pwc-archive/papers-with-abstracts huggingface dataset:
irbasis: Open-source database and software for intermediate-representation basis functions of imaginary-time Green's function
Authors: Naoya Chikano, Kazuyoshi Yoshimi, Junya Otsuki, Hiroshi Shinaoka
Date: 2018-07-13 00:00:00

The open-source library, irbasis, provides easy-to-use tools for two sets of
orthogonal functions named intermediate representation (IR). The IR basis
enables a compact representation of the Matsubara Green's function and
efficient calculations of quantum models. The IR basis functions are defined as
the solution of an integral equation whose analytical solution is not available
for this moment. The library consists of a database of pre-computed
high-precision numerical solutions and computational code for evaluating the
functions from the database. This paper describes technical details and
demonstrates how to use the library.
Forecasting market states
Authors: Pier Francesco Procacci, Tomaso Aste
Date: 2018-07-13 00:00:00
Tasks: Clustering, Management

We propose a novel methodology to define, analyze and forecast market states. In our approach market states are identified by a reference sparse precision matrix and a vector of expectation values. In our procedure, each multivariate observation is associated with a given market state accordingly to a minimization of a penalized Mahalanobis distance. The procedure is made computationally very efficient and can be used with a large number of assets. We demonstrate that this procedure is successful at clustering different states of the markets in an unsupervised manner. In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates states prevalently to pre- and post- crisis periods with one state gathering periods with average positive returns and the other state periods with average negative returns, therefore discovering spontaneously the common classification of `bull' and `bear' markets. In another experiment, with again one hundred log-returns and two states, we demonstrate that this procedure can be efficiently used to forecast off-sample future market states with significant prediction accuracy. This methodology opens the way to a range of applications in risk management and trading strategies in the context where the correlation structure plays a central role.
Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception
Authors: German I. Parisi, Jonathan Tong, Pablo Barros, Brigitte Röder, Stefan Wermter
Date: 2018-07-13 00:00:00
Tasks: Causal Inference

Our daily perceptual experience is driven by different neural mechanisms that
yield multisensory interaction as the interplay between exogenous stimuli and
endogenous expectations. While the interaction of multisensory cues according
to their spatiotemporal properties and the formation of multisensory
feature-based representations have been widely studied, the interaction of
spatial-associative neural representations has received considerably less
attention. In this paper, we propose a neural network architecture that models
the interaction of spatial-associative representations to perform causal
inference of audiovisual stimuli. We investigate the spatial alignment of
exogenous audiovisual stimuli modulated by associative congruence. In the
spatial layer, topographically arranged networks account for the interaction of
audiovisual input in terms of population codes. In the associative layer,
congruent audiovisual representations are obtained via the experience-driven
development of feature-based associations. Levels of congruency are obtained as
a by-product of the neurodynamics of self-organizing networks, where the amount
of neural activation triggered by the input can be expressed via a nonlinear
distance function. Our novel proposal is that activity-driven levels of
congruency can be used as top-down modulatory projections to spatially
distributed representations of sensory input, e.g. semantically related
audiovisual pairs will yield a higher level of integration than unrelated
pairs. Furthermore, levels of neural response in unimodal layers may be seen as
sensory reliability for the dynamic weighting of crossmodal cues. We describe a
series of planned experiments to validate our model in the tasks of
multisensory interaction on the basis of semantic congruence and unimodal cue
reliability.
Safe Reinforcement Learning via Probabilistic Shields
Authors: Nils Jansen, Bettina Könighofer, Sebastian Junges, Alexandru C. Serban, Roderick Bloem
Date: 2018-07-16 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration, Safe Reinforcement Learning

This paper targets the efficient construction of a safety shield for decision making in scenarios that incorporate uncertainty. Markov decision processes (MDPs) are prominent models to capture such planning problems. Reinforcement learning (RL) is a machine learning technique to determine near-optimal policies in MDPs that may be unknown prior to exploring the model. However, during exploration, RL is prone to induce behavior that is undesirable or not allowed in safety- or mission-critical contexts. We introduce the concept of a probabilistic shield that enables decision-making to adhere to safety constraints with high probability. In a separation of concerns, we employ formal verification to efficiently compute the probabilities of critical decisions within a safety-relevant fragment of the MDP. We use these results to realize a shield that is applied to an RL algorithm which then optimizes the actual performance objective. We discuss tradeoffs between sufficient progress in exploration of the environment and ensuring safety. In our experiments, we demonstrate on the arcade game PAC-MAN and on a case study involving service robots that the learning efficiency increases as the learning needs orders of magnitude fewer episodes.
Practical and Optimal LSH for Angular Distance
Authors: Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt
Date: 2015-09-09 00:00:00
Proceeding: NeurIPS 2015 12

