pwc-archive/papers-with-abstracts huggingface dataset:
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Authors: Sergey Ioffe
Date: 2017-02-10 00:00:00
Proceeding: NeurIPS 2017 12

Batch Normalization is quite effective at accelerating and improving the
training of deep models. However, its effectiveness diminishes when the
training minibatches are small, or do not consist of independent samples. We
hypothesize that this is due to the dependence of model layer inputs on all the
examples in the minibatch, and different activations being produced between
training and inference. We propose Batch Renormalization, a simple and
effective extension to ensure that the training and inference models generate
the same outputs that depend on individual examples rather than the entire
minibatch. Models trained with Batch Renormalization perform substantially
better than batchnorm when training with small or non-i.i.d. minibatches. At
the same time, Batch Renormalization retains the benefits of batchnorm such as
insensitivity to initialization and training efficiency.
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
Authors: Joseph Gomes, Bharath Ramsundar, Evan N. Feinberg, Vijay S. Pande
Date: 2017-03-30 00:00:00
Tasks: Drug Discovery, Molecular Docking

Empirical scoring functions based on either molecular force fields or
cheminformatics descriptors are widely used, in conjunction with molecular
docking, during the early stages of drug discovery to predict potency and
binding affinity of a drug-like molecule to a given target. These models
require expert-level knowledge of physical chemistry and biology to be encoded
as hand-tuned parameters or features rather than allowing the underlying model
to select features in a data-driven procedure. Here, we develop a general
3-dimensional spatial convolution operation for learning atomic-level chemical
interactions directly from atomic coordinates and demonstrate its application
to structure-based bioactivity prediction. The atomic convolutional neural
network is trained to predict the experimentally determined binding affinity of
a protein-ligand complex by direct calculation of the energy associated with
the complex, protein, and ligand given the crystal structure of the binding
pose. Non-covalent interactions present in the complex that are absent in the
protein-ligand sub-structures are identified and the model learns the
interaction strength associated with these features. We test our model by
predicting the binding free energy of a subset of protein-ligand complexes
found in the PDBBind dataset and compare with state-of-the-art cheminformatics
and machine learning-based approaches. We find that all methods achieve
experimental accuracy and that atomic convolutional networks either outperform
or perform competitively with the cheminformatics based methods. Unlike all
previous protein-ligand prediction systems, atomic convolutional networks are
end-to-end and fully-differentiable. They represent a new data-driven,
physics-based deep learning model paradigm that offers a strong foundation for
future improvements in structure-based bioactivity prediction.
Geometric Affordances from a Single Example via the Interaction Tensor
Authors: Eduardo Ruiz, Walterio Mayol-Cuevas
Date: 2017-03-30 00:00:00

This paper develops and evaluates a new tensor field representation to
express the geometric affordance of one object over another. We expand the well
known bisector surface representation to one that is weight-driven and that
retains the provenance of surface points with directional vectors. We also
incorporate the notion of affordance keypoints which allow for faster decisions
at a point of query and with a compact and straightforward descriptor. Using a
single interaction example, we are able to generalize to previously-unseen
scenarios; both synthetic and also real scenes captured with RGBD sensors. We
show how our interaction tensor allows for significantly better performance
over alternative formulations. Evaluations also include crowdsourcing
comparisons that confirm the validity of our affordance proposals, which agree
on average 84% of the time with human judgments, and which is 20-40% better
than the baseline methods.
Evaluating Complex Task through Crowdsourcing: Multiple Views Approach
Authors: Lingyu Lyu, Mehmed Kantardzic
Date: 2017-03-30 00:00:00

With the popularity of massive open online courses, grading through
crowdsourcing has become a prevalent approach towards large scale classes.
However, for getting grades for complex tasks, which require specific skills
and efforts for grading, crowdsourcing encounters a restriction of insufficient
knowledge of the workers from the crowd. Due to knowledge limitation of the
crowd graders, grading based on partial perspectives becomes a big challenge
for evaluating complex tasks through crowdsourcing. Especially for those tasks
which not only need specific knowledge for grading, but also should be graded
as a whole instead of being decomposed into smaller and simpler subtasks. We
propose a framework for grading complex tasks via multiple views, which are
different grading perspectives defined by experts for the task, to provide
uniformity. Aggregation algorithm based on graders variances are used to
combine the grades for each view. We also detect bias patterns of the graders,
and debias them regarding each view of the task. Bias pattern determines how
the behavior is biased among graders, which is detected by a statistical
technique. The proposed approach is analyzed on a synthetic data set. We show
that our model gives more accurate results compared to the grading approaches
without different views and debiasing algorithm.
Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis
Authors: Aram Ter-Sarkisov, Robert Ross, John Kelleher
Date: 2017-03-30 00:00:00
Tasks: Instance Segmentation, Object, Object Localization, Object Tracking, Semantic Segmentation

