pwc-archive/papers-with-abstracts huggingface dataset:
Worst-case Optimal Submodular Extensions for Marginal Estimation
Authors: Pankaj Pansari, Chris Russell, M. Pawan Kumar
Date: 2018-01-10 00:00:00
Tasks: Variational Inference

Submodular extensions of an energy function can be used to efficiently
compute approximate marginals via variational inference. The accuracy of the
marginals depends crucially on the quality of the submodular extension. To
identify the best possible extension, we show an equivalence between the
submodular extensions of the energy and the objective functions of linear
programming (LP) relaxations for the corresponding MAP estimation problem. This
allows us to (i) establish the worst-case optimality of the submodular
extension for Potts model used in the literature; (ii) identify the worst-case
optimal submodular extension for the more general class of metric labeling; and
(iii) efficiently compute the marginals for the widely used dense CRF model
with the help of a recently proposed Gaussian filtering method. Using synthetic
and real data, we show that our approach provides comparable upper bounds on
the log-partition function to those obtained using tree-reweighted message
passing (TRW) in cases where the latter is computationally feasible.
Importantly, unlike TRW, our approach provides the first practical algorithm to
compute an upper bound on the dense CRF model.
Data-driven forecasting of solar irradiance
Authors: Pierrick Bruneau, Philippe Pinheiro, Yoann Didry
Date: 2018-01-10 00:00:00
Tasks: Prediction, regression, Time Series, Time Series Analysis, Variable Selection

This paper describes a flexible approach to short term prediction of meteorological variables. In particular, we focus on the prediction of the solar irradiance one hour ahead, a task that has high practical value when optimizing solar energy resources. As D\'efi EGC 2018 provides us with time series data for multiple sensors (e.g. solar irradiance, temperature, hygrometry), recorded every minute for two years and 5 geographical sites from La R\'eunion island, we test the value of using recently observed data as input for prediction models, as well as the performance of models across sites. After describing our data cleaning and normalization process, we combine a variable selection step based on AutoRegressive Integrated Moving Average (ARIMA) models, to using general purpose regression techniques such as neural networks and regression trees.
Lexical representation explains cortical entrainment during speech comprehension
Authors: Stefan Frank, Jinbiao Yang
Date: 2017-06-18 00:00:00
Tasks: Sentence

Results from a recent neuroimaging study on spoken sentence comprehension
have been interpreted as evidence for cortical entrainment to hierarchical
syntactic structure. We present a simple computational model that predicts the
power spectra from this study, even though the model's linguistic knowledge is
restricted to the lexical level, and word-level representations are not
combined into higher-level units (phrases or sentences). Hence, the cortical
entrainment results can also be explained from the lexical properties of the
stimuli, without recourse to hierarchical syntax.
Axiomatizations of inconsistency indices for triads
Authors: László Csató
Date: 2018-01-10 00:00:00

Pairwise comparison matrices often exhibit inconsistency, therefore many
indices have been suggested to measure their deviation from a consistent
matrix. A set of axioms has been proposed recently that is required to be
satisfied by any reasonable inconsistency index. This set seems to be not
exhaustive as illustrated by an example, hence it is expanded by adding two new
properties. All axioms are considered on the set of triads, pairwise comparison
matrices with three alternatives, which is the simplest case of inconsistency.
We choose the logically independent properties and prove that they
characterize, that is, uniquely determine the inconsistency ranking induced by
most inconsistency indices that coincide on this restricted domain. Since
triads play a prominent role in a number of inconsistency indices, our results
can also contribute to the measurement of inconsistency for pairwise comparison
matrices with more than three alternatives.
Planning with Pixels in (Almost) Real Time
Authors: Wilmer Bandres, Blai Bonet, Hector Geffner
Date: 2018-01-10 00:00:00

Recently, width-based planning methods have been shown to yield
state-of-the-art results in the Atari 2600 video games. For this, the states
were associated with the (RAM) memory states of the simulator. In this work, we
consider the same planning problem but using the screen instead. By using the
same visual inputs, the planning results can be compared with those of humans
and learning methods. We show that the planning approach, out of the box and
without training, results in scores that compare well with those obtained by
humans and learning methods, and moreover, by developing an episodic, rollout
version of the IW(k) algorithm, we show that such scores can be obtained in
almost real time.
Reasoning about Unforeseen Possibilities During Policy Learning
Authors: Craig Innes, Alex Lascarides, Stefano V. Albrecht, Subramanian Ramamoorthy, Benjamin Rosman
Date: 2018-01-10 00:00:00