We show the existence of a Locality-Sensitive Hashing (LSH) family for the
angular distance that yields an approximate Near Neighbor Search algorithm with
the asymptotically optimal running time exponent. Unlike earlier algorithms
with this property (e.g., Spherical LSH [Andoni, Indyk, Nguyen, Razenshteyn
2014], [Andoni, Razenshteyn 2015]), our algorithm is also practical, improving
upon the well-studied hyperplane LSH [Charikar, 2002] in practice. We also
introduce a multiprobe version of this algorithm, and conduct experimental
evaluation on real and synthetic data sets.
  We complement the above positive results with a fine-grained lower bound for
the quality of any LSH family for angular distance. Our lower bound implies
that the above LSH family exhibits a trade-off between evaluation time and
quality that is close to optimal for a natural class of LSH functions.
Using semantic clustering to support situation awareness on Twitter: The case of World Views
Authors: Charlie Kingston, Jason R. C. Nurse, Ioannis Agrafiotis, Andrew Milich
Date: 2018-07-17 00:00:00
Tasks: Clustering, Decision Making, Management

In recent years, situation awareness has been recognised as a critical part
of effective decision making, in particular for crisis management. One way to
extract value and allow for better situation awareness is to develop a system
capable of analysing a dataset of multiple posts, and clustering consistent
posts into different views or stories (or, world views). However, this can be
challenging as it requires an understanding of the data, including determining
what is consistent data, and what data corroborates other data. Attempting to
address these problems, this article proposes Subject-Verb-Object Semantic
Suffix Tree Clustering (SVOSSTC) and a system to support it, with a special
focus on Twitter content. The novelty and value of SVOSSTC is its emphasis on
utilising the Subject-Verb-Object (SVO) typology in order to construct
semantically consistent world views, in which individuals---particularly those
involved in crisis response---might achieve an enhanced picture of a situation
from social media data. To evaluate our system and its ability to provide
enhanced situation awareness, we tested it against existing approaches,
including human data analysis, using a variety of real-world scenarios. The
results indicated a noteworthy degree of evidence (e.g., in cluster granularity
and meaningfulness) to affirm the suitability and rigour of our approach.
Moreover, these results highlight this article's proposals as innovative and
practical system contributions to the research field.
Deep Exemplar-based Colorization
Authors: Mingming He, Dong-Dong Chen, Jing Liao, Pedro V. Sander, Lu Yuan
Date: 2018-07-17 00:00:00
Tasks: Colorization, Image Retrieval, Retrieval

We propose the first deep learning approach for exemplar-based local
colorization. Given a reference color image, our convolutional neural network
directly maps a grayscale image to an output colorized image. Rather than using
hand-crafted rules as in traditional exemplar-based methods, our end-to-end
colorization network learns how to select, propagate, and predict colors from
the large-scale data. The approach performs robustly and generalizes well even
when using reference images that are unrelated to the input grayscale image.
More importantly, as opposed to other learning-based colorization methods, our
network allows the user to achieve customizable results by simply feeding
different references. In order to further reduce manual effort in selecting the
references, the system automatically recommends references with our proposed
image retrieval algorithm, which considers both semantic and luminance
information. The colorization can be performed fully automatically by simply
picking the top reference suggestion. Our approach is validated through a user
study and favorable quantitative comparisons to the-state-of-the-art methods.
Furthermore, our approach can be naturally extended to video colorization. Our
code and models will be freely available for public use.
Interpretable Latent Spaces for Learning from Demonstration
Authors: Yordan Hristov, Alex Lascarides, Subramanian Ramamoorthy
Date: 2018-07-17 00:00:00

Effective human-robot interaction, such as in robot learning from human
demonstration, requires the learning agent to be able to ground abstract
concepts (such as those contained within instructions) in a corresponding
high-dimensional sensory input stream from the world. Models such as deep
neural networks, with high capacity through their large parameter spaces, can
be used to compress the high-dimensional sensory data to lower dimensional
representations. These low-dimensional representations facilitate symbol
grounding, but may not guarantee that the representation would be
human-interpretable. We propose a method which utilises the grouping of
user-defined symbols and their corresponding sensory observations in order to
align the learnt compressed latent representation with the semantic notions
contained in the abstract labels. We demonstrate this through experiments with
both simulated and real-world object data, showing that such alignment can be
achieved in a process of physical symbol grounding.
Comparison of RNN Encoder-Decoder Models for Anomaly Detection
Authors: YeongHyeon Park, Il Dong Yun
Date: 2018-07-17 00:00:00
Tasks: Anomaly Detection, Decoder

