pwc-archive/papers-with-abstracts huggingface dataset:
Total Variation-Based Dense Depth from Multi-Camera Array
Authors: Hossein Javidnia, Peter Corcoran
Date: 2017-11-21 00:00:00
Tasks: Depth Estimation

Multi-Camera arrays are increasingly employed in both consumer and industrial
applications, and various passive techniques are documented to estimate depth
from such camera arrays. Current depth estimation methods provide useful
estimations of depth in an imaged scene but are often impractical due to
significant computational requirements. This paper presents a novel framework
that generates a high-quality continuous depth map from multi-camera
array/light field cameras. The proposed framework utilizes analysis of the
local Epipolar Plane Image (EPI) to initiate the depth estimation process. The
estimated depth map is then processed using Total Variation (TV) minimization
based on the Fenchel-Rockafellar duality. Evaluation of this method based on a
well-known benchmark indicates that the proposed framework performs well in
terms of accuracy when compared to the top-ranked depth estimation methods and
a baseline algorithm. The test dataset includes both photorealistic and
non-photorealistic scenes. Notably, the computational requirements required to
achieve an equivalent accuracy are significantly reduced when compared to the
top algorithms. As a consequence, the proposed framework is suitable for
deployment in consumer and industrial applications.
Residual Parameter Transfer for Deep Domain Adaptation
Authors: Artem Rozantsev, Mathieu Salzmann, Pascal Fua
Date: 2017-11-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation

The goal of Deep Domain Adaptation is to make it possible to use Deep Nets
trained in one domain where there is enough annotated training data in another
where there is little or none. Most current approaches have focused on learning
feature representations that are invariant to the changes that occur when going
from one domain to the other, which means using the same network parameters in
both domains. While some recent algorithms explicitly model the changes by
adapting the network parameters, they either severely restrict the possible
domain changes, or significantly increase the number of model parameters.
  By contrast, we introduce a network architecture that includes auxiliary
residual networks, which we train to predict the parameters in the domain with
little annotated data from those in the other one. This architecture enables us
to flexibly preserve the similarities between domains where they exist and
model the differences when necessary. We demonstrate that our approach yields
higher accuracy than state-of-the-art methods without undue complexity.
Natural Language Guided Visual Relationship Detection
Authors: Wentong Liao, Lin Shuai, Bodo Rosenhahn, Michael Ying Yang
Date: 2017-11-16 00:00:00
Tasks: Relationship Detection, Scene Understanding, Visual Relationship Detection

Reasoning about the relationships between object pairs in images is a crucial
task for holistic scene understanding. Most of the existing works treat this
task as a pure visual classification task: each type of relationship or phrase
is classified as a relation category based on the extracted visual features.
However, each kind of relationships has a wide variety of object combination
and each pair of objects has diverse interactions. Obtaining sufficient
training samples for all possible relationship categories is difficult and
expensive. In this work, we propose a natural language guided framework to
tackle this problem. We propose to use a generic bi-directional recurrent
neural network to predict the semantic connection between the participating
objects in the relationship from the aspect of natural language. The proposed
simple method achieves the state-of-the-art on the Visual Relationship
Detection (VRD) and Visual Genome datasets, especially when predicting unseen
relationships (e.g. recall improved from 76.42% to 89.79% on VRD zero-shot
testing set).
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions
Authors: Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens
Date: 2017-11-20 00:00:00
Tasks: Dimensionality Reduction

