pwc-archive/papers-with-abstracts huggingface dataset:
PyPhi: A toolbox for integrated information theory
Authors: William G. P. Mayner, William Marshall, Larissa Albantakis, Graham Findlay, Robert Marchman, Giulio Tononi
Date: 2017-12-27 00:00:00

Integrated information theory provides a mathematical framework to fully
characterize the cause-effect structure of a physical system. Here, we
introduce PyPhi, a Python software package that implements this framework for
causal analysis and unfolds the full cause-effect structure of discrete
dynamical systems of binary elements. The software allows users to easily study
these structures, serves as an up-to-date reference implementation of the
formalisms of integrated information theory, and has been applied in research
on complexity, emergence, and certain biological questions. We first provide an
overview of the main algorithm and demonstrate PyPhi's functionality in the
course of analyzing an example system, and then describe details of the
algorithm's design and implementation.
  PyPhi can be installed with Python's package manager via the command 'pip
install pyphi' on Linux and macOS systems equipped with Python 3.4 or higher.
PyPhi is open-source and licensed under the GPLv3; the source code is hosted on
GitHub at https://github.com/wmayner/pyphi . Comprehensive and
continually-updated documentation is available at https://pyphi.readthedocs.io/
. The pyphi-users mailing list can be joined at
https://groups.google.com/forum/#!forum/pyphi-users . A web-based graphical
interface to the software is available at
http://integratedinformationtheory.org/calculate.html .
Matching Networks for One Shot Learning
Authors: Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
Date: 2016-06-13 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Few-Shot Image Classification, Few-Shot Learning, Language Modeling, Language Modelling, Metric Learning, One-Shot Learning

Learning from a few examples remains a key challenge in machine learning.
Despite recent advances in important domains such as vision and language, the
standard supervised deep learning paradigm does not offer a satisfactory
solution for learning new concepts rapidly from little data. In this work, we
employ ideas from metric learning based on deep neural features and from recent
advances that augment neural networks with external memories. Our framework
learns a network that maps a small labelled support set and an unlabelled
example to its label, obviating the need for fine-tuning to adapt to new class
types. We then define one-shot learning problems on vision (using Omniglot,
ImageNet) and language tasks. Our algorithm improves one-shot accuracy on
ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to
competing approaches. We also demonstrate the usefulness of the same model on
language modeling by introducing a one-shot task on the Penn Treebank.
Boosting the Actor with Dual Critic
Authors: Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song
Date: 2017-12-29 00:00:00
Proceeding: ICLR 2018 1

This paper proposes a new actor-critic-style algorithm called Dual
Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian
dual form of the Bellman optimality equation, which can be viewed as a
two-player game between the actor and a critic-like function, which is named as
dual critic. Compared to its actor-critic relatives, Dual-AC has the desired
property that the actor and dual critic are updated cooperatively to optimize
the same objective function, providing a more transparent way for learning the
critic that is directly related to the objective function of the actor. We then
provide a concrete algorithm that can effectively solve the minimax
optimization problem, using techniques of multi-step bootstrapping, path
regularization, and stochastic dual ascent algorithm. We demonstrate that the
proposed algorithm achieves the state-of-the-art performances across several
benchmarks.
Towards an unanimous international regulatory body for responsible use of Artificial Intelligence [UIRB-AI]
Authors: Rajesh Chidambaram
Date: 2017-12-21 00:00:00

Artificial Intelligence (AI), is once again in the phase of drastic
advancements. Unarguably, the technology itself can revolutionize the way we
live our everyday life. But the exponential growth of technology poses a
daunting task for policy researchers and law makers in making amendments to the
existing norms. In addition, not everyone in the society is studying the
potential socio-economic intricacies and cultural drifts that AI can bring
about. It is prudence to reflect from our historical past to propel the
development of technology in the right direction. To benefit the society of the
present and future, I scientifically explore the societal impact of AI. While
there are many public and private partnerships working on similar aspects, here
I describe the necessity for an Unanimous International Regulatory Body for all
applications of AI (UIRB-AI). I also discuss the benefits and drawbacks of such
an organization. To combat any drawbacks in the formation of an UIRB-AI, both
idealistic and pragmatic perspectives are discussed alternatively. The paper
further advances the discussion by proposing novel policies on how such
organization should be structured and how it can bring about a win-win
situation for everyone in the society.
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Authors: Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi Jaakkola
Date: 2017-09-13 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Computational chemistry

