pwc-archive/papers-with-abstracts huggingface dataset:
A Simple Quantum Neural Net with a Periodic Activation Function
Authors: Ammar Daskin
Date: 2018-04-20 00:00:00

In this paper, we propose a simple neural net that requires only $O(nlog_2k)$
number of qubits and $O(nk)$ quantum gates: Here, $n$ is the number of input
parameters, and $k$ is the number of weights applied to these parameters in the
proposed neural net. We describe the network in terms of a quantum circuit, and
then draw its equivalent classical neural net which involves $O(k^n)$ nodes in
the hidden layer. Then, we show that the network uses a periodic activation
function of cosine values of the linear combinations of the inputs and weights.
The backpropagation is described through the gradient descent, and then iris
and breast cancer datasets are used for the simulations. The numerical results
indicate the network can be used in machine learning problems and it may
provide exponential speedup over the same structured classical neural net.
ASR Performance Prediction on Unseen Broadcast Programs using Convolutional Neural Networks
Authors: Zied Elloumi, Laurent Besacier, Olivier Galibert, Juliette Kahn, Benjamin Lecouteux
Date: 2018-04-23 00:00:00
Tasks: Prediction, regression

In this paper, we address a relatively new task: prediction of ASR
performance on unseen broadcast programs. We first propose an heterogenous
French corpus dedicated to this task. Two prediction approaches are compared: a
state-of-the-art performance prediction based on regression (engineered
features) and a new strategy based on convolutional neural networks (learnt
features). We particularly focus on the combination of both textual (ASR
transcription) and signal inputs. While the joint use of textual and signal
features did not work for the regression baseline, the combination of inputs
for CNNs leads to the best WER prediction performance. We also show that our
CNN prediction remarkably predicts the WER distribution on a collection of
speech recordings.
High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model
Authors: Liao Zhu, Sumanta Basu, Robert A. Jarrow, Martin T. Wells
Date: 2018-04-23 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.
Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories
Authors: Daniil Sorokin, Iryna Gurevych
Date: 2018-04-23 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Entity Disambiguation, Entity Linking, Knowledge Base Question Answering, Question Answering

The first stage of every knowledge base question answering approach is to
link entities in the input question. We investigate entity linking in the
context of a question answering task and present a jointly optimized neural
architecture for entity mention detection and entity disambiguation that models
the surrounding context on different levels of granularity. We use the Wikidata
knowledge base and available question answering datasets to create benchmarks
for entity linking on question answering data. Our approach outperforms the
previous state-of-the-art system on this data, resulting in an average 8%
improvement of the final score. We further demonstrate that our model delivers
a strong performance across different entity categories.
Entropy production rate as a criterion for inconsistency in decision theory
Authors: Purushottam D. Dixit
Date: 2018-01-05 00:00:00
Tasks: Decision Making

Individual and group decisions are complex, often involving choosing an apt
alternative from a multitude of options. Evaluating pairwise comparisons breaks
down such complex decision problems into tractable ones. Pairwise comparison
matrices (PCMs) are regularly used to solve multiple-criteria decision-making
(MCDM) problems, for example, using Saaty's analytic hierarchy process (AHP)
framework. However, there are two significant drawbacks of using PCMs. First,
humans evaluate PCMs in an inconsistent manner. Second, not all entries of a
large PCM can be reliably filled by human decision makers. We address these two
issues by first establishing a novel connection between PCMs and
time-irreversible Markov processes. Specifically, we show that every PCM
induces a family of dissipative maximum path entropy random walks (MERW) over
the set of alternatives. We show that only `consistent' PCMs correspond to
detailed balanced MERWs. We identify the non-equilibrium entropy production in
the induced MERWs as a metric of inconsistency of the underlying PCMs. Notably,
the entropy production satisfies all of the recently laid out criteria for
reasonable consistency indices. We also propose an approach to use incompletely
filled PCMs in AHP. Potential future avenues are discussed as well.
  keywords: analytic hierarchy process, markov chains, maximum entropy
Attention Based Natural Language Grounding by Navigating Virtual Environment
Authors: Akilesh B, Abhishek Sinha, Mausoom Sarkar, Balaji Krishnamurthy
Date: 2018-04-23 00:00:00
Tasks: Navigate, Zero-shot Generalization

In this work, we focus on the problem of grounding language by training an
agent to follow a set of natural language instructions and navigate to a target
object in an environment. The agent receives visual information through raw
pixels and a natural language instruction telling what task needs to be
achieved and is trained in an end-to-end way. We develop an attention mechanism
for multi-modal fusion of visual and textual modalities that allows the agent
to learn to complete the task and achieve language grounding. Our experimental
results show that our attention mechanism outperforms the existing multi-modal
fusion mechanisms proposed for both 2D and 3D environments in order to solve
the above-mentioned task in terms of both speed and success rate. We show that
the learnt textual representations are semantically meaningful as they follow
vector arithmetic in the embedding space. The effectiveness of our attention
approach over the contemporary fusion mechanisms is also highlighted from the
textual embeddings learnt by the different approaches. We also show that our
model generalizes effectively to unseen scenarios and exhibit zero-shot
generalization capabilities both in 2D and 3D environments. The code for our 2D
environment as well as the models that we developed for both 2D and 3D are
available at https://github.com/rl-lang-grounding/rl-lang-ground.
Decorrelated Batch Normalization
Authors: Lei Huang, Dawei Yang, Bo Lang, Jia Deng
Date: 2018-04-23 00:00:00
Proceeding: CVPR 2018 6

Batch Normalization (BN) is capable of accelerating the training of deep
models by centering and scaling activations within mini-batches. In this work,
we propose Decorrelated Batch Normalization (DBN), which not just centers and
scales activations but whitens them. We explore multiple whitening techniques,
and find that PCA whitening causes a problem we call stochastic axis swapping,
which is detrimental to learning. We show that ZCA whitening does not suffer
from this problem, permitting successful learning. DBN retains the desirable
qualities of BN and further improves BN's optimization efficiency and
generalization ability. We design comprehensive experiments to show that DBN
can improve the performