pwc-archive/papers-with-abstracts huggingface dataset:
A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit
Authors: Mark W. Lewis
Date: 2017-05-31 00:00:00
Tasks: GPU

Multi-start algorithms are a common and effective tool for metaheuristic
searches. In this paper we amplify multi-start capabilities by employing the
parallel processing power of the graphics processer unit (GPU) to quickly
generate a diverse starting set of solutions for the Unconstrained Binary
Quadratic Optimization Problem which are evaluated and used to implement
screening methods to select solutions for further optimization. This method is
implemented as an initial high quality solution generation phase prior to a
secondary steepest ascent search and a comparison of results to best known
approaches on benchmark unconstrained binary quadratic problems demonstrates
that GPU-enabled diversified multi-start with screening quickly yields very
good results.
A Learning Based Optimal Human Robot Collaboration with Linear Temporal Logic Constraints
Authors: Bo Wu, Bin Hu, Hai Lin
Date: 2017-05-31 00:00:00

This paper considers an optimal task allocation problem for human robot
collaboration in human robot systems with persistent tasks. Such human robot
systems consist of human operators and intelligent robots collaborating with
each other to accomplish complex tasks that cannot be done by either part
alone. The system objective is to maximize the probability of successfully
executing persistent tasks that are formulated as linear temporal logic
specifications and minimize the average cost between consecutive visits of a
particular proposition. This paper proposes to model the human robot
collaboration under a framework with the composition of multiple Markov
Decision Process (MDP) with possibly unknown transition probabilities, which
characterizes how human cognitive states, such as human trust and fatigue,
stochastically change with the robot performance. Under the unknown MDP models,
an algorithm is developed to learn the model and obtain an optimal task
allocation policy that minimizes the expected average cost for each task cycle
and maximizes the probability of satisfying linear temporal logic constraints.
Moreover, this paper shows that the difference between the optimal policy based
on the learned model and that based on the underlying ground truth model can be
bounded by arbitrarily small constant and large confidence level with
sufficient samples. The case study of an assembly process demonstrates the
effectiveness and benefits of our proposed learning based human robot
collaboration.
U-Phylogeny: Undirected Provenance Graph Construction in the Wild
Authors: Aparna Bharati, Daniel Moreira, Allan Pinto, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha
Date: 2017-05-31 00:00:00
Tasks: graph construction, Misinformation

Deriving relationships between images and tracing back their history of
modifications are at the core of Multimedia Phylogeny solutions, which aim to
combat misinformation through doctored visual media. Nonetheless, most recent
image phylogeny solutions cannot properly address cases of forged composite
images with multiple donors, an area known as multiple parenting phylogeny
(MPP). This paper presents a preliminary undirected graph construction solution
for MPP, without any strict assumptions. The algorithm is underpinned by robust
image representative keypoints and different geometric consistency checks among
matching regions in both images to provide regions of interest for direct
comparison. The paper introduces a novel technique to geometrically filter the
most promising matches as well as to aid in the shared region localization
task. The strength of the approach is corroborated by experiments with
real-world cases, with and without image distractors (unrelated cases).
Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning
Authors: Li Lucy, Jon Gauthier
Date: 2017-05-31 00:00:00
Proceeding: WS 2017 8
Tasks: Word Similarity

Distributional word representation methods exploit word co-occurrences to
build compact vector encodings of words. While these representations enjoy
widespread use in modern natural language processing, it is unclear whether
they accurately encode all necessary facets of conceptual meaning. In this
paper, we evaluate how well these representations can predict perceptual and
conceptual features of concrete concepts, drawing on two semantic norm datasets
sourced from human participants. We find that several standard word
representations fail to encode many salient perceptual features of concepts,
and show that these deficits correlate with word-word similarity prediction
errors. Our analyses provide motivation for grounded and embodied language
learning approaches, which may help to remedy these deficits.
Gaussian process regression for forecasting battery state of health
Authors: Robert R. Richardson, Michael A. Osborne, David A. Howey
Date: 2017-03-16 00:00:00
Tasks: regression

Accurately predicting the future capacity and remaining useful life of
batteries is necessary to ensure reliable system operation and to minimise
maintenance costs. The complex nature of battery degradation has meant that
mechanistic modelling of capacity fade has thus far remained intractable;
however, with the advent of cloud-connected devices, data from cells in various
applications is becoming increasingly available, and the feasibility of
data-driven methods for battery prognostics is increasing. Here we propose
Gaussian process (GP) regression for forecasting battery state of health, and
highlight various advantages of GPs over other data-driven and mechanistic
approaches. GPs are a type of Bayesian non-parametric method, and hence can
model complex systems whilst handling uncertainty in a principled manner. Prior
information can be exploited by GPs in a variety of ways: explicit mean
functions can be used if the functional form of the underlying degradation
model is available, and multiple-output GPs can effectively exploit
correlations between data from different cells. We demonstrate the predictive
capability of GPs for short-term and long-term (remaining useful life)
forecasting on a selection of capacity vs. cycle datasets from lithium-ion
cells.
Learning When to Attend for Neural Machine Translation
Authors: Junhui Li, Muhua Zhu
Date: 2017-05-31 00:00:00
Tasks: Decoder, Machine Translation, Translation

In the past few years, attention mechanisms have become an indispensable
component of end-to-end neural machine translation models. However, previous
attention models always refer to some source words when predicting a target
word, which contradicts with the fact that some target words have no
corresponding source words. Motivated by this observation, we propose a novel
attention model that has the capability of determining when a decoder should
attend to source words and when it should not. Experimental results on NIST
Chinese-English translation tasks show that the new model achieves an
improvement of 0.8 BLEU score over a state-of-the-art baseline.
Reinforcement Learning for Learning Rate Control
Authors: Chang Xu, Tao Qin, Gang Wang, Tie-Yan Liu
Date: 2017-05-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Stochastic gradient descent (SGD), which updates the model parameters by
adding a local gradient times a learning rate at each step, is widely used in
model training of machine learning algorithms such as neural networks. It is
observed that the models trained by SGD are sensitive to learning rates and
good learning rates are problem specific. We propose an algorithm to
automatically learn learning rates using neural network based actor-critic
methods from deep reinforcement learning (RL).In particular, we train a policy
network called actor to decide the learning rate at each step during training,
and a value network called critic to give feedback about quality of the
decision (e.g., the goodness of the learning rate outputted by the actor) that
the actor made. The introduction of auxiliary actor and critic networks helps
the main network achieve better performance. Experiments on different datasets
and network architectures show that our approach leads to better convergence of
SGD than human-designed competitors.
Representation Learning by Rotating Your Faces
Authors: Luan Tran, Xi Yin, Xiaoming Liu
Date: 2017-05-31 00:00:00
Tasks: Decoder, Face Recognition, Generative Adversarial Network, Image Generation, Pose Estimation, Representation Learning, Robust Face Recognition

The large pose discrepancy between two face images is one of the fundamental
challenges in automatic face recognition. Conventional approaches to
pose-invariant face recognition either perform face frontalization on, or learn
a pose-invariant representation from, a non-frontal face image. We argue that
it is more desirable to perform both tasks jointly to allow them to leverage
each other. To this end, this paper proposes a Disentangled Representation
learning-Generative Adversarial Network (DR-GAN) with three distinct novelties.
First, the encoder-decoder structure of the generator enables DR-GAN to learn a
representation that is both generative and discriminative, which can be used
for face image synthesis and pose-invariant face recognition. Second, this
representation is explicitly disentangled from other face variations such as
pose, through the pose code provided to the decoder and pose estimation in the
discriminator. Third, DR-GAN can take one or multiple images as the input, and
generate one unified identity representation along with an arbitrary number of
synthetic face images. Extensive quantitative and qualitative evaluation on a
number of controlled and in-the-wild databases demonstrate the superiority of
DR-GAN over the state of the art in both learning representations and rotating
large-pose face images.
Coresets for Kernel Regression
Authors: Yan Zheng, Jeff M. Phillips
Date: 2017-02-13 00:00:00
Tasks: regression, Time Series, Time Series Analysis