In machine learning or statistics, it is often desirable to reduce the dimensionality of a sample of data points in a high dimensional space $\mathbb{R}^d$. This paper introduces a dimensionality reduction method where the embedding coordinates are the eigenvectors of a positive semi-definite kernel obtained as the solution of an infinite dimensional analogue of a semi-definite program. This embedding is adaptive and non-linear. We discuss this problem both with weak and strong smoothness assumptions about the learned kernel. A main feature of our approach is the existence of an out-of-sample extension formula of the embedding coordinates in both cases. This extrapolation formula yields an extension of the kernel matrix to a data-dependent Mercer kernel function. Our empirical results indicate that this embedding method is more robust with respect to the influence of outliers, compared with a spectral embedding method.
Design Automation for Binarized Neural Networks: A Quantum Leap Opportunity?
Authors: Manuele Rusci, Lukas Cavigelli, Luca Benini
Date: 2017-11-21 00:00:00
Tasks: Deep Learning

Design automation in general, and in particular logic synthesis, can play a
key role in enabling the design of application-specific Binarized Neural
Networks (BNN). This paper presents the hardware design and synthesis of a
purely combinational BNN for ultra-low power near-sensor processing. We
leverage the major opportunities raised by BNN models, which consist mostly of
logical bit-wise operations and integer counting and comparisons, for pushing
ultra-low power deep learning circuits close to the sensor and coupling it with
binarized mixed-signal image sensor data. We analyze area, power and energy
metrics of BNNs synthesized as combinational networks. Our synthesis results in
GlobalFoundries 22nm SOI technology shows a silicon area of 2.61mm2 for
implementing a combinational BNN with 32x32 binary input sensor receptive field
and weight parameters fixed at design time. This is 2.2x smaller than a
synthesized network with re-configurable parameters. With respect to other
comparable techniques for deep learning near-sensor processing, our approach
features a 10x higher energy efficiency.
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
Authors: Dmytro Mishkin, Filip Radenovic, Jiri Matas
Date: 2017-11-17 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Matching, Image Retrieval, Retrieval

A method for learning local affine-covariant regions is presented. We show
that maximizing geometric repeatability does not lead to local regions, a.k.a
features,that are reliably matched and this necessitates descriptor-based
learning. We explore factors that influence such learning and registration: the
loss function, descriptor type, geometric parametrization and the trade-off
between matchability and geometric accuracy and propose a novel hard
negative-constant loss function for learning of affine regions. The affine
shape estimator -- AffNet -- trained with the hard negative-constant loss
outperforms the state-of-the-art in bag-of-words image retrieval and wide
baseline stereo. The proposed training process does not require precisely
geometrically aligned patches.The source codes and trained weights are
available at https://github.com/ducha-aiki/affnet
JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs
Authors: Gino Brunner, Yuyi Wang, Roger Wattenhofer, Jonas Wiesendanger
Date: 2017-11-21 00:00:00

We propose a novel approach for the generation of polyphonic music based on
LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord
progression based on a chord embedding. A second LSTM then generates polyphonic
music from the predicted chord progression. The generated music sounds pleasing
and harmonic, with only few dissonant notes. It has clear long-term structure
that is similar to what a musician would play during a jam session. We show
that our approach is sensible from a music theory perspective by evaluating the
learned chord embeddings. Surprisingly, our simple model managed to extract the
circle of fifths, an important tool in music theory, from the dataset.
A New Oscillating-Error Technique for Classifiers
Authors: Kieran Greer
Date: 2015-05-20 00:00:00