The prediction of organic reaction outcomes is a fundamental problem in
computational chemistry. Since a reaction may involve hundreds of atoms, fully
exploring the space of possible transformations is intractable. The current
solution utilizes reaction templates to limit the space, but it suffers from
coverage and efficiency issues. In this paper, we propose a template-free
approach to efficiently explore the space of product molecules by first
pinpointing the reaction center -- the set of nodes and edges where graph edits
occur. Since only a small number of atoms contribute to reaction center, we can
directly enumerate candidate products. The generated candidates are scored by a
Weisfeiler-Lehman Difference Network that models high-order interactions
between changes occurring at nodes across the molecule. Our framework
outperforms the top-performing template-based approach with a 10\% margin,
while running orders of magnitude faster. Finally, we demonstrate that the
model accuracy rivals the performance of domain experts.
Tracking the Diffusion of Named Entities
Authors: Leon Derczynski, Matthew Rowe
Date: 2017-12-22 00:00:00
Tasks: Entity Extraction using GAN

Existing studies of how information diffuses across social networks have thus
far concentrated on analysing and recovering the spread of deterministic
innovations such as URLs, hashtags, and group membership. However investigating
how mentions of real-world entities appear and spread has yet to be explored,
largely due to the computationally intractable nature of performing large-scale
entity extraction. In this paper we present, to the best of our knowledge, one
of the first pieces of work to closely examine the diffusion of named entities
on social media, using Reddit as our case study platform. We first investigate
how named entities can be accurately recognised and extracted from discussion
posts. We then use these extracted entities to study the patterns of entity
cascades and how the probability of a user adopting an entity (i.e. mentioning
it) is associated with exposures to the entity. We put these pieces together by
presenting a parallelised diffusion model that can forecast the probability of
entity adoption, finding that the influence of adoption between users can be
characterised by their prior interactions -- as opposed to whether the users
propagated entity-adoptions beforehand. Our findings have important
implications for researchers studying influence and language, and for community
analysts who wish to understand entity-level influence dynamics.
Data-dependent Generalization Bounds for Multi-class Classification
Authors: Yunwen Lei, Urun Dogan, Ding-Xuan Zhou, Marius Kloft
Date: 2017-06-29 00:00:00
Tasks: Classification, General Classification, Generalization Bounds, Multi-class Classification

In this paper, we study data-dependent generalization error bounds exhibiting
a mild dependency on the number of classes, making them suitable for
multi-class learning with a large number of label classes. The bounds generally
hold for empirical multi-class risk minimization algorithms using an arbitrary
norm as regularizer. Key to our analysis are new structural results for
multi-class Gaussian complexities and empirical $\ell_\infty$-norm covering
numbers, which exploit the Lipschitz continuity of the loss function with
respect to the $\ell_2$- and $\ell_\infty$-norm, respectively. We establish
data-dependent error bounds in terms of complexities of a linear function class
defined on a finite set induced by training examples, for which we show tight
lower and upper bounds. We apply the results to several prominent multi-class
learning machines, exhibiting a tighter dependency on the number of classes
than the state of the art. For instance, for the multi-class SVM by Crammer and
Singer (2002), we obtain a data-dependent bound with a logarithmic dependency
which significantly improves the previous square-root dependency. Experimental
results are reported to verify the effectiveness of our theoretical findings.
Hybrid Recommender System based on Autoencoders
Authors: Florian Strub, Romaric Gaudel, Jérémie Mary
Date: 2016-06-24 00:00:00
Tasks: Collaborative Filtering, Matrix Completion, Missing Values, Recommendation Systems