Methods for learning optimal policies in autonomous agents often assume that
the way the domain is conceptualised---its possible states and actions and
their causal structure---is known in advance and does not change during
learning. This is an unrealistic assumption in many scenarios, because new
evidence can reveal important information about what is possible, possibilities
that the agent was not aware existed prior to learning. We present a model of
an agent which both discovers and learns to exploit unforeseen possibilities
using two sources of evidence: direct interaction with the world and
communication with a domain expert. We use a combination of probabilistic and
symbolic reasoning to estimate all components of the decision problem,
including its set of random variables and their causal dependencies. Agent
simulations show that the agent converges on optimal polices even when it
starts out unaware of factors that are critical to behaving optimally.
Expected Policy Gradients for Reinforcement Learning
Authors: Kamil Ciosek, Shimon Whiteson
Date: 2018-01-10 00:00:00
Tasks: Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadratic critics and then extend it to a universal analytical method, covering a broad class of actors and critics, including Gaussian, exponential families, and policies with bounded support. For Gaussian policies, we introduce an exploration method that uses covariance proportional to the matrix exponential of the scaled Hessian of the critic with respect to the actions. For discrete action spaces, we derive a variant of EPG based on softmax policies. We also establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. Furthermore, we prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and with little computational overhead. Finally, we provide an extensive experimental evaluation of EPG and show that it outperforms existing approaches on multiple challenging control domains.
Visual art inspired by the collective feeding behavior of sand-bubbler crabs
Authors: Hendrik Richter
Date: 2017-09-01 00:00:00
Tasks: Sand

Sand--bubblers are crabs of the genera Dotilla and Scopimera which are known
to produce remarkable patterns and structures at tropical beaches. From these
pattern-making abilities, we may draw inspiration for digital visual art. A
simple mathematical model is proposed and an algorithm is designed that may
create such sand-bubbler patterns artificially. In addition, design parameters
to modify the patterns are identified and analyzed by computational aesthetic
measures. Finally, an extension of the algorithm is discussed that may enable
controlling and guiding generative evolution of the art-making process.
Unsupervised Despeckling
Authors: Deepak Mishra, Santanu Chaudhury, Mukul Sarkar, Arvinder Singh Soin
Date: 2018-01-10 00:00:00

Contrast and quality of ultrasound images are adversely affected by the
excessive presence of speckle. However, being an inherent imaging property,
speckle helps in tissue characterization and tracking. Thus, despeckling of the
ultrasound images requires the reduction of speckle extent without any
oversmoothing. In this letter, we aim to address the despeckling problem using
an unsupervised deep adversarial approach. A despeckling residual neural
network (DRNN) is trained with an adversarial loss imposed by a discriminator.
The discriminator tries to differentiate between the despeckled images
generated by the DRNN and the set of high-quality images. Further to prevent
the developed DRNN from oversmoothing, a structural loss term is used along
with the adversarial loss. Experimental evaluations show that the proposed DRNN
is able to outperform the state-of-the-art despeckling approaches.
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization
Authors: Tatsuya Yokota, Hidekata Hontani
Date: 2018-01-10 00:00:00
Tasks: Denoising, Missing Elements, Retrieval

Tensor completion is a technique of filling missing elements of the
incomplete data tensors. It being actively studied based on the convex
optimization scheme such as nuclear-norm minimization. When given data tensors
include some noises, the nuclear-norm minimization problem is usually converted
to the nuclear-norm `regularization' problem which simultaneously minimize
penalty and error terms with some trade-off parameter. However, the good value
of trade-off is not easily determined because of the difference of two units
and the data dependence. In the sense of trade-off tuning, the noisy tensor
completion problem with the `noise inequality constraint' is better choice than
the `regularization' because the good noise threshold can be easily bounded
with noise standard deviation. In this study, we tackle to solve the convex
tensor completion problems with two types of noise inequality constraints:
Gaussian and Laplace distributions. The contributions of this study are
follows: (1) New tensor completion and denoising models using tensor total
variation and nuclear-norm are proposed which can be characterized as a
generalization/extension of many past matrix and tensor completion models, (2)
proximal mappings for noise inequalities are derived which are analytically
computable with low computational complexity, (3) convex optimization algorithm
is proposed based on primal-dual splitting framework, (4) new step-size
adaptation method is proposed to accelerate the optimization, and (5) extensive
experiments demonstrated the advantages of the proposed method for visual data
retrieval such as for color images, movies, and 3D-volumetric data.
Detecting Approximate Reflection Symmetry in a Point Set using Optimization on Manifold
Authors: Rajendra Nagar, Shanmuganathan Raman
Date: 2017-06-27 00:00:00
Tasks: Symmetry Detection