This paper introduces a new approach to the long-term tracking of an object
in a challenging environment. The object is a cow and the environment is an
enclosure in a cowshed. Some of the key challenges in this domain are a
cluttered background, low contrast and high similarity between moving objects
which greatly reduces the efficiency of most existing approaches, including
those based on background subtraction. Our approach is split into object
localization, instance segmentation, learning and tracking stages. Our solution
is compared to a range of semi-supervised object tracking algorithms and we
show that the performance is strong and well suited to subsequent analysis. We
present our solution as a first step towards broader tracking and behavior
monitoring for cows in precision agriculture with the ultimate objective of
early detection of lameness.
FairJudge: Trustworthy User Prediction in Rating Platforms
Authors: Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, V. S. Subrahamanian
Date: 2017-03-30 00:00:00
Tasks: Fairness, Prediction

Rating platforms enable large-scale collection of user opinion about items
(products, other users, etc.). However, many untrustworthy users give
fraudulent ratings for excessive monetary gains. In the paper, we present
FairJudge, a system to identify such fraudulent users. We propose three
metrics: (i) the fairness of a user that quantifies how trustworthy the user is
in rating the products, (ii) the reliability of a rating that measures how
reliable the rating is, and (iii) the goodness of a product that measures the
quality of the product. Intuitively, a user is fair if it provides reliable
ratings that are close to the goodness of the product. We formulate a mutually
recursive definition of these metrics, and further address cold start problems
and incorporate behavioral properties of users and products in the formulation.
We propose an iterative algorithm, FairJudge, to predict the values of the
three metrics. We prove that FairJudge is guaranteed to converge in a bounded
number of iterations, with linear time complexity. By conducting five different
experiments on five rating platforms, we show that FairJudge significantly
outperforms nine existing algorithms in predicting fair and unfair users. We
reported the 100 most unfair users in the Flipkart network to their review
fraud investigators, and 80 users were correctly identified (80% accuracy). The
FairJudge algorithm is already being deployed at Flipkart.
Domain Adaptation for Visual Applications: A Comprehensive Survey
Authors: Gabriela Csurka
Date: 2017-02-17 00:00:00
Tasks: Domain Adaptation, Image Categorization, Image Segmentation, object-detection, Object Detection, Position, Semantic Segmentation, Survey, Transfer Learning

The aim of this paper is to give an overview of domain adaptation and
transfer learning with a specific view on visual applications. After a general
motivation, we first position domain adaptation in the larger transfer learning
problem. Second, we try to address and analyze briefly the state-of-the-art
methods for different types of scenarios, first describing the historical
shallow methods, addressing both the homogeneous and the heterogeneous domain
adaptation methods. Third, we discuss the effect of the success of deep
convolutional architectures which led to new type of domain adaptation methods
that integrate the adaptation within the deep architecture. Fourth, we overview
the methods that go beyond image categorization, such as object detection or
image segmentation, video analyses or learning visual attributes. Finally, we
conclude the paper with a section where we relate domain adaptation to other
machine learning solutions.
Efficient optimization for Hierarchically-structured Interacting Segments (HINTS)
Authors: Hossam Isack, Olga Veksler, Ipek Oguz, Milan Sonka, Yuri Boykov
Date: 2017-03-30 00:00:00
Proceeding: CVPR 2017 7
Tasks: Segmentation

We propose an effective optimization algorithm for a general hierarchical
segmentation model with geometric interactions between segments. Any given tree
can specify a partial order over object labels defining a hierarchy. It is
well-established that segment interactions, such as inclusion/exclusion and
margin constraints, make the model significantly more discriminant. However,
existing optimization methods do not allow full use of such models. Generic
-expansion results in weak local minima, while common binary multi-layered
formulations lead to non-submodularity, complex high-order potentials, or polar
domain unwrapping and shape biases. In practice, applying these methods to
arbitrary trees does not work except for simple cases. Our main contribution is
an optimization method for the Hierarchically-structured Interacting Segments
(HINTS) model with arbitrary trees. Our Path-Moves algorithm is based on
multi-label MRF formulation and can be seen as a combination of well-known
a-expansion and Ishikawa techniques. We show state-of-the-art biomedical
segmentation for many diverse examples of complex trees.
On Bayesian Exponentially Embedded Family for Model Order Selection
Authors: Zhenghan Zhu, Steven Kay
Date: 2017-03-30 00:00:00
Tasks: Model Selection