Kernel regression is an essential and ubiquitous tool for non-parametric data
analysis, particularly popular among time series and spatial data. However, the
central operation which is performed many times, evaluating a kernel on the
data set, takes linear time. This is impractical for modern large data sets.
  In this paper we describe coresets for kernel regression: compressed data
sets which can be used as proxy for the original data and have provably bounded
worst case error. The size of the coresets are independent of the raw number of
data points, rather they only depend on the error guarantee, and in some cases
the size of domain and amount of smoothing. We evaluate our methods on very
large time series and spatial data, and demonstrate that they incur negligible
error, can be constructed extremely efficiently, and allow for great
computational gains.
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Authors: Linus Hamilton, Frederic Koehler, Ankur Moitra
Date: 2017-05-31 00:00:00
Proceeding: NeurIPS 2017 12

Markov random fields area popular model for high-dimensional probability
distributions. Over the years, many mathematical, statistical and algorithmic
problems on them have been studied. Until recently, the only known algorithms
for provably learning them relied on exhaustive search, correlation decay or
various incoherence assumptions. Bresler gave an algorithm for learning general
Ising models on bounded degree graphs. His approach was based on a structural
result about mutual information in Ising models.
  Here we take a more conceptual approach to proving lower bounds on the mutual
information through setting up an appropriate zero-sum game. Our proof
generalizes well beyond Ising models, to arbitrary Markov random fields with
higher order interactions. As an application, we obtain algorithms for learning
Markov random fields on bounded degree graphs on $n$ nodes with $r$-order
interactions in $n^r$ time and $\log n$ sample complexity. The sample
complexity is information theoretically optimal up to the dependence on the
maximum degree. The running time is nearly optimal under standard conjectures
about the hardness of learning parity with noise.
EvaluationNet: Can Human Skill be Evaluated by Deep Networks?
Authors: Seong Tae Kim, Yong Man Ro
Date: 2017-05-31 00:00:00

With the recent substantial growth of media such as YouTube, a considerable
number of instructional videos covering a wide variety of tasks are available
online. Therefore, online instructional videos have become a rich resource for
humans to learn everyday skills. In order to improve the effectiveness of the
learning with instructional video, observation and evaluation of the activity
are required. However, it is difficult to observe and evaluate every activity
steps by expert. In this study, a novel deep learning framework which targets
human activity evaluation for learning from instructional video has been
proposed. In order to deal with the inherent variability of activities, we
propose to model activity as a structured process. First, action units are
encoded from dense trajectories with LSTM network. The variable-length action
unit features are then evaluated by a Siamese LSTM network. By the comparative
experiments on public dataset, the effectiveness of the proposed method has
been demonstrated.
Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy
Authors: Laetitia Le, Camille Marini, Alexandre Gramfort, David Nguyen, Mehdi Cherti, Sana Tfaili, Ali Tfayli, Arlette Baillet-Guffroy, Patrice Prognon, Pierre Chaminade, Eric Caudron, Balázs Kégl
Date: 2017-05-19 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Monoclonal antibodies constitute one of the most important strategies to
treat patients suffering from cancers such as hematological malignancies and
solid tumors. In order to guarantee the quality of those preparations prepared
at hospital, quality control has to be developed. The aim of this study was to
explore a noninvasive, nondestructive, and rapid analytical method to ensure
the quality of the final preparation without causing any delay in the process.
We analyzed four mAbs (Inlfiximab, Bevacizumab, Ramucirumab and Rituximab)
diluted at therapeutic concentration in chloride sodium 0.9% using Raman
spectroscopy. To reduce the prediction errors obtained with traditional
chemometric data analysis, we explored a data-driven approach using statistical
machine learning methods where preprocessing and predictive models are jointly
optimized. We prepared a data analytics workflow and submitted the problem to a
collaborative data challenge platform called Rapid Analytics and Model
Prototyping (RAMP). This allowed to use solutions from about 300 data
scientists during five days of collaborative work. The prediction of the four
mAbs samples was considerably improved with a misclassification rate and the
mean error rate of 0.8% and 4%, respectively.
Neuron Segmentation Using Deep Complete Bipartite Networks
Authors: Jianxu Chen, Sreya Banerjee, Abhinav Grama, Walter J. Scheirer, Danny Z. Chen
Date: 2017-05-31 00:00:00
Tasks: Segmentation

