pwc-archive/papers-with-abstracts huggingface dataset:
On efficient global optimization via universal Kriging surrogate models
Authors: Pramudita Satria Palar, Koji Shimoyama
Date: 2018-03-23 00:00:00
Tasks: feature selection, global-optimization

In this paper, we investigate the capability of the universal Kriging (UK)
model for single-objective global optimization applied within an efficient
global optimization (EGO) framework. We implemented this combined UK-EGO
framework and studied four variants of the UK methods, that is, a UK with a
first-order polynomial, a UK with a second-order polynomial, a blind Kriging
(BK) implementation from the ooDACE toolbox, and a polynomial-chaos Kriging
(PCK) implementation. The UK-EGO framework with automatic trend function
selection derived from the BK and PCK models works by building a UK surrogate
model and then performing optimizations via expected improvement criteria on
the Kriging model with the lowest leave-one-out cross-validation error. Next,
we studied and compared the UK-EGO variants and standard EGO using five
synthetic test functions and one aerodynamic problem. Our results show that the
proper choice for the trend function through automatic feature selection can
improve the optimization performance of UK-EGO relative to EGO. From our
results, we found that PCK-EGO was the best variant, as it had more robust
performance as compared to the rest of the UK-EGO schemes; however, total-order
expansion should be used to generate the candidate trend function set for
high-dimensional problems. Note that, for some test functions, the UK with
predetermined polynomial trend functions performed better than that of BK and
PCK, indicating that the use of automatic trend function selection does not
always lead to the best quality solutions. We also found that although some
variants of UK are not as globally accurate as the ordinary Kriging (OK), they
can still identify better-optimized solutions due to the addition of the trend
function, which helps the optimizer locate the global optimum.
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
Authors: Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Date: 2018-03-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution

In recent years, deep learning methods have been successfully applied to
single-image super-resolution tasks. Despite their great performances, deep
learning methods cannot be easily applied to real-world applications due to the
requirement of heavy computation. In this paper, we address this issue by
proposing an accurate and lightweight deep network for image super-resolution.
In detail, we design an architecture that implements a cascading mechanism upon
a residual network. We also present variant models of the proposed cascading
residual network to further improve efficiency. Our extensive experiments show
that even with much fewer parameters and operations, our models achieve
performance comparable to that of state-of-the-art methods.
Bayesian Optimization with Expensive Integrands
Authors: Saul Toscano-Palmerin, Peter I. Frazier
Date: 2018-03-23 00:00:00
Tasks: Bayesian Optimization

We propose a Bayesian optimization algorithm for objective functions that are
sums or integrals of expensive-to-evaluate functions, allowing noisy
evaluations. These objective functions arise in multi-task Bayesian
optimization for tuning machine learning hyperparameters, optimization via
simulation, and sequential design of experiments with random environmental
conditions. Our method is average-case optimal by construction when a single
evaluation of the integrand remains within our evaluation budget. Achieving
this one-step optimality requires solving a challenging value of information
optimization problem, for which we provide a novel efficient
discretization-free computational method. We also provide consistency proofs
for our method in both continuum and discrete finite domains for objective
functions that are sums. In numerical experiments comparing against previous
state-of-the-art methods, including those that also leverage sum or integral
structure, our method performs as well or better across a wide range of
problems and offers significant improvements when evaluations are noisy or the
integrand varies smoothly in the integrated variables.
Lifting Layers: Analysis and Applications
Authors: Peter Ochs, Tim Meinhardt, Laura Leal-Taixe, Michael Moeller
Date: 2018-03-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Denoising, image-classification, Image Classification

The great advances of learning-based approaches in image processing and
computer vision are largely based on deeply nested networks that compose linear
transfer functions with suitable non-linearities. Interestingly, the most
frequently used non-linearities in imaging applications (variants of the
rectified linear unit) are uncommon in low dimensional approximation problems.
In this paper we propose a novel non-linear transfer function, called lifting,
which is motivated from a related technique in convex optimization. A lifting
layer increases the dimensionality of the input, naturally yields a linear
spline when combined with a fully connected layer, and therefore closes the gap
between low and high dimensional approximation problems. Moreover, applying the
lifting operation to the loss layer of the network allows us to handle
non-convex and flat (zero-gradient) cost functions. We analyze the proposed
lifting theoretically, exemplify interesting properties in synthetic
experiments and demonstrate its effectiveness in deep learning approaches to
image classification and denoising.
Studio Ousia's Quiz Bowl Question Answering System
Authors: Ikuya Yamada, Ryuji Tamaki, Hiroyuki Shindo, Yoshiyasu Takefuji
Date: 2018-03-23 00:00:00
Tasks: BIG-bench Machine Learning, Information Retrieval, Question Answering, Retrieval