We propose an algorithm to detect approximate reflection symmetry present in
a set of volumetrically distributed points belonging to $\mathbb{R}^d$
containing a distorted reflection symmetry pattern. We pose the problem of
detecting approximate reflection symmetry as the problem of establishing
correspondences between the points which are reflections of each other and we
determine the reflection symmetry transformation. We formulate an optimization
framework in which the problem of establishing the correspondences amounts to
solving a linear assignment problem and the problem of determining the
reflection symmetry transformation amounts to solving an optimization problem
on a smooth Riemannian product manifold. The proposed approach estimates the
symmetry from the geometry of the points and is descriptor independent. We
evaluate the performance of the proposed approach on the standard benchmark
dataset and achieve the state-of-the-art performance. We further show the
robustness of our approach by varying the amount of distortion in a perfect
reflection symmetry pattern where we perturb each point by a different amount
of perturbation. We demonstrate the effectiveness of the method by applying it
to the problem of 2-D and 3-D reflection symmetry detection along with
comparisons.
Deep Transfer Learning for Error Decoding from Non-Invasive EEG
Authors: Martin Völker, Robin T. Schirrmeister, Lukas D. J. Fiederer, Wolfram Burgard, Tonio Ball
Date: 2017-10-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Transfer Learning

We recorded high-density EEG in a flanker task experiment (31 subjects) and
an online BCI control paradigm (4 subjects). On these datasets, we evaluated
the use of transfer learning for error decoding with deep convolutional neural
networks (deep ConvNets). In comparison with a regularized linear discriminant
analysis (rLDA) classifier, ConvNets were significantly better in both intra-
and inter-subject decoding, achieving an average accuracy of 84.1 % within
subject and 81.7 % on unknown subjects (flanker task). Neither method was,
however, able to generalize reliably between paradigms. Visualization of
features the ConvNets learned from the data showed plausible patterns of brain
activity, revealing both similarities and differences between the different
kinds of errors. Our findings indicate that deep learning techniques are useful
to infer information about the correctness of action in BCI applications,
particularly for the transfer of pre-trained classifiers to new recording
sessions or subjects.
Multivariate Dependency Measure based on Copula and Gaussian Kernel
Authors: Angshuman Roy, Alok Goswami, C. A. Murthy
Date: 2017-08-24 00:00:00

We propose a new multivariate dependency measure. It is obtained by considering a Gaussian kernel based distance between the copula transform of the given d-dimensional distribution and the uniform copula and then appropriately normalizing it. The resulting measure is shown to satisfy a number of desirable properties. A nonparametric estimate is proposed for this dependency measure and its properties (finite sample as well as asymptotic) are derived. Some comparative studies of the proposed dependency measure estimate with some widely used dependency measure estimates on artificial datasets are included. A non-parametric test of independence between two or more random variables based on this measure is proposed. A comparison of the proposed test with some existing nonparametric multivariate test for independence is presented.
Translating Pro-Drop Languages with Reconstruction Models
Authors: Long-Yue Wang, Zhaopeng Tu, Shuming Shi, Tong Zhang, Yvette Graham, Qun Liu
Date: 2018-01-10 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Pronouns are frequently omitted in pro-drop languages, such as Chinese,
generally leading to significant challenges with respect to the production of
complete translations. To date, very little attention has been paid to the
dropped pronoun (DP) problem within neural machine translation (NMT). In this
work, we propose a novel reconstruction-based approach to alleviating DP
translation problems for NMT models. Firstly, DPs within all source sentences
are automatically annotated with parallel information extracted from the
bilingual training corpus. Next, the annotated source sentence is reconstructed
from hidden representations in the NMT model. With auxiliary training
objectives, in terms of reconstruction scores, the parameters associated with
the NMT model are guided to produce enhanced hidden representations that are
encouraged as much as possible to embed annotated DP information. Experimental
results on both Chinese-English and Japanese-English dialogue translation tasks
show that the proposed approach significantly and consistently improves
translation performance over a strong NMT baseline, which is directly built on
the training data annotated with DPs.
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network
Authors: Ziqiang Zheng, Chao Wang, Zhibin Yu, Haiyong Zheng, Bing Zheng
Date: 2018-01-10 00:00:00
Tasks: Denoising, Generative Adversarial Network, Image Generation