In this paper, we derive a Bayesian model order selection rule by using the
exponentially embedded family method, termed Bayesian EEF. Unlike many other
Bayesian model selection methods, the Bayesian EEF can use vague proper priors
and improper noninformative priors to be objective in the elicitation of
parameter priors. Moreover, the penalty term of the rule is shown to be the sum
of half of the parameter dimension and the estimated mutual information between
parameter and observed data. This helps to reveal the EEF mechanism in
selecting model orders and may provide new insights into the open problems of
choosing an optimal penalty term for model order selection and choosing a good
prior from information theoretic viewpoints. The important example of linear
model order selection is given to illustrate the algorithms and arguments.
Lastly, the Bayesian EEF that uses Jeffreys prior coincides with the EEF rule
derived by frequentist strategies. This shows another interesting relationship
between the frequentist and Bayesian philosophies for model selection.
A Paradigm Shift: Detecting Human Rights Violations Through Web Images
Authors: Grigorios Kalliatakis, Shoaib Ehsan, Klaus D. McDonald-Maier
Date: 2017-03-30 00:00:00

The growing presence of devices carrying digital cameras, such as mobile
phones and tablets, combined with ever improving internet networks have enabled
ordinary citizens, victims of human rights abuse, and participants in armed
conflicts, protests, and disaster situations to capture and share via social
media networks images and videos of specific events. This paper discusses the
potential of images in human rights context including the opportunities and
challenges they present. This study demonstrates that real-world images have
the capacity to contribute complementary data to operational human rights
monitoring efforts when combined with novel computer vision approaches. The
analysis is concluded by arguing that if images are to be used effectively to
detect and identify human rights violations by rights advocates, greater
attention to gathering task-specific visual concepts from large-scale web
images is required.
Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation
Authors: Xin Jin, Peng Yuan, Xiao-Dong Li, Chenggen Song, Shiming Ge, Geng Zhao, Yingya Chen
Date: 2017-02-27 00:00:00
Tasks: Object, object-detection, Object Detection, Privacy Preserving

A cloud server spent a lot of time, energy and money to train a Viola-Jones
type object detector with high accuracy. Clients can upload their photos to the
cloud server to find objects. However, the client does not want the leakage of
the content of his/her photos. In the meanwhile, the cloud server is also
reluctant to leak any parameters of the trained object detectors. 10 years ago,
Avidan & Butman introduced Blind Vision, which is a method for securely
evaluating a Viola-Jones type object detector. Blind Vision uses standard
cryptographic tools and is painfully slow to compute, taking a couple of hours
to scan a single image. The purpose of this work is to explore an efficient
method that can speed up the process. We propose the Random Base Image (RBI)
Representation. The original image is divided into random base images. Only the
base images are submitted randomly to the cloud server. Thus, the content of
the image can not be leaked. In the meanwhile, a random vector and the secure
Millionaire protocol are leveraged to protect the parameters of the trained
object detector. The RBI makes the integral-image enable again for the great
acceleration. The experimental results reveal that our method can retain the
detection accuracy of that of the plain vision algorithm and is significantly
faster than the traditional blind vision, with only a very low probability of
the information leakage theoretically.
Application of a Shallow Neural Network to Short-Term Stock Trading
Authors: Abhinav Madahar, Yuze Ma, Kunal Patel
Date: 2017-03-30 00:00:00

Machine learning is increasingly prevalent in stock market trading. Though
neural networks have seen success in computer vision and natural language
processing, they have not been as useful in stock market trading. To
demonstrate the applicability of a neural network in stock trading, we made a
single-layer neural network that recommends buying or selling shares of a stock
by comparing the highest high of 10 consecutive days with that of the next 10
days, a process repeated for the stock's year-long historical data. A
chi-squared analysis found that the neural network can accurately and
appropriately decide whether to buy or sell shares for a given stock, showing
that a neural network can make simple decisions about the stock market.
Whale swarm algorithm for function optimization
Authors: Bing Zeng, Liang Gao, Xinyu Li
Date: 2017-02-11 00:00:00

Increasing nature-inspired metaheuristic algorithms are applied to solving
the real-world optimization problems, as they have some advantages over the
classical methods of numerical optimization. This paper has proposed a new
nature-inspired metaheuristic called Whale Swarm Algorithm for function
optimization, which is inspired by the whales behavior of communicating with
each other via ultrasound for hunting. The proposed Whale Swarm Algorithm has
been compared with several popular metaheuristic algorithms on comprehensive
performance metrics. According to the experimental results, Whale Swarm
Algorithm has a quite competitive performance when compared with other
algorithms.
On Fundamental Limits of Robust Learning
Authors: Jiashi Feng
Date: 2017-03-30 00:00:00
Tasks: PAC learning