In this paper, we consider the problem of automatically segmenting neuronal
cells in dual-color confocal microscopy images. This problem is a key task in
various quantitative analysis applications in neuroscience, such as tracing
cell genesis in Danio rerio (zebrafish) brains. Deep learning, especially using
fully convolutional networks (FCN), has profoundly changed segmentation
research in biomedical imaging. We face two major challenges in this problem.
First, neuronal cells may form dense clusters, making it difficult to correctly
identify all individual cells (even to human experts). Consequently,
segmentation results of the known FCN-type models are not accurate enough.
Second, pixel-wise ground truth is difficult to obtain. Only a limited amount
of approximate instance-wise annotation can be collected, which makes the
training of FCN models quite cumbersome. We propose a new FCN-type deep
learning model, called deep complete bipartite networks (CB-Net), and a new
scheme for leveraging approximate instance-wise annotation to train our
pixel-wise prediction model. Evaluated using seven real datasets, our proposed
new CB-Net model outperforms the state-of-the-art FCN models and produces
neuron segmentation results of remarkable quality
Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network
Authors: Neha Rani, Sharda Vashisth
Date: 2017-05-31 00:00:00
Tasks: General Classification

Brain is an organ that controls activities of all the parts of the body.
Recognition of automated brain tumor in Magnetic resonance imaging (MRI) is a
difficult task due to complexity of size and location variability. This
automatic method detects all the type of cancer present in the body. Previous
methods for tumor are time consuming and less accurate. In the present work,
statistical analysis morphological and thresholding techniques are used to
process the images obtained by MRI. Feed-forward back-prop neural network is
used to classify the performance of tumors part of the image. This method
results high accuracy and less iterations detection which further reduces the
consumption time.
Dynamic Discovery of Type Classes and Relations in Semantic Web Data
Authors: Serkan Ayvaz, Mehmet Aydar
Date: 2017-05-31 00:00:00
Tasks: Vocal Bursts Type Prediction

The continuing development of Semantic Web technologies and the increasing
user adoption in the recent years have accelerated the progress incorporating
explicit semantics with data on the Web. With the rapidly growing RDF (Resource
Description Framework) data on the Semantic Web, processing large semantic
graph data have become more challenging. Constructing a summary graph structure
from the raw RDF can help obtain semantic type relations and reduce the
computational complexity for graph processing purposes. In this paper, we
addressed the problem of graph summarization in RDF graphs, and we proposed an
approach for building summary graph structures automatically from RDF graph
data. Moreover, we introduced a measure to help discover optimum class
dissimilarity thresholds and an effective method to discover the type classes
automatically. In future work, we plan to investigate further improvement
options on the scalability of the proposed method.
A computational investigation of sources of variability in sentence comprehension difficulty in aphasia
Authors: Paul Mätzig, Shravan Vasishth, Felix Engelmann, David Caplan
Date: 2017-03-14 00:00:00
Tasks: Sentence

