pwc-archive/papers-with-abstracts huggingface dataset:
A quantum dynamic belief decision making model
Authors: Zichang He, Wen Jiang
Date: 2017-03-06 00:00:00
Tasks: Decision Making, model

The sure thing principle and the law of total probability are basic laws in
classic probability theory. A disjunction fallacy leads to the violation of
these two classical probability laws. In this paper, a new quantum dynamic
belief decision making model based on quantum dynamic modelling and
Dempster-Shafer (D-S) evidence theory is proposed to address this issue and
model the real human decision-making process. Some mathematical techniques are
borrowed from quantum mathematics. Generally, belief and action are two parts
in a decision making process. The uncertainty in belief part is represented by
a superposition of certain states. The uncertainty in actions is represented as
an extra uncertainty state. The interference effect is produced due to the
entanglement between beliefs and actions. Basic probability assignment (BPA) of
decisions is generated by quantum dynamic modelling. Then BPA of the extra
uncertain state and an entanglement degree defined by an entropy function named
Deng entropy are used to measure the interference effect. Compared the existing
model, the number of free parameters is less in our model. Finally, a classical
categorization decision-making experiment is illustrated to show the
effectiveness of our model.
High-Resolution Multispectral Dataset for Semantic Segmentation
Authors: Ronald Kemker, Carl Salvaggio, Christopher Kanan
Date: 2017-03-06 00:00:00
Tasks: General Classification, Semantic Segmentation, Vocal Bursts Intensity Prediction

Unmanned aircraft have decreased the cost required to collect remote sensing
imagery, which has enabled researchers to collect high-spatial resolution data
from multiple sensor modalities more frequently and easily. The increase in
data will push the need for semantic segmentation frameworks that are able to
classify non-RGB imagery, but this type of algorithmic development requires an
increase in publicly available benchmark datasets with class labels. In this
paper, we introduce a high-resolution multispectral dataset with image labels.
This new benchmark dataset has been pre-split into training/testing folds in
order to standardize evaluation and continue to push state-of-the-art
classification frameworks for non-RGB imagery.
Particle Smoothing for Hidden Diffusion Processes: Adaptive Path Integral Smoother
Authors: H. -Ch. Ruiz, H. J. Kappen
Date: 2016-05-01 00:00:00

Particle smoothing methods are used for inference of stochastic processes
based on noisy observations. Typically, the estimation of the marginal
posterior distribution given all observations is cumbersome and computational
intensive. In this paper, we propose a simple algorithm based on path integral
control theory to estimate the smoothing distribution of continuous-time
diffusion processes with partial observations. In particular, we use an
adaptive importance sampling method to improve the effective sampling size of
the posterior over processes given the observations and the reliability of the
estimation of the marginals. This is achieved by estimating a feedback
controller to sample efficiently from the joint smoothing distributions. We
compare the results with estimations obtained from the standard Forward
Filter/Backward Simulator for two diffusion processes of different complexity.
We show that the proposed method gives more reliable estimations than the
standard FFBSi when the smoothing distribution is poorly represented by the
filter distribution.
Near-Optimal Closeness Testing of Discrete Histogram Distributions
Authors: Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin
Date: 2017-03-06 00:00:00

We investigate the problem of testing the equivalence between two discrete
histograms. A {\em $k$-histogram} over $[n]$ is a probability distribution that
is piecewise constant over some set of $k$ intervals over $[n]$. Histograms
have been extensively studied in computer science and statistics. Given a set
of samples from two $k$-histogram distributions $p, q$ over $[n]$, we want to
distinguish (with high probability) between the cases that $p = q$ and
$\|p-q\|_1 \geq \epsilon$. The main contribution of this paper is a new
algorithm for this testing problem and a nearly matching information-theoretic
lower bound. Specifically, the sample complexity of our algorithm matches our
lower bound up to a logarithmic factor, improving on previous work by
polynomial factors in the relevant parameters. Our algorithmic approach applies
in a more general setting and yields improved sample upper bounds for testing
closeness of other structured distributions as well.
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
Authors: Sebastian Schmitt, Johann Klaehn, Guillaume Bellec, Andreas Gruebl, Maurice Guettler, Andreas Hartel, Stephan Hartmann, Dan Husmann, Kai Husmann, Vitali Karasenko, Mitja Kleider, Christoph Koke, Christian Mauch, Eric Mueller, Paul Mueller, Johannes Partzsch, Mihai A. Petrovici, Stefan Schiefer, Stefan Scholze, Bernhard Vogginger, Robert Legenstein, Wolfgang Maass, Christian Mayr, Johannes Schemmel, Karlheinz Meier
Date: 2017-03-06 00:00:00