We consider the problems of robust PAC learning from distributed and
streaming data, which may contain malicious errors and outliers, and analyze
their fundamental complexity questions. In particular, we establish lower
bounds on the communication complexity for distributed robust learning
performed on multiple machines, and on the space complexity for robust learning
from streaming data on a single machine. These results demonstrate that gaining
robustness of learning algorithms is usually at the expense of increased
complexities. As far as we know, this work gives the first complexity results
for distributed and online robust PAC learning.
Analysing Congestion Problems in Multi-agent Reinforcement Learning
Authors: Roxana Rădulescu, Peter Vrancx, Ann Nowé
Date: 2017-02-28 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Congestion problems are omnipresent in today's complex networks and represent
a challenge in many research domains. In the context of Multi-agent
Reinforcement Learning (MARL), approaches like difference rewards and resource
abstraction have shown promising results in tackling such problems. Resource
abstraction was shown to be an ideal candidate for solving large-scale resource
allocation problems in a fully decentralized manner. However, its performance
and applicability strongly depends on some, until now, undocumented
assumptions. Two of the main congestion benchmark problems considered in the
literature are: the Beach Problem Domain and the Traffic Lane Domain. In both
settings the highest system utility is achieved when overcrowding one resource
and keeping the rest at optimum capacity. We analyse how abstract grouping can
promote this behaviour and how feasible it is to apply this approach in a
real-world domain (i.e., what assumptions need to be satisfied and what
knowledge is necessary). We introduce a new test problem, the Road Network
Domain (RND), where the resources are no longer independent, but rather part of
a network (e.g., road network), thus choosing one path will also impact the
load on other paths having common road segments. We demonstrate the application
of state-of-the-art MARL methods for this new congestion model and analyse
their performance. RND allows us to highlight an important limitation of
resource abstraction and show that the difference rewards approach manages to
better capture and inform the agents about the dynamics of the environment.
An Empirical Approach for Modeling Fuzzy Geographical Descriptors
Authors: Alejandro Ramos-Soto, Jose M. Alonso, Ehud Reiter, Kees Van Deemter, Albert Gatt
Date: 2017-03-30 00:00:00
Tasks: Referring Expression, Referring expression generation, Text Generation

We present a novel heuristic approach that defines fuzzy geographical
descriptors using data gathered from a survey with human subjects. The
participants were asked to provide graphical interpretations of the descriptors
`north' and `south' for the Galician region (Spain). Based on these
interpretations, our approach builds fuzzy descriptors that are able to compute
membership degrees for geographical locations. We evaluated our approach in
terms of efficiency and precision. The fuzzy descriptors are meant to be used
as the cornerstones of a geographical referring expression generation algorithm
that is able to linguistically characterize geographical locations and regions.
This work is also part of a general research effort that intends to establish a
methodology which reunites the empirical studies traditionally practiced in
data-to-text and the use of fuzzy sets to model imprecision and vagueness in
words and expressions for text generation purposes.
Minimum energy path calculations with Gaussian process regression
Authors: Olli-Pekka Koistinen, Emile Maras, Aki Vehtari, Hannes Jónsson
Date: 2017-03-30 00:00:00
Tasks: regression

The calculation of minimum energy paths for transitions such as atomic and/or
spin re-arrangements is an important task in many contexts and can often be
used to determine the mechanism and rate of transitions. An important challenge
is to reduce the computational effort in such calculations, especially when ab
initio or electron density functional calculations are used to evaluate the
energy since they can require large computational effort. Gaussian process
regression is used here to reduce significantly the number of energy
evaluations needed to find minimum energy paths of atomic rearrangements. By
using results of previous calculations to construct an approximate energy
surface and then converge to the minimum energy path on that surface in each
Gaussian process iteration, the number of energy evaluations is reduced
significantly as compared with regular nudged elastic band calculations. For a
test problem involving rearrangements of a heptamer island on a crystal
surface, the number of energy evaluations is reduced to less than a fifth. The
scaling of the computational effort with the number of degrees of freedom as
well as various possible further improvements to this approach are discussed.
Improving Interpretability of Deep Neural Networks with Semantic Information
Authors: Yinpeng Dong, Hang Su, Jun Zhu, Bo Zhang
Date: 2017-03-12 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, Temporal Action Localization, Video Captioning

Interpretability of deep neural networks (DNNs) is essential since it enables
users to understand the overall strengths and weaknesses of the models, conveys
an understanding of how the models will behave in the future, and how to
diagnose and correct potential problems. However, it is challenging to reason
about what a DNN actually does due to its opaque or black-box nature. To
address this issue, we propose a novel technique to improve the
interpretability of DNNs by leveraging the rich semantic information embedded
in human descriptions. By concentrating on the video captioning task, we first
extract a set of semantically meaningful topics from the human descriptions
that cover a wide range of visual concepts, and integrate them into the model
with an interpretive loss. We then propose a prediction difference maximization
algorithm to interpret the learned features of each neuron. Experimental
results demonstrate its effectiveness in video captioning using the
interpretable features, which can also be transferred to video action
recognition. By clearly understanding the learned features, users can easily
revise false predictions via a human-in-the-loop procedure.