In this chapter, we describe our question answering system, which was the
winning system at the Human-Computer Question Answering (HCQA) Competition at
the Thirty-first Annual Conference on Neural Information Processing Systems
(NIPS). The competition requires participants to address a factoid question
answering task referred to as quiz bowl. To address this task, we use two novel
neural network models and combine these models with conventional information
retrieval models using a supervised machine learning model. Our system achieved
the best performance among the systems submitted in the competition and won a
match against six top human quiz experts by a wide margin.
T-RECS: Training for Rate-Invariant Embeddings by Controlling Speed for Action Recognition
Authors: Madan Ravi Ganesh, Eric Hofesmann, Byungsu Min, Nadha Gafoor, Jason J. Corso
Date: 2018-03-21 00:00:00
Tasks: Action Recognition, Temporal Action Localization

An action should remain identifiable when modifying its speed: consider the
contrast between an expert chef and a novice chef each chopping an onion. Here,
we expect the novice chef to have a relatively measured and slow approach to
chopping when compared to the expert. In general, the speed at which actions
are performed, whether slower or faster than average, should not dictate how
they are recognized. We explore the erratic behavior caused by this phenomena
on state-of-the-art deep network-based methods for action recognition in terms
of maximum performance and stability in recognition accuracy across a range of
input video speeds. By observing the trends in these metrics and summarizing
them based on expected temporal behaviour w.r.t. variations in input video
speeds, we find two distinct types of network architectures. In this paper, we
propose a preprocessing method named T-RECS, as a way to extend
deep-network-based methods for action recognition to explicitly account for
speed variability in the data. We do so by adaptively resampling the inputs to
a given model. T-RECS is agnostic to the specific deep-network model; we apply
it to four state-of-the-art action recognition architectures, C3D, I3D, TSN,
and ConvNet+LSTM. On HMDB51 and UCF101, T-RECS-based I3D models show a peak
improvement of at least 2.9% in performance over the baseline while
T-RECS-based C3D models achieve a maximum improvement in stability by 59% over
the baseline, on the HMDB51 dataset.
Learning Recommendations While Influencing Interests
Authors: Rahul Meshram, D. Manjunath, Nikhil Karamchandani
Date: 2018-03-23 00:00:00
Tasks: Recommendation Systems

Personalized recommendation systems (RS) are extensively used in many
services. Many of these are based on learning algorithms where the RS uses the
recommendation history and the user response to learn an optimal strategy.
Further, these algorithms are based on the assumption that the user interests
are rigid. Specifically, they do not account for the effect of learning
strategy on the evolution of the user interests. In this paper we develop
influence models for a learning algorithm that is used to optimally recommend
websites to web users. We adapt the model of \cite{Ioannidis10} to include an
item-dependent reward to the RS from the suggestions that are accepted by the
user. For this we first develop a static optimisation scheme when all the
parameters are known. Next we develop a stochastic approximation based learning
scheme for the RS to learn the optimal strategy when the user profiles are not
known. Finally, we describe several user-influence models for the learning
algorithm and analyze their effect on the steady user interests and on the
steady state optimal strategy as compared to that when the users are not
influenced.
Fictitious GAN: Training GANs with Historical Models
Authors: Hao Ge, Yin Xia, Xu Chen, Randall Berry, Ying Wu
Date: 2018-03-23 00:00:00
Proceeding: ECCV 2018 9

Generative adversarial networks (GANs) are powerful tools for learning
generative models. In practice, the training may suffer from lack of
convergence. GANs are commonly viewed as a two-player zero-sum game between two
neural networks. Here, we leverage this game theoretic view to study the
convergence behavior of the training process. Inspired by the fictitious play
learning process, a novel training method, referred to as Fictitious GAN, is
introduced. Fictitious GAN trains the deep neural networks using a mixture of
historical models. Specifically, the discriminator (resp. generator) is updated
according to the best-response to the mixture outputs from a sequence of
previously trained generators (resp. discriminators). It is shown that
Fictitious GAN can effectively resolve some convergence issues that cannot be
resolved by the standard training approach. It is proved that asymptotically
the average of the generator outputs has the same distribution as the data
samples.
An end-to-end TextSpotter with Explicit Alignment and Attention
Authors: Tong He, Zhi Tian, Weilin Huang, Chunhua Shen, Yu Qiao, Changming Sun
Date: 2018-03-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Text Detection