This paper describes a new method for reducing the error in a classifier. It
uses an error correction update that includes the very simple rule of either
adding or subtracting the error adjustment, based on whether the variable value
is currently larger or smaller than the desired value. While a traditional
neuron would sum the inputs together and then apply a function to the total,
this new method can change the function decision for each input value. This
gives added flexibility to the convergence procedure, where through a series of
transpositions, variables that are far away can continue towards the desired
value, whereas variables that are originally much closer can oscillate from one
side to the other. Tests show that the method can successfully classify some
benchmark datasets. It can also work in a batch mode, with reduced training
times and can be used as part of a neural network architecture. Some
comparisons with an earlier wave shape paper are also made.
Transferring Agent Behaviors from Videos via Motion GANs
Authors: Ashley D. Edwards, Charles L. Isbell Jr
Date: 2017-11-21 00:00:00
Tasks: General Reinforcement Learning, Generative Adversarial Network, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A major bottleneck for developing general reinforcement learning agents is
determining rewards that will yield desirable behaviors under various
circumstances. We introduce a general mechanism for automatically specifying
meaningful behaviors from raw pixels. In particular, we train a generative
adversarial network to produce short sub-goals represented through motion
templates. We demonstrate that this approach generates visually meaningful
behaviors in unknown environments with novel agents and describe how these
motions can be used to train reinforcement learning agents.
Efficient Architecture Search by Network Transformation
Authors: Han Cai, Tianyao Chen, Wei-Nan Zhang, Yong Yu, Jun Wang
Date: 2017-07-16 00:00:00
Tasks: Image Classification, Neural Architecture Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Techniques for automatically designing deep neural network architectures such
as reinforcement learning based approaches have recently shown promising
results. However, their success is based on vast computational resources (e.g.
hundreds of GPUs), making them difficult to be widely used. A noticeable
limitation is that they still design and train each network from scratch during
the exploration of the architecture space, which is highly inefficient. In this
paper, we propose a new framework toward efficient architecture search by
exploring the architecture space based on the current network and reusing its
weights. We employ a reinforcement learning agent as the meta-controller, whose
action is to grow the network depth or layer width with function-preserving
transformations. As such, the previously validated networks can be reused for
further exploration, thus saves a large amount of computational cost. We apply
our method to explore the architecture space of the plain convolutional neural
networks (no skip-connections, branching etc.) on image benchmark datasets
(CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method
can design highly competitive networks that outperform existing networks using
the same design scheme. On CIFAR-10, our model without skip-connections
achieves 4.23\% test error rate, exceeding a vast majority of modern
architectures and approaching DenseNet. Furthermore, by applying our method to
explore the DenseNet architecture space, we are able to achieve more accurate
networks with fewer parameters.
Learning to Predict Indoor Illumination from a Single Image
Authors: Marc-André Gardner, Kalyan Sunkavalli, Ersin Yumer, Xiaohui Shen, Emiliano Gambaretto, Christian Gagné, Jean-François Lalonde
Date: 2017-04-01 00:00:00
Tasks: Lighting Estimation

We propose an automatic method to infer high dynamic range illumination from
a single, limited field-of-view, low dynamic range photograph of an indoor
scene. In contrast to previous work that relies on specialized image capture,
user input, and/or simple scene models, we train an end-to-end deep neural
network that directly regresses a limited field-of-view photo to HDR
illumination, without strong assumptions on scene geometry, material
properties, or lighting. We show that this can be accomplished in a three step
process: 1) we train a robust lighting classifier to automatically annotate the
location of light sources in a large dataset of LDR environment maps, 2) we use
these annotations to train a deep neural network that predicts the location of
lights in a scene from a single limited field-of-view photo, and 3) we
fine-tune this network using a small dataset of HDR environment maps to predict
light intensities. This allows us to automatically recover high-quality HDR
illumination estimates that significantly outperform previous state-of-the-art
methods. Consequently, using our illumination estimates for applications like
3D object insertion, we can achieve results that are photo-realistic, which is
validated via a perceptual user study.
The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers
Authors: Mika Viking Mäntylä, Daniel Graziotin, Miikka Kuutila
Date: 2016-12-05 00:00:00
Tasks: Sentiment Analysis, Subjectivity Analysis