Emulating spiking neural networks on analog neuromorphic hardware offers
several advantages over simulating them on conventional computers, particularly
in terms of speed and energy consumption. However, this usually comes at the
cost of reduced control over the dynamics of the emulated networks. In this
paper, we demonstrate how iterative training of a hardware-emulated network can
compensate for anomalies induced by the analog substrate. We first convert a
deep neural network trained in software to a spiking network on the BrainScaleS
wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10
000 compared to the biological time domain. This mapping is followed by the
in-the-loop training, where in each training step, the network activity is
first recorded in hardware and then used to compute the parameter updates in
software via backpropagation. An essential finding is that the parameter
updates do not have to be precise, but only need to approximately follow the
correct gradient, which simplifies the computation of updates. Using this
approach, after only several tens of iterations, the spiking network shows an
accuracy close to the ideal software-emulated prototype. The presented
techniques show that deep spiking networks emulated on analog neuromorphic
devices can attain good computational performance despite the inherent
variations of the analog substrate.
Approximate Muscle Guided Beam Search for Three-Index Assignment Problem
Authors: He Jiang, Shuwei Zhang, Zhilei Ren, Xiaochen Lai, Yong Piao
Date: 2017-03-06 00:00:00

As a well-known NP-hard problem, the Three-Index Assignment Problem (AP3) has
attracted lots of research efforts for developing heuristics. However, existing
heuristics either obtain less competitive solutions or consume too much time.
In this paper, a new heuristic named Approximate Muscle guided Beam Search
(AMBS) is developed to achieve a good trade-off between solution quality and
running time. By combining the approximate muscle with beam search, the
solution space size can be significantly decreased, thus the time for searching
the solution can be sharply reduced. Extensive experimental results on the
benchmark indicate that the new algorithm is able to obtain solutions with
competitive quality and it can be employed on instances with largescale. Work
of this paper not only proposes a new efficient heuristic, but also provides a
promising method to improve the efficiency of beam search.
Direct White Matter Bundle Segmentation using Stacked U-Nets
Authors: Jakob Wasserthal, Peter F. Neher, Fabian Isensee, Klaus H. Maier-Hein
Date: 2017-03-06 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The state-of-the-art method for automatically segmenting white matter bundles
in diffusion-weighted MRI is tractography in conjunction with streamline
cluster selection. This process involves long chains of processing steps which
are not only computationally expensive but also complex to setup and tedious
with respect to quality control. Direct bundle segmentation methods treat the
task as a traditional image segmentation problem. While they so far did not
deliver competitive results, they can potentially mitigate many of the
mentioned issues. We present a novel supervised approach for direct tract
segmentation that shows major performance gains. It builds upon a stacked U-Net
architecture which is trained on manual bundle segmentations from Human
Connectome Project subjects. We evaluate our approach \textit{in vivo} as well
as \textit{in silico} using the ISMRM 2015 Tractography Challenge phantom
dataset. We achieve human segmentation performance and a major performance gain
over previous pipelines. We show how the learned spatial priors efficiently
guide the segmentation even at lower image qualities with little quality loss.
Deep Head Pose Estimation from Depth Data for In-car Automotive Applications
Authors: Marco Venturelli, Guido Borghi, Roberto Vezzani, Rita Cucchiara
Date: 2017-03-06 00:00:00
Tasks: Head Pose Estimation, Pose Estimation, regression

Recently, deep learning approaches have achieved promising results in various
fields of computer vision. In this paper, we tackle the problem of head pose
estimation through a Convolutional Neural Network (CNN). Differently from other
proposals in the literature, the described system is able to work directly and
based only on raw depth data. Moreover, the head pose estimation is solved as a
regression problem and does not rely on visual facial features like facial
landmarks. We tested our system on a well known public dataset, Biwi Kinect
Head Pose, showing that our approach achieves state-of-art results and is able
to meet real time performance requirements.
ShaResNet: reducing residual network parameter number by sharing weights
Authors: Alexandre Boulch
Date: 2017-02-28 00:00:00
Tasks: image-classification, Image Classification

Deep Residual Networks have reached the state of the art in many image
processing tasks such image classification. However, the cost for a gain in
accuracy in terms of depth and memory is prohibitive as it requires a higher
number of residual blocks, up to double the initial value. To tackle this
problem, we propose in this paper a way to reduce the redundant information of
the networks. We share the weights of convolutional layers between residual
blocks operating at the same spatial scale. The signal flows multiple times in
the same convolutional layer. The resulting architecture, called ShaResNet,
contains block specific layers and shared layers. These ShaResNet are trained
exactly in the same fashion as the commonly used residual networks. We show, on
the one hand, that they are almost as efficient as their sequential
counterparts while involving less parameters, and on the other hand that they
are more efficient than a residual network with the same number of parameters.
For example, a 152-layer-deep residual network can be reduced to 106
convolutional layers, i.e. a parameter gain of 39\%, while loosing less than
0.2\% accuracy on ImageNet.
Critical Echo State Networks that Anticipate Input using Morphable Transfer Functions
Authors: Norbert Michael Mayer
Date: 2016-06-12 00:00:00
Tasks: Vocal Bursts Type Prediction