A standard model for Recommender Systems is the Matrix Completion setting:
given partially known matrix of ratings given by users (rows) to items
(columns), infer the unknown ratings. In the last decades, few attempts where
done to handle that objective with Neural Networks, but recently an
architecture based on Autoencoders proved to be a promising approach. In
current paper, we enhanced that architecture (i) by using a loss function
adapted to input data with missing values, and (ii) by incorporating side
information. The experiments demonstrate that while side information only
slightly improve the test error averaged on all users/items, it has more impact
on cold users/items.
Data-Driven Stochastic Robust Optimization: A General Computational Framework and Algorithm for Optimization under Uncertainty in the Big Data Era
Authors: Chao Ning, Fengqi You
Date: 2017-07-28 00:00:00

A novel data-driven stochastic robust optimization (DDSRO) framework is
proposed for optimization under uncertainty leveraging labeled multi-class
uncertainty data. Uncertainty data in large datasets are often collected from
various conditions, which are encoded by class labels. Machine learning methods
including Dirichlet process mixture model and maximum likelihood estimation are
employed for uncertainty modeling. A DDSRO framework is further proposed based
on the data-driven uncertainty model through a bi-level optimization structure.
The outer optimization problem follows a two-stage stochastic programming
approach to optimize the expected objective across different data classes;
adaptive robust optimization is nested as the inner problem to ensure the
robustness of the solution while maintaining computational tractability. A
decomposition-based algorithm is further developed to solve the resulting
multi-level optimization problem efficiently. Case studies on process network
design and planning are presented to demonstrate the applicability of the
proposed framework and algorithm.
Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network
Authors: Christine A. Liang, Lei Chen, Amer Wahed, Andy N. D. Nguyen
Date: 2017-12-29 00:00:00
Tasks: Deep Learning, Specificity

Deep Learning can significantly benefit cancer proteomics and genomics. In
this study, we attempt to determine a set of critical proteins that are
associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia
patients. A Deep Learning network consisting of autoencoders forming a
hierarchical model from which high-level features are extracted without labeled
training data. Dimensional reduction reduced the number of critical proteins
from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD
mutation with the levels of these 20 critical proteins (accuracy 97%,
sensitivity 90%, specificity 100%). Our Deep Learning network could hone in on
20 proteins with the strongest association with FLT3-ITD. The results of this
study allow a novel approach to determine critical protein pathways in the
FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to
model big data in cancer proteomics and genomics.
Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval
Authors: Daiguo Deng, Ruomei Wang, Hefeng Wu, Huayong He, Qi Li, Xiaonan Luo
Date: 2017-12-29 00:00:00
Tasks: Image Retrieval, Representation Learning, Retrieval

Fabric image retrieval is beneficial to many applications including clothing
searching, online shopping and cloth modeling. Learning pairwise image
similarity is of great importance to an image retrieval task. With the
resurgence of Convolutional Neural Networks (CNNs), recent works have achieved
significant progresses via deep representation learning with metric embedding,
which drives similar examples close to each other in a feature space, and
dissimilar ones apart from each other. In this paper, we propose a novel
embedding method termed focus ranking that can be easily unified into a CNN for
jointly learning image representations and metrics in the context of
fine-grained fabric image retrieval. Focus ranking aims to rank similar
examples higher than all dissimilar ones by penalizing ranking disorders via
the minimization of the overall cost attributed to similar samples being ranked
below dissimilar ones. At the training stage, training samples are organized
into focus ranking units for efficient optimization. We build a large-scale
fabric image retrieval dataset (FIRD) with about 25,000 images of 4,300
fabrics, and test the proposed model on the FIRD dataset. Experimental results
show the superiority of the proposed model over existing metric embedding
models.
Annotating and Modeling Empathy in Spoken Conversations
Authors: Firoj Alam, Morena Danieli, Giuseppe Riccardi
Date: 2017-05-13 00:00:00
Tasks: General Classification

Empathy, as defined in behavioral sciences, expresses the ability of human
beings to recognize, understand and react to emotions, attitudes and beliefs of
others. The lack of an operational definition of empathy makes it difficult to
measure it. In this paper, we address two related problems in automatic
affective behavior analysis: the design of the annotation protocol and the
automatic recognition of empathy from spoken conversations. We propose and
evaluate an annotation scheme for empathy inspired by the modal model of
emotions. The annotation scheme was evaluated on a corpus of real-life, dyadic
spoken conversations. In the context of behavioral analysis, we designed an
automatic segmentation and classification system for empathy. Given the
different speech and language levels of representation where empathy may be
communicated, we investigated features derived from the lexical and acoustic
spaces. The feature development process was designed to support both the fusion
and automatic selection of relevant features from high dimensional space. The
automatic classification system was evaluated on call center conversations
where it showed significantly better performance than the baseline.
A Resilient Image Matching Method with an Affine Invariant Feature Detector and Descriptor
Authors: Biao Zhao, Shigang Yue
Date: 2017-12-29 00:00:00
Tasks: 3D Object Reconstruction, Object Recognition, Object Reconstruction

Image feature matching is to seek, localize and identify the similarities
across the images. The matched local features between different images can
indicate the similarities of their content. Resilience of image feature
matching to large view point changes is challenging for a lot of applications
such as 3D object reconstruction, object recognition and navigation, etc, which
need accurate and robust feature matching from quite different view points. In
this paper we propose a novel image feature matching algorithm, integrating our
previous proposed Affine Invariant Feature Detector (AIFD) and new proposed
Affine Invariant Feature Descriptor (AIFDd). Both stages of this new proposed
algorithm can provide sufficient resilience to view point changes. With
systematic experiments, we can prove that the proposed method of feature
detector and descriptor outperforms other state-of-the-art feature matching
algorithms especially on view points robustness. It also performs well under
other conditions such as the change of illumination, rotation and compression,
etc.
RedDwarfData: a simplified dataset of StarCraft matches
Authors: Juan J. Merelo-Guervós, Antonio Fernández-Ares, Antonio Álvarez Caballero, Pablo García-Sánchez, Victor Rivas
Date: 2017-12-29 00:00:00
Tasks: Starcraft

The game Starcraft is one of the most interesting arenas to test new machine
learning and computational intelligence techniques; however, StarCraft matches
take a long time and creating a good dataset for training can be hard. Besides,
analyzing match logs to extract the main characteristics can also be done in
many different ways to the point that extracting and processing data itself can
take an inordinate amount of time and of course, depending on what you choose,
can bias learning algorithms. In this paper we present a simplified dataset
extracted from the set of matches published by Robinson and Watson, which we
have called RedDwarfData, containing several thousand matches processed to
frames, so that temporal studies can also be undertaken. This dataset is
available from GitHub under a free license. An initial analysis and appraisal
of these matches is also made.
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching
Authors: Zhang Chen, Xinqing Guo, Siyuan Li, Xuan Cao, Jingyi Yu
Date: 2017-08-02 00:00:00
Tasks: 3D Reconstruction, Stereo Matching, Stereo Matching Hand