In this paper, we compare different types of Recurrent Neural Network (RNN)
Encoder-Decoders in anomaly detection viewpoint. We focused on finding the
model that can learn the same data more effectively. We compared multiple
models under the same conditions, such as the number of parameters, optimizer,
and learning rate. However, the difference is whether to predict the future
sequence or restore the current sequence. We constructed the dataset with
simple vectors and used them for the experiment. Finally, we experimentally
confirmed that the model performs better when the model restores the current
sequence, rather than predict the future sequence.
Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization
Authors: Rishabh Iyer, John T. Halloran, Kai Wei
Date: 2018-07-17 00:00:00
Tasks: BIG-bench Machine Learning, regression

This paper introduces Jensen, an easily extensible and scalable toolkit for
production-level machine learning and convex optimization. Jensen implements a
framework of convex (or loss) functions, convex optimization algorithms
(including Gradient Descent, L-BFGS, Stochastic Gradient Descent, Conjugate
Gradient, etc.), and a family of machine learning classifiers and regressors
(Logistic Regression, SVMs, Least Square Regression, etc.). This framework
makes it possible to deploy and train models with a few lines of code, and also
extend and build upon this by integrating new loss functions and optimization
algorithms.
Explicating feature contribution using Random Forest proximity distances
Authors: Leanne S. Whitmore, Anthe George, Corey M. Hudson
Date: 2018-07-17 00:00:00
Tasks: Decision Making, General Classification

In Random Forests, proximity distances are a metric representation of data
into decision space. By observing how changes in input map to the movement of
instances in this space we are able to determine the independent contribution
of each feature to the decision-making process. For binary feature vectors,
this process is fully specified. As these changes in input move particular
instances nearer to the in-group or out-group, the independent contribution of
each feature can be uncovered. Using this technique, we are able to calculate
the contribution of each feature in determining how black-box decisions were
made. This allows explication of the decision-making process, audit of the
classifier, and post-hoc analysis of errors in classification.
Using link and content over time for embedding generation in Dynamic Attributed Networks
Authors: Ana Paula Appel, Renato L. F. Cunha, Charu C. Aggarwal, Marcela Megumi Terakado
Date: 2018-07-17 00:00:00
Tasks: Community Detection, Question Answering

In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks. Such temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurring over multiple snapshots. Incorporating dynamic changes in the communities into the analysis can also provide useful insights about the changes in the network such as the migration of authors across communities. In this work, we propose Chimera, a shared factorization model that can simultaneously account for graph links, content, and temporal analysis. This approach works by extracting the latent semantic structure of the network in multidimensional form, but in a way that takes into account the temporal continuity of these embeddings. Such an approach simplifies temporal analysis of the underlying network by using the embedding as a surrogate. A consequence of this simplification is that it is also possible to use this temporal sequence of embeddings to predict future communities. We present experimental results illustrating the effectiveness of the approach.
Power Networks: A Novel Neural Architecture to Predict Power Relations
Authors: Michelle Lam, Catherina Xu, Angela Kong, Vinodkumar Prabhakaran
Date: 2018-07-17 00:00:00
Proceeding: COLING 2018 8

Can language analysis reveal the underlying social power relations that exist
between participants of an interaction? Prior work within NLP has shown promise
in this area, but the performance of automatically predicting power relations
using NLP analysis of social interactions remains wanting. In this paper, we
present a novel neural architecture that captures manifestations of power
within individual emails which are then aggregated in an order-preserving way
in order to infer the direction of power between pairs of participants in an
email thread. We obtain an accuracy of 80.4%, a 10.1% improvement over
state-of-the-art methods, in this task. We further apply our model to the task
of predicting power relations between individuals based on the entire set of
messages exchanged between them; here also, our model significantly outperforms
the70.0% accuracy using prior state-of-the-art techniques, obtaining an
accuracy of 83.0%.
Training Recurrent Neural Networks against Noisy Computations during Inference
Authors: Minghai Qin, Dejan Vucinic
Date: 2018-07-17 00:00:00
Tasks: CPU, GPU, speech-recognition, Speech Recognition

We explore the robustness of recurrent neural networks when the computations
within the network are noisy. One of the motivations for looking into this
problem is to reduce the high power cost of conventional computing of neural
network operations through the use of analog neuromorphic circuits. Traditional
GPU/CPU-centered deep learning architectures exhibit bottlenecks in
power-restricted applications, such as speech recognition in embedded systems.
The use of specialized neuromorphic circuits, where analog signals passed
through memory-cell arrays are sensed to accomplish matrix-vector
multiplications, promises large power savings and speed gains but brings with
it the problems of limited precision of computations and unavoidable analog
noise.
  In this paper we propose a method, called {\em Deep Noise Injection
training}, to train RNNs to obtain a set of weights/biases that is much more
robust against noisy computation during inference. We explore several RNN
architectures, such as vanilla RNN and long-short-term memories (LSTM), and
show that after convergence of Deep Noise Injection training the set of trained
weights/biases has more consistent performance over a wide range of noise
powers entering the network during inference. Surprisingly, we find that Deep
Noise Injection training improves overall performance of some networks even for
numerically accurate inference.
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections
Authors: David George, Xianguha Xie, Yu-Kun Lai, Gary KL Tam
Date: 2018-07-17 00:00:00
Tasks: Active Learning, Segmentation, Semantic Segmentation