The paper investigates a new type of truly critical echo state networks where
individual transfer functions for every neuron can be modified to anticipate
the expected next input. Deviations from expected input are only forgotten
slowly in power law fashion. The paper outlines the theory, numerically
analyzes a one neuron model network and finally discusses technical and also
biological implications of this type of approach.
The Power of Side-information in Subgraph Detection
Authors: Arun Kadavankandy, Konstantin Avrachenkov, Laura Cottatellucci, Rajesh Sundaresan
Date: 2016-11-10 00:00:00
Tasks: Community Detection

In this work, we tackle the problem of hidden community detection. We
consider Belief Propagation (BP) applied to the problem of detecting a hidden
Erd\H{o}s-R\'enyi (ER) graph embedded in a larger and sparser ER graph, in the
presence of side-information. We derive two related algorithms based on BP to
perform subgraph detection in the presence of two kinds of side-information.
The first variant of side-information consists of a set of nodes, called cues,
known to be from the subgraph. The second variant of side-information consists
of a set of nodes that are cues with a given probability. It was shown in past
works that BP without side-information fails to detect the subgraph correctly
when an effective signal-to-noise ratio (SNR) parameter falls below a
threshold. In contrast, in the presence of non-trivial side-information, we
show that the BP algorithm achieves asymptotically zero error for any value of
the SNR parameter. We validate our results through simulations on synthetic
datasets as well as on a few real world networks.
The Neural Noisy Channel
Authors: Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomas Kocisky
Date: 2016-11-08 00:00:00
Tasks: Decoder, Machine Translation, Morphological Inflection, Sentence, Translation

We formulate sequence to sequence transduction as a noisy channel decoding
problem and use recurrent neural networks to parameterise the source and
channel models. Unlike direct models which can suffer from explaining-away
effects during training, noisy channel models must produce outputs that explain
their inputs, and their component models can be trained with not only paired
training samples but also unpaired samples from the marginal output
distribution. Using a latent variable to control how much of the conditioning
sequence the channel model needs to read in order to generate a subsequent
symbol, we obtain a tractable and effective beam search decoder. Experimental
results on abstractive sentence summarisation, morphological inflection, and
machine translation show that noisy channel models outperform direct models,
and that they significantly benefit from increased amounts of unpaired output
data that direct models cannot easily use.
Multi-GPU parallel memetic algorithm for capacitated vehicle routing problem
Authors: Michał Karpiński, Maciej Pacut
Date: 2014-01-21 00:00:00
Tasks: GPU

The goal of this paper is to propose and test a new memetic algorithm for the
capacitated vehicle routing problem in parallel computing environment. In this
paper we consider simple variation of vehicle routing problem in which the only
parameter is the capacity of the vehicle and each client only needs one
package. We present simple reduction to prove the existence of polynomial-time
algorithm for capacity 2. We analyze the efficiency of the algorithm using
hierarchical Parallel Random Access Machine (PRAM) model and run experiments
with code written in CUDA (for capacities larger than 2).
Decomposable Submodular Function Minimization: Discrete and Continuous
Authors: Alina Ene, Huy L. Nguyen, László A. Végh
Date: 2017-03-06 00:00:00
Proceeding: NeurIPS 2017 12

This paper investigates connections between discrete and continuous
approaches for decomposable submodular function minimization. We provide
improved running time estimates for the state-of-the-art continuous algorithms
for the problem using combinatorial arguments. We also provide a systematic
experimental comparison of the two types of methods, based on a clear
distinction between level-0 and level-1 algorithms.
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
Authors: Vincent Drouard, Radu Horaud, Antoine Deleforge, Silèye Ba, Georgios Evangelidis
Date: 2016-03-31 00:00:00
Tasks: Face Alignment, Head Pose Estimation, Pose Estimation

Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods.
Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization
Authors: Georgios Evangelidis, Radu Horaud
Date: 2016-09-06 00:00:00
Tasks: Clustering, Surface Reconstruction