Text detection and recognition in natural images have long been considered as
two separate tasks that are processed sequentially. Training of two tasks in a
unified framework is non-trivial due to significant dif- ferences in
optimisation difficulties. In this work, we present a conceptually simple yet
efficient framework that simultaneously processes the two tasks in one shot.
Our main contributions are three-fold: 1) we propose a novel text-alignment
layer that allows it to precisely compute convolutional features of a text
instance in ar- bitrary orientation, which is the key to boost the per-
formance; 2) a character attention mechanism is introduced by using character
spatial information as explicit supervision, leading to large improvements in
recognition; 3) two technologies, together with a new RNN branch for word
recognition, are integrated seamlessly into a single model which is end-to-end
trainable. This allows the two tasks to work collaboratively by shar- ing
convolutional features, which is critical to identify challenging text
instances. Our model achieves impressive results in end-to-end recognition on
the ICDAR2015 dataset, significantly advancing most recent results, with
improvements of F-measure from (0.54, 0.51, 0.47) to (0.82, 0.77, 0.63), by
using a strong, weak and generic lexicon respectively. Thanks to joint
training, our method can also serve as a good detec- tor by achieving a new
state-of-the-art detection performance on two datasets.
Conditional Image-Text Embedding Networks
Authors: Bryan A. Plummer, Paige Kordas, M. Hadi Kiapour, Shuai Zheng, Robinson Piramuthu, Svetlana Lazebnik
Date: 2017-11-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Phrase Grounding

This paper presents an approach for grounding phrases in images which jointly
learns multiple text-conditioned embeddings in a single end-to-end model. In
order to differentiate text phrases into semantically distinct subspaces, we
propose a concept weight branch that automatically assigns phrases to
embeddings, whereas prior works predefine such assignments. Our proposed
solution simplifies the representation requirements for individual embeddings
and allows the underrepresented concepts to take advantage of the shared
representations before feeding them into concept-specific layers. Comprehensive
experiments verify the effectiveness of our approach across three phrase
grounding datasets, Flickr30K Entities, ReferIt Game, and Visual Genome, where
we obtain a (resp.) 4%, 3%, and 4% improvement in grounding performance over a
strong region-phrase embedding baseline.
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection
Authors: Chunbiao Zhu, Xing Cai, Kan Huang, Thomas H. Li, Ge Li
Date: 2018-03-23 00:00:00
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Fully convolutional neural networks (FCNs) have shown outstanding performance
in many computer vision tasks including salient object detection. However,
there still remains two issues needed to be addressed in deep learning based
saliency detection. One is the lack of tremendous amount of annotated data to
train a network. The other is the lack of robustness for extracting salient
objects in images containing complex scenes. In this paper, we present a new
architecture$ - $PDNet, a robust prior-model guided depth-enhanced network for
RGB-D salient object detection. In contrast to existing works, in which RGB-D
values of image pixels are fed directly to a network, the proposed architecture
is composed of a master network for processing RGB values, and a sub-network
making full use of depth cues and incorporate depth-based features into the
master network. To overcome the limited size of the labeled RGB-D dataset for
training, we employ a large conventional RGB dataset to pre-train the master
network, which proves to contribute largely to the final accuracy. Extensive
evaluations over five benchmark datasets demonstrate that our proposed method
performs favorably against the state-of-the-art approaches.
Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis
Authors: Eduardo Valle, Michel Fornaciali, Afonso Menegola, Julia Tavares, Flávia Vasques Bittencourt, Lin Tzy Li, Sandra Avila
Date: 2017-11-01 00:00:00
Tasks: Data Augmentation, Transfer Learning

Deep learning fostered a leap ahead in automated skin lesion analysis in the last two years. Those models are expensive to train and difficult to parameterize. Objective: We investigate methodological issues for designing and evaluating deep learning models for skin lesion analysis. We explore 10 choices faced by researchers: use of transfer learning, model architecture, train dataset, image resolution, type of data augmentation, input normalization, use of segmentation, duration of training, additional use of SVMs, and test data augmentation. Methods: We perform two full factorial experiments, for five different test datasets, resulting in 2560 exhaustive trials in our main experiment, and 1280 trials in our assessment of transfer learning. We analyze both with multi-way ANOVA. We use the exhaustive trials to simulate sequential decisions and ensembles, with and without the use of privileged information from the test set. Results -- main experiment: Amount of train data has disproportionate influence, explaining almost half the variation in performance. Of the other factors, test data augmentation and input resolution are the most influential. Deeper models, when combined, with extra data, also help. -- transfer experiment: Transfer learning is critical, its absence brings huge performance penalties. -- simulations: Ensembles of models are the best option to provide reliable results with limited resources, without using privileged information and sacrificing methodological rigor. Conclusions and Significance: Advancing research on automated skin lesion analysis requires curating larger public datasets. Indirect use of privileged information from the test set to design the models is a subtle, but frequent methodological mistake that leads to overoptimistic results. Ensembles of models are a cost-effective alternative to the expensive full-factorial and to the unstable sequential designs.
Hardware based Spatio-Temporal Neural Processing Backend for Imaging Sensors: Towards a Smart Camera
Authors: Samiran Ganguly, Yunfei Gu, Mircea R. Stan, Avik W. Ghosh
Date: 2018-03-23 00:00:00
Tasks: object-detection, Object Detection, Sensitivity