Semantic layouts based Image synthesizing, which has benefited from the
success of Generative Adversarial Network (GAN), has drawn much attention in
these days. How to enhance the synthesis image equality while keeping the
stochasticity of the GAN is still a challenge. We propose a novel denoising
framework to handle this problem. The overlapped objects generation is another
challenging task when synthesizing images from a semantic layout to a realistic
RGB photo. To overcome this deficiency, we include a one-hot semantic label map
to force the generator paying more attention on the overlapped objects
generation. Furthermore, we improve the loss function of the discriminator by
considering perturb loss and cascade layer loss to guide the generation
process. We applied our methods on the Cityscapes, Facades and NYU datasets and
demonstrate the image generation ability of our model.
Role of zero synapses in unsupervised feature learning
Authors: Haiping Huang
Date: 2017-03-23 00:00:00

Synapses in real neural circuits can take discrete values, including zero
(silent or potential) synapses. The computational role of zero synapses in
unsupervised feature learning of unlabeled noisy data is still unclear, thus it
is important to understand how the sparseness of synaptic activity is shaped
during learning and its relationship with receptive field formation. Here, we
formulate this kind of sparse feature learning by a statistical mechanics
approach. We find that learning decreases the fraction of zero synapses, and
when the fraction decreases rapidly around a critical data size, an
intrinsically structured receptive field starts to develop. Further increasing
the data size refines the receptive field, while a very small fraction of zero
synapses remain to act as contour detectors. This phenomenon is discovered not
only in learning a handwritten digits dataset, but also in learning retinal
neural activity measured in a natural-movie-stimuli experiment.
eCommerceGAN : A Generative Adversarial Network for E-commerce
Authors: Ashutosh Kumar, Arijit Biswas, Subhajit Sanyal
Date: 2018-01-10 00:00:00
Tasks: Generative Adversarial Network

E-commerce companies such as Amazon, Alibaba and Flipkart process billions of
orders every year. However, these orders represent only a small fraction of all
plausible orders. Exploring the space of all plausible orders could help us
better understand the relationships between the various entities in an
e-commerce ecosystem, namely the customers and the products they purchase. In
this paper, we propose a Generative Adversarial Network (GAN) for orders made
in e-commerce websites. Once trained, the generator in the GAN could generate
any number of plausible orders. Our contributions include: (a) creating a dense
and low-dimensional representation of e-commerce orders, (b) train an
ecommerceGAN (ecGAN) with real orders to show the feasibility of the proposed
paradigm, and (c) train an ecommerce-conditional-GAN (ec^2GAN) to generate the
plausible orders involving a particular product. We propose several qualitative
methods to evaluate ecGAN and demonstrate its effectiveness. The ec^2GAN is
used for various kinds of characterization of possible orders involving a
product that has just been introduced into the e-commerce system. The proposed
approach ec^2GAN performs significantly better than the baseline in most of the
scenarios.
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
Authors: M. Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko, Ebrahim. M. Songhori, Thomas Schneider, Farinaz Koushanfar
Date: 2018-01-10 00:00:00
Tasks: BIG-bench Machine Learning

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively.
Eliciting Worker Preference for Task Completion
Authors: Mohammadreza Esfandiari, Senjuti Basu Roy, Sihem Amer-Yahia
Date: 2018-01-10 00:00:00

Current crowdsourcing platforms provide little support for worker feedback.
Workers are sometimes invited to post free text describing their experience and
preferences in completing tasks. They can also use forums such as Turker
Nation1 to exchange preferences on tasks and requesters. In fact, crowdsourcing
platforms rely heavily on observing workers and inferring their preferences
implicitly. In this work, we believe that asking workers to indicate their
preferences explicitly improve their experience in task completion and hence,
the quality of their contributions. Explicit elicitation can indeed help to
build more accurate worker models for task completion that captures the
evolving nature of worker preferences. We design a worker model whose accuracy
is improved iteratively by requesting preferences for task factors such as
required skills, task payment, and task relevance. We propose a generic
framework, develop efficient solutions in realistic scenarios, and run
extensive experiments that show the benefit of explicit preference elicitation
over implicit ones with statistical significance.