Sentiment analysis is one of the fastest growing research areas in computer
science, making it challenging to keep track of all the activities in the area.
We present a computer-assisted literature review, where we utilize both text
mining and qualitative coding, and analyze 6,996 papers from Scopus. We find
that the roots of sentiment analysis are in the studies on public opinion
analysis at the beginning of 20th century and in the text subjectivity analysis
performed by the computational linguistics community in 1990's. However, the
outbreak of computer-based sentiment analysis only occurred with the
availability of subjective texts on the Web. Consequently, 99% of the papers
have been published after 2004. Sentiment analysis papers are scattered to
multiple publication venues, and the combined number of papers in the top-15
venues only represent ca. 30% of the papers in total. We present the top-20
cited papers from Google Scholar and Scopus and a taxonomy of research topics.
In recent years, sentiment analysis has shifted from analyzing online product
reviews to social media texts from Twitter and Facebook. Many topics beyond
product reviews like stock markets, elections, disasters, medicine, software
engineering and cyberbullying extend the utilization of sentiment analysis
Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition
Authors: Kaixuan Chen, Lina Yao, Tao Gu, Zhiwen Yu, Xianzhi Wang, Dalin Zhang
Date: 2017-11-21 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, valid

Multimodal features play a key role in wearable sensor based Human Activity
Recognition (HAR). Selecting the most salient features adaptively is a
promising way to maximize the effectiveness of multimodal sensor data. In this
regard, we propose a "collect fully and select wisely (Fullie and Wiselie)"
principle as well as a dual-stream recurrent convolutional attention model,
Recurrent Attention and Activity Frame (RAAF), to improve the recognition
performance. We first collect modality features and the relations between each
pair of features to generate activity frames, and then introduce an attention
mechanism to select the most prominent regions from activity frames precisely.
The selected frames not only maximize the utilization of valid features but
also reduce the number of features to be computed effectively. We further
analyze the hyper-parameters, accuracy, interpretability, and annotation
dependency of the proposed model based on extensive experiments. The results
show that RAAF achieves competitive performance on two benchmarked datasets and
works well in real life scenarios.
A Study on Modeling of Inputting Electrical Power of Ultra High Power Electric Furnace by using Fuzzy Rule and Regression Model
Authors: Choe Un-Chol, Yun Kum-Il, Kwak Son-Il
Date: 2017-11-21 00:00:00
Tasks: regression

: In this paper a method to make inputting electrical model upon factors that
affect melting process of high ultra power(UHP) electric furnace by using fuzzy
rule and regression model is suggested and its effectiveness is verified with
simulation experiment.
Towards Grounding Conceptual Spaces in Neural Representations
Authors: Lucas Bechberger, Kai-Uwe Kühnberger
Date: 2017-06-15 00:00:00

The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. It aims at bridging the gap between symbolic and
subsymbolic processing. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. In this paper, we present our approach towards grounding the dimensions
of a conceptual space in latent spaces learned by an InfoGAN from unlabeled
data.
Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Date: 2017-11-21 00:00:00

Temporal gates play a significant role in modern recurrent-based neural
encoders, enabling fine-grained control over recursive compositional operations
over time. In recurrent models such as the long short-term memory (LSTM),
temporal gates control the amount of information retained or discarded over
time, not only playing an important role in influencing the learned
representations but also serving as a protection against vanishing gradients.
This paper explores the idea of learning temporal gates for sequence pairs
(question and answer), jointly influencing the learned representations in a
pairwise manner. In our approach, temporal gates are learned via 1D
convolutional layers and then subsequently cross applied across question and
answer for joint learning. Empirically, we show that this conceptually simple
sharing of temporal gates can lead to competitive performance across multiple
benchmarks. Intuitively, what our network achieves can be interpreted as
learning representations of question and answer pairs that are aware of what
each other is remembering or forgetting, i.e., pairwise temporal gating. Via
extensive experiments, we show that our proposed model achieves
state-of-the-art performance on two community-based QA datasets and competitive
performance on one factoid-based QA dataset.
Genetic Algorithms for Evolving Computer Chess Programs
Authors: Eli David, H. Jaap van den Herik, Moshe Koppel, Nathan S. Netanyahu
Date: 2017-11-21 00:00:00