In this work we show how we can build a technology platform for cognitive
imaging sensors using recent advances in recurrent neural network architectures
and training methods inspired from biology. We demonstrate learning and
processing tasks specific to imaging sensors, including enhancement of
sensitivity and signal-to-noise ratio (SNR) purely through neural filtering
beyond the fundamental limits sensor materials, and inferencing and
spatio-temporal pattern recognition capabilities of these networks with
applications in object detection, motion tracking and prediction. We then show
designs of unit hardware cells built using complementary metal-oxide
semiconductor (CMOS) and emerging materials technologies for ultra-compact and
energy-efficient embedded neural processors for smart cameras.
A Concept Learning Tool Based On Calculating Version Space Cardinality
Authors: Kuo-Kai Hsieh, Li-C. Wang
Date: 2018-03-23 00:00:00

In this paper, we proposed VeSC-CoL (Version Space Cardinality based Concept
Learning) to deal with concept learning on extremely imbalanced datasets,
especially when cross-validation is not a viable option. VeSC-CoL uses version
space cardinality as a measure for model quality to replace cross-validation.
Instead of naive enumeration of the version space, Ordered Binary Decision
Diagram and Boolean Satisfiability are used to compute the version space.
Experiments show that VeSC-CoL can accurately learn the target concept when
computational resource is allowed.
Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligence
Authors: G. A. Cox, S. Egly, G. R. Harp, J. Richards, S. Vinodababu, J. Voien
Date: 2018-03-23 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

We describe a new approach and algorithm for the detection of artificial
signals and their classification in the search for extraterrestrial
intelligence (SETI). The characteristics of radio signals observed during SETI
research are often most apparent when those signals are represented as
spectrograms. Additionally, many observed signals tend to share the same
characteristics, allowing for sorting of the signals into different classes.
For this work, complex-valued time-series data were simulated to produce a
corpus of 140,000 signals from seven different signal classes. A wide residual
neural network was then trained to classify these signal types using the
gray-scale 2D spectrogram representation of those signals. An average $F_1$
score of 95.11\% was attained when tested on previously unobserved simulated
signals. We also report on the performance of the model across a range of
signal amplitudes.
Visualizing and Understanding Atari Agents
Authors: Sam Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern
Date: 2017-10-31 00:00:00
Proceeding: ICML 2018 7
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

While deep reinforcement learning (deep RL) agents are effective at
maximizing rewards, it is often unclear what strategies they use to do so. In
this paper, we take a step toward explaining deep RL agents through a case
study using Atari 2600 environments. In particular, we focus on using saliency
maps to understand how an agent learns and executes a policy. We introduce a
method for generating useful saliency maps and use it to show 1) what strong
agents attend to, 2) whether agents are making decisions for the right or wrong
reasons, and 3) how agents evolve during learning. We also test our method on
non-expert human subjects and find that it improves their ability to reason
about these agents. Overall, our results show that saliency information can
provide significant insight into an RL agent's decisions and learning behavior.
MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification
Authors: Jeremy Barnes, Patrik Lambert, Toni Badia
Date: 2018-03-22 00:00:00
Proceeding: LREC 2018 5
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

While sentiment analysis has become an established field in the NLP
community, research into languages other than English has been hindered by the
lack of resources. Although much research in multi-lingual and cross-lingual
sentiment analysis has focused on unsupervised or semi-supervised approaches,
these still require a large number of resources and do not reach the
performance of supervised approaches. With this in mind, we introduce two
datasets for supervised aspect-level sentiment analysis in Basque and Catalan,
both of which are under-resourced languages. We provide high-quality
annotations and benchmarks with the hope that they will be useful to the
growing community of researchers working on these languages.
Iteratively Linearized Reweighted Alternating Direction Method of Multipliers for a Class of Nonconvex Problems
Authors: Tao Sun, Hao Jiang, Lizhi Cheng, Wei Zhu
Date: 2017-09-01 00:00:00

In this paper, we consider solving a class of nonconvex and nonsmooth
problems frequently appearing in signal processing and machine learning
research. The traditional alternating direction method of multipliers
encounters troubles in both mathematics and computations in solving the
nonconvex and nonsmooth subproblem. In view of this, we propose a reweighted
alternating direction method of multipliers. In this algorithm, all subproblems
are convex and easy to solve. We also provide several guarantees for the
convergence and prove that the algorithm globally converges to a critical point
of an auxiliary function with the help of the Kurdyka-{\L}ojasiewicz property.
Several numerical results are presented to demonstrate the efficiency of the
proposed algorithm.