High-level shape understanding and technique evaluation on large repositories
of 3D shapes often benefit from additional information known about the shapes.
One example of such information is the semantic segmentation of a shape into
functional or meaningful parts. Generating accurate segmentations with
meaningful segment boundaries is, however, a costly process, typically
requiring large amounts of user time to achieve high quality results. In this
paper we present an active learning framework for large dataset segmentation,
which iteratively provides the user with new predictions by training new models
based on already segmented shapes. Our proposed pipeline consists of three
novel components. First, we a propose a fast and relatively accurate
feature-based deep learning model to provide dataset-wide segmentation
predictions. Second, we propose an information theory measure to estimate the
prediction quality and for ordering subsequent fast and meaningful shape
selection. Our experiments show that such suggestive ordering helps reduce
users time and effort, produce high quality predictions, and construct a model
that generalizes well. Finally, we provide effective segmentation refinement
features to help the user quickly correct any incorrect predictions. We show
that our framework is more accurate and in general more efficient than
state-of-the-art, for massive dataset segmentation with while also providing
consistent segment boundaries.
Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning
Authors: Tomohiko Konno, Michiaki Iwazume
Date: 2018-07-17 00:00:00
Tasks: Deep Learning, General Classification

We found an easy and quick post-learning method named "Icing on the Cake" to
enhance a classification performance in deep learning. The method is that we
train only the final classifier again after an ordinary training is done.
Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learning
Authors: Tomohiko Konno, Michiaki Iwazume
Date: 2018-07-17 00:00:00

Herein, we generate pseudo-features based on the multivariate probability distributions obtained from the feature maps in layers of trained deep neural networks. Further, we augment the minor-class data based on these generated pseudo-features to overcome the imbalanced data problems. The proposed method, i.e., cavity filling, improves the deep learning capabilities in several problems because all the real-world data are observed to be imbalanced.
PIMMS: Permutation Invariant Multi-Modal Segmentation
Authors: Thomas Varsavsky, Zach Eaton-Rosen, Carole H. Sudre, Parashkev Nachev, M. Jorge Cardoso
Date: 2018-07-17 00:00:00
Tasks: Segmentation

In a research context, image acquisition will often involve a pre-defined
static protocol and the data will be of high quality. If we are to build
applications that work in hospitals without significant operational changes in
care delivery, algorithms should be designed to cope with the available data in
the best possible way. In a clinical environment, imaging protocols are highly
flexible, with MRI sequences commonly missing appropriate sequence labeling
(e.g. T1, T2, FLAIR). To this end we introduce PIMMS, a Permutation Invariant
Multi-Modal Segmentation technique that is able to perform inference over sets
of MRI scans without using modality labels. We present results which show that
our convolutional neural network can, in some settings, outperform a baseline
model which utilizes modality labels, and achieve comparable performance
otherwise.
A framework for remote sensing images processing using deep learning technique
Authors: Rémi Cresson
Date: 2018-07-17 00:00:00
Tasks: Deep Learning

Deep learning techniques are becoming increasingly important to solve a
number of image processing tasks. Among common algorithms, Convolutional Neural
Networks and Recurrent Neural Networks based systems achieve state of the art
results on satellite and aerial imagery in many applications. While these
approaches are subject to scientific interest, there is currently no
operational and generic implementation available at user-level for the remote
sensing community. In this paper, we presents a framework enabling the use of
deep learning techniques with remote sensing images and geospatial data. Our
solution takes roots in two extensively used open-source libraries, the remote
sensing image processing library Orfeo ToolBox, and the high performance
numerical computation library TensorFlow. It can apply deep nets without
restriction on images size and is computationally efficient, regardless
hardware configuration.
An Acceleration Scheme for Memory Limited, Streaming PCA
Authors: Salaheddin Alakkari, John Dingliana
Date: 2018-07-17 00:00:00

In this paper, we propose an acceleration scheme for online memory-limited
PCA methods. Our scheme converges to the first $k>1$ eigenvectors in a single
data pass. We provide empirical convergence results of our scheme based on the
spiked covariance model. Our scheme does not require any predefined parameters
such as the eigengap and hence is well facilitated for streaming data
scenarios. Furthermore, we apply our scheme to challenging time-varying systems
where online PCA methods fail to converge. Specifically, we discuss a family of
time-varying systems that are based on Molecular Dynamics simulations where
batch PCA converges to the actual analytic solution of such systems.