Depth from defocus (DfD) and stereo matching are two most studied passive
depth sensing schemes. The techniques are essentially complementary: DfD can
robustly handle repetitive textures that are problematic for stereo matching
whereas stereo matching is insensitive to defocus blurs and can handle large
depth range. In this paper, we present a unified learning-based technique to
conduct hybrid DfD and stereo matching. Our input is image triplets: a stereo
pair and a defocused image of one of the stereo views. We first apply
depth-guided light field rendering to construct a comprehensive training
dataset for such hybrid sensing setups. Next, we adopt the hourglass network
architecture to separately conduct depth inference from DfD and stereo.
Finally, we exploit different connection methods between the two separate
networks for integrating them into a unified solution to produce high fidelity
3D disparity maps. Comprehensive experiments on real and synthetic data show
that our new learning-based hybrid 3D sensing technique can significantly
improve accuracy and robustness in 3D reconstruction.
Polyp detection inside the capsule endoscopy: an approach for power consumption reduction
Authors: Mohammad Amin Khorsandi, Nader Karimi, Shadrokh Samavi
Date: 2017-12-29 00:00:00

Capsule endoscopy is a novel and non-invasive method for diagnosis, which
assists gastroenterologists to monitor the digestive track. Although this new
technology has many advantages over the conventional endoscopy, there are
weaknesses that limits the usage of this technology. Some weaknesses are due to
using small-size batteries. Radio transmitter consumes the largest portion of
energy; consequently, a simple way to reduce the power consumption is to reduce
the data to be transmitted. Many works are proposed to reduce the amount of
data to be transmitted consist of specific compression methods and reduction in
video resolution and frame rate. We proposed a system inside the capsule for
detecting informative frames and sending these frames instead of several
non-informative frames. In this work, we specifically focused on hardware
friendly algorithm (with capability of parallelism and pipeline) for
implementation of polyp detection. Two features of positive contrast and
customized edges of polyps are exploited to define whether the frame consists
of polyp or not. The proposed method is devoid of complex and iterative
structure to save power and reduce the response time. Experimental results
indicate acceptable rate of detection of our work.
Non-linear motor control by local learning in spiking neural networks
Authors: Aditya Gilra, Wulfram Gerstner
Date: 2017-12-29 00:00:00
Proceeding: ICML 2018 7

Learning weights in a spiking neural network with hidden neurons, using
local, stable and online rules, to control non-linear body dynamics is an open
problem. Here, we employ a supervised scheme, Feedback-based Online Local
Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking
neurons with hidden layers, to control a two-link arm so as to reproduce a
desired state trajectory. The network first learns an inverse model of the
non-linear dynamics, i.e. from state trajectory as input to the network, it
learns to infer the continuous-time command that produced the trajectory.
Connection weights are adjusted via a local plasticity rule that involves
pre-synaptic firing and post-synaptic feedback of the error in the inferred
command. We choose a network architecture, termed differential feedforward,
that gives the lowest test error from different feedforward and recurrent
architectures. The learned inverse model is then used to generate a
continuous-time motor command to control the arm, given a desired trajectory.
Exploring the significance of using perceptually relevant image decolorization method for scene classification
Authors: V. Sowmya, D. Govind, K. P. Soman
Date: 2017-12-29 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Quality Assessment, Scene Classification, SSIM

A color image contains luminance and chrominance components representing the
intensity and color information respectively. The objective of the work
presented in this paper is to show the significance of incorporating the
chrominance information for the task of scene classification. An improved
color-to-grayscale image conversion algorithm by effectively incorporating the
chrominance information is proposed using color-to-gay structure similarity
index (C2G-SSIM) and singular value decomposition (SVD) to improve the
perceptual quality of the converted grayscale images. The experimental result
analysis based on the image quality assessment for image decolorization called
C2G-SSIM and success rate (Cadik and COLOR250 datasets) shows that the proposed
image decolorization technique performs better than 8 existing benchmark
algorithms for image decolorization. In the second part of the paper, the
effectiveness of incorporating the chrominance component in scene
classification task is demonstrated using the deep belief network (DBN) based
image classification system developed using dense scale invariant feature
transform (SIFT) as features. The levels of chrominance information
incorporated by the proposed image decolorization technique is confirmed by the
improvement in the overall scene classification accuracy . Also, the overall
scene classification performance is improved by the combination of models
obtained using the proposed and the conventional decolorization methods.