016-12-13 00:00:00
Tasks: Bayesian Optimization

A key drawback of the current generation of artificial decision-makers is
that they do not adapt well to changes in unexpected situations. This paper
addresses the situation in which an AI for aerial dog fighting, with tunable
parameters that govern its behavior, will optimize behavior with respect to an
objective function that must be evaluated and learned through simulations. Once
this objective function has been modeled, the agent can then choose its desired
behavior in different situations. Bayesian optimization with a Gaussian Process
surrogate is used as the method for investigating the objective function. One
key benefit is that during optimization the Gaussian Process learns a global
estimate of the true objective function, with predicted outcomes and a
statistical measure of confidence in areas that haven't been investigated yet.
However, standard Bayesian optimization does not perform consistently or
provide an accurate Gaussian Process surrogate function for highly volatile
objective functions. We treat these problems by introducing a novel sampling
technique called Hybrid Repeat/Multi-point Sampling. This technique gives the
AI ability to learn optimum behaviors in a highly uncertain environment. More
importantly, it not only improves the reliability of the optimization, but also
creates a better model of the entire objective surface. With this improved
model the agent is equipped to better adapt behaviors.
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
Authors: Robyn Speer, Joshua Chin, Catherine Havasi
Date: 2016-12-12 00:00:00
Tasks: General Knowledge, Word Embeddings

Machine learning about language can be improved by supplying it with specific
knowledge and sources of external information. We present here a new version of
the linked open data resource ConceptNet that is particularly well suited to be
used with modern NLP techniques such as word embeddings.
  ConceptNet is a knowledge graph that connects words and phrases of natural
language with labeled edges. Its knowledge is collected from many sources that
include expert-created resources, crowd-sourcing, and games with a purpose. It
is designed to represent the general knowledge involved in understanding
language, improving natural language applications by allowing the application
to better understand the meanings behind the words people use.
  When ConceptNet is combined with word embeddings acquired from distributional
semantics (such as word2vec), it provides applications with understanding that
they would not acquire from distributional semantics alone, nor from narrower
resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art
results on intrinsic evaluations of word relatedness that translate into
improvements on applications of word vectors, including solving SAT-style
analogies.
Probabilistic Bisection Converges Almost as Quickly as Stochastic Approximation
Authors: Peter I. Frazier, Shane G. Henderson, Rolf Waeber
Date: 2016-12-12 00:00:00

The probabilistic bisection algorithm (PBA) solves a class of stochastic
root-finding problems in one dimension by successively updating a prior belief
on the location of the root based on noisy responses to queries at chosen
points. The responses indicate the direction of the root from the queried
point, and are incorrect with a fixed probability. The fixed-probability
assumption is problematic in applications, and so we extend the PBA to apply
when this assumption is relaxed. The extension involves the use of a power-one
test at each queried point. We explore the convergence behavior of the extended
PBA, showing that it converges at a rate arbitrarily close to, but slower than,
the canonical "square root" rate of stochastic approximation.
Neural Networks with Manifold Learning for Diabetic Retinopathy Detection
Authors: Arjun Raj Rajanna, Kamelia Aryafar, Rajeev Ramchandran, Christye Sisson, Ali Shokoufandeh, Raymond Ptucha
Date: 2016-12-12 00:00:00
Tasks: Diabetic Retinopathy Detection, General Classification

Widespread outreach programs using remote retinal imaging have proven to
decrease the risk from diabetic retinopathy, the leading cause of blindness in
the US. However, this process still requires manual verification of image
quality and grading of images for level of disease by a trained human grader
and will continue to be limited by the lack of such scarce resources.
Computer-aided diagnosis of retinal images have recently gained increasing
attention in the machine learning community. In this paper, we introduce a set
of neural networks for diabetic retinopathy classification of fundus retinal
images. We evaluate the efficiency of the proposed classifiers in combination
with preprocessing and augmentation steps on a sample dataset. Our experimental
results show that neural networks in combination with preprocessing on the
images can boost the classification accuracy on this dataset. Moreover the
proposed models are scalable and can be used in large scale datasets for
diabetic retinopathy detection. The models introduced in this paper can be used
to facilitate the diagnosis and speed up the detection process.
Autoencoder-based holographic image restoration
Authors: Tomoyoshi Shimobaba, Yutaka Endo, Ryuji Hirayama, Yuki Nagahama, Takayuki Takahashi, Takashi Nishitsuji, Takashi Kakue, Atsushi Shiraki, Naoki Takada, Nobuyuki Masuda, Tomoyoshi Ito
Date: 2016-12-12 00:00:00
Tasks: Image Restoration

We propose a holographic image restoration method using an autoencoder, which
is an artificial neural network. Because holographic reconstructed images are
often contaminated by direct light, conjugate light, and speckle noise, the
discrimination of reconstructed images may be difficult. In this paper, we
demonstrate the restoration of reconstructed images from holograms that record
page data in holographic memory and QR codes by using the proposed method.
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks
Authors: Soheil Hashemi, Nicholas Anthony, Hokchhay Tann, R. Iris Bahar, Sherief Reda
Date: 2016-12-12 00:00:00
Tasks: Quantization

Deep neural networks are gaining in popularity as they are used to generate
state-of-the-art results for a variety of computer vision and machine learning
applications. At the same time, these networks have grown in depth and
complexity in order to solve harder problems. Given the limitations in power
budgets dedicated to these networks, the importance of low-power, low-memory
solutions has been stressed in recent years. While a large number of dedicated
hardware using different precisions has recently been proposed, there exists no
comprehensive study of different bit precisions and arithmetic in both inputs
and network parameters. In this work, we address this issue and perform a study
of different bit-precisions in neural networks (from floating-point to
fixed-point, powers of two, and binary). In our evaluation, we consider and
analyze the effect of precision scaling on both network accuracy and hardware
metrics including memory footprint, power and energy consumption, and design
area. We also investigate training-time methodologies to compensate for the
reduction in accuracy due to limited bit precision and demonstrate that in most
cases, precision scaling can deliver significant benefits in design metrics at
the cost of very modest decreases in network accuracy. In addition, we propose
that a small portion of the benefits achieved when using lower precisions can
be forfeited to increase the network size and therefore the accuracy. We
evaluate our experiments, using three well-recognized networks and datasets to
show its generality. We investigate the trade-offs and highlight the benefits
of using lower precisions in terms of energy and memory footprint.