We present a computational evaluation of three hypotheses about sources of
deficit in sentence comprehension in aphasia: slowed processing, intermittent
deficiency, and resource reduction. The ACT-R based Lewis and Vasishth (2005)
model is used to implement these three proposals. Slowed processing is
implemented as slowed default production-rule firing time; intermittent
deficiency as increased random noise in activation of chunks in memory; and
resource reduction as reduced goal activation. As data, we considered subject
vs. object rela- tives whose matrix clause contained either an NP or a
reflexive, presented in a self-paced listening modality to 56 individuals with
aphasia (IWA) and 46 matched controls. The participants heard the sentences and
carried out a picture verification task to decide on an interpretation of the
sentence. These response accuracies are used to identify the best parameters
(for each participant) that correspond to the three hypotheses mentioned above.
We show that controls have more tightly clustered (less variable) parameter
values than IWA; specifically, compared to controls, among IWA there are more
individuals with low goal activations, high noise, and slow default action
times. This suggests that (i) individual patients show differential amounts of
deficit along the three dimensions of slowed processing, intermittent
deficient, and resource reduction, (ii) overall, there is evidence for all
three sources of deficit playing a role, and (iii) IWA have a more variable
range of parameter values than controls. In sum, this study contributes a proof
of concept of a quantitative implementation of, and evidence for, these three
accounts of comprehension deficits in aphasia.
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation
Authors: Zhiyuan Zha, Xin Yuan, Bei Li, Xinggan Zhang, Xin Liu, Lan Tang, Ying-Chang Liang
Date: 2017-02-15 00:00:00
Tasks: Denoising, Image Denoising, Image Inpainting

Rank minimization methods have attracted considerable interest in various
areas, such as computer vision and machine learning. The most representative
work is nuclear norm minimization (NNM), which can recover the matrix rank
exactly under some restricted and theoretical guarantee conditions. However,
for many real applications, NNM is not able to approximate the matrix rank
accurately, since it often tends to over-shrink the rank components. To rectify
the weakness of NNM, recent advances have shown that weighted nuclear norm
minimization (WNNM) can achieve a better matrix rank approximation than NNM,
which heuristically set the weight being inverse to the singular values.
However, it still lacks a sound mathematical explanation on why WNNM is more
feasible than NNM. In this paper, we propose a scheme to analyze WNNM and NNM
from the perspective of the group sparse representation. Specifically, we
design an adaptive dictionary to bridge the gap between the group sparse
representation and the rank minimization models. Based on this scheme, we
provide a mathematical derivation to explain why WNNM is more feasible than
NNM. Moreover, due to the heuristical set of the weight, WNNM sometimes pops
out error in the operation of SVD, and thus we present an adaptive weight
setting scheme to avoid this error. We then employ the proposed scheme on two
low-level vision tasks including image denoising and image inpainting.
Experimental results demonstrate that WNNM is more feasible than NNM and the
proposed scheme outperforms many current state-of-the-art methods.
Criticality & Deep Learning II: Momentum Renormalisation Group
Authors: Dan Oprisa, Peter Toth
Date: 2017-05-31 00:00:00
Tasks: Deep Learning

Guided by critical systems found in nature we develop a novel mechanism
consisting of inhomogeneous polynomial regularisation via which we can induce
scale invariance in deep learning systems. Technically, we map our deep
learning (DL) setup to a genuine field theory, on which we act with the
Renormalisation Group (RG) in momentum space and produce the flow equations of
the couplings; those are translated to constraints and consequently interpreted
as "critical regularisation" conditions in the optimiser; the resulting
equations hence prove to be sufficient conditions for - and serve as an elegant
and simple mechanism to induce scale invariance in any deep learning setup.
Criticality & Deep Learning I: Generally Weighted Nets
Authors: Dan Oprisa, Peter Toth
Date: 2017-02-26 00:00:00
Tasks: Deep Learning

Motivated by the idea that criticality and universality of phase transitions
might play a crucial role in achieving and sustaining learning and intelligent
behaviour in biological and artificial networks, we analyse a theoretical and a
pragmatic experimental set up for critical phenomena in deep learning. On the
theoretical side, we use results from statistical physics to carry out critical
point calculations in feed-forward/fully connected networks, while on the
experimental side we set out to find traces of criticality in deep neural
networks. This is our first step in a series of upcoming investigations to map
out the relationship between criticality and learning in deep networks.