This paper demonstrates the use of genetic algorithms for evolving: 1) a
grandmaster-level evaluation function, and 2) a search mechanism for a chess
program, the parameter values of which are initialized randomly. The evaluation
function of the program is evolved by learning from databases of (human)
grandmaster games. At first, the organisms are evolved to mimic the behavior of
human grandmasters, and then these organisms are further improved upon by means
of coevolution. The search mechanism is evolved by learning from tactical test
suites. Our results show that the evolved program outperforms a two-time world
computer chess champion and is at par with the other leading computer chess
programs.
Genetic Algorithms for Evolving Deep Neural Networks
Authors: Eli David, Iddo Greental
Date: 2017-11-21 00:00:00
Tasks: Deep Learning

In recent years, deep learning methods applying unsupervised learning to
train deep layers of neural networks have achieved remarkable results in
numerous fields. In the past, many genetic algorithms based methods have been
successfully applied to training neural networks. In this paper, we extend
previous work and propose a GA-assisted method for deep learning. Our
experimental results indicate that this GA-assisted approach improves the
performance of a deep autoencoder, producing a sparser neural network.
Image Registration of Very Large Images via Genetic Programming
Authors: Sarit Chicotay, Eli David, Nathan S. Netanyahu
Date: 2017-11-17 00:00:00
Tasks: Diversity, Image Registration

Image registration (IR) is a fundamental task in image processing for
matching two or more images of the same scene taken at different times, from
different viewpoints and/or by different sensors. Due to the enormous diversity
of IR applications, automatic IR remains a challenging problem to this day. A
wide range of techniques has been developed for various data types and
problems. However, they might not handle effectively very large images, which
give rise usually to more complex transformations, e.g., deformations and
various other distortions.
  In this paper we present a genetic programming (GP)-based approach for IR,
which could offer a significant advantage in dealing with very large images, as
it does not make any prior assumptions about the transformation model. Thus, by
incorporating certain generic building blocks into the proposed GP framework,
we hope to realize a large set of specialized transformations that should yield
accurate registration of very large images.
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification
Authors: Daoyu Lin, Kun fu, Yang Wang, Guangluan Xu, Xian Sun
Date: 2016-12-28 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Remote Sensing Image Classification, Representation Learning

With the development of deep learning, supervised learning has frequently
been adopted to classify remotely sensed images using convolutional networks
(CNNs). However, due to the limited amount of labeled data available,
supervised learning is often difficult to carry out. Therefore, we proposed an
unsupervised model called multiple-layer feature-matching generative
adversarial networks (MARTA GANs) to learn a representation using only
unlabeled data. MARTA GANs consists of both a generative model $G$ and a
discriminative model $D$. We treat $D$ as a feature extractor. To fit the
complex properties of remote sensing data, we use a fusion layer to merge the
mid-level and global features. $G$ can produce numerous images that are similar
to the training data; therefore, $D$ can learn better representations of
remotely sensed images using the training data provided by $G$. The
classification results on two widely used remote sensing image databases show
that the proposed method significantly improves the classification performance
compared with other state-of-the-art methods.
Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics
Authors: Hongbin Pei, Bo Yang, Jiming Liu, Lei Dong
Date: 2017-11-21 00:00:00

Predicting epidemic dynamics is of great value in understanding and
controlling diffusion processes, such as infectious disease spread and
information propagation. This task is intractable, especially when surveillance
resources are very limited. To address the challenge, we study the problem of
active surveillance, i.e., how to identify a small portion of system components
as sentinels to effect monitoring, such that the epidemic dynamics of an entire
system can be readily predicted from the partial data collected by such
sentinels. We propose a novel measure, the gamma value, to identify the
sentinels by modeling a sentinel network with row sparsity structure. We design
a flexible group sparse Bayesian learning algorithm to mine the sentinel
network suitable for handling both linear and non-linear dynamical systems by
using the expectation maximization method and variational approximation. The
efficacy of the proposed algorithm is theoretically analyzed and empirically
validated using both synthetic and real-world data.