This paper addresses the problem of registering multiple point sets.
Solutions to this problem are often approximated by repeatedly solving for
pairwise registration, which results in an uneven treatment of the sets forming
a pair: a model set and a data set. The main drawback of this strategy is that
the model set may contain noise and outliers, which negatively affects the
estimation of the registration parameters. In contrast, the proposed
formulation treats all the point sets on an equal footing. Indeed, all the
points are drawn from a central Gaussian mixture, hence the registration is
cast into a clustering problem. We formally derive batch and incremental EM
algorithms that robustly estimate both the GMM parameters and the rotations and
translations that optimally align the sets. Moreover, the mixture's means play
the role of the registered set of points while the variances provide rich
information about the contribution of each component to the alignment. We
thoroughly test the proposed algorithms on simulated data and on challenging
real data collected with range sensors. We compare them with several
state-of-the-art algorithms, and we show their potential for surface
reconstruction from depth data.
Building a Regular Decision Boundary with Deep Networks
Authors: Edouard Oyallon
Date: 2017-03-06 00:00:00
Proceeding: CVPR 2017 7

In this work, we build a generic architecture of Convolutional Neural
Networks to discover empirical properties of neural networks. Our first
contribution is to introduce a state-of-the-art framework that depends upon few
hyper parameters and to study the network when we vary them. It has no max
pooling, no biases, only 13 layers, is purely convolutional and yields up to
95.4% and 79.6% accuracy respectively on CIFAR10 and CIFAR100. We show that the
nonlinearity of a deep network does not need to be continuous, non expansive or
point-wise, to achieve good performance. We show that increasing the width of
our network permits being competitive with very deep networks. Our second
contribution is an analysis of the contraction and separation properties of
this network. Indeed, a 1-nearest neighbor classifier applied on deep features
progressively improves with depth, which indicates that the representation is
progressively more regular. Besides, we defined and analyzed local support
vectors that separate classes locally. All our experiments are reproducible and
code is available online, based on TensorFlow.
Efficiency and complexity of price competition among single-product vendors
Authors: Ioannis Caragiannis, Xenophon Chatzigeorgiou, Panagiotis Kanellopoulos, George A. Krimpas, Nikos Protopapas, Alexandros A. Voudouris
Date: 2015-02-13 00:00:00

Motivated by recent progress on pricing in the AI literature, we study
marketplaces that contain multiple vendors offering identical or similar
products and unit-demand buyers with different valuations on these vendors. The
objective of each vendor is to set the price of its product to a fixed value so
that its profit is maximized. The profit depends on the vendor's price itself
and the total volume of buyers that find the particular price more attractive
than the price of the vendor's competitors. We model the behaviour of buyers
and vendors as a two-stage full-information game and study a series of
questions related to the existence, efficiency (price of anarchy) and
computational complexity of equilibria in this game. To overcome situations
where equilibria do not exist or exist but are highly inefficient, we consider
the scenario where some of the vendors are subsidized in order to keep prices
low and buyers highly satisfied.
The Mythos of Model Interpretability
Authors: Zachary C. Lipton
Date: 2016-06-10 00:00:00
Tasks: model

Supervised machine learning models boast remarkable predictive capabilities.
But can you trust your model? Will it work in deployment? What else can it tell
you about the world? We want models to be not only good, but interpretable. And
yet the task of interpretation appears underspecified. Papers provide diverse
and sometimes non-overlapping motivations for interpretability, and offer
myriad notions of what attributes render models interpretable. Despite this
ambiguity, many papers proclaim interpretability axiomatically, absent further
explanation. In this paper, we seek to refine the discourse on
interpretability. First, we examine the motivations underlying interest in
interpretability, finding them to be diverse and occasionally discordant. Then,
we address model properties and techniques thought to confer interpretability,
identifying transparency to humans and post-hoc explanations as competing
notions. Throughout, we discuss the feasibility and desirability of different
notions, and question the oft-made assertions that linear models are
interpretable and that deep neural networks are not.
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Authors: Joshua Achiam, Shankar Sastry
Date: 2017-03-06 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Exploration in complex domains is a key challenge in reinforcement learning,
especially for tasks with very sparse rewards. Recent successes in deep
reinforcement learning have been achieved mostly using simple heuristic
exploration strategies such as $\epsilon$-greedy action selection or Gaussian
control noise, but there are many tasks where these methods are insufficient to
make any learning progress. Here, we consider more complex heuristics:
efficient and scalable exploration strategies that maximize a notion of an
agent's surprise about its experiences via intrinsic motivation. We propose to
learn a model of the MDP transition probabilities concurrently with the policy,
and to form intrinsic rewards that approximate the KL-divergence of the true
transition probabilities from the learned model. One of our approximations
results in using surprisal as intrinsic motivation, while the other gives the
$k$-step learning progress. We show that our incentives enable agents to
succeed in a wide range of environments with high-dimensional state spaces and
very sparse rewards, including continuous control tasks and games in the Atari
RAM domain, outperforming several other heuristic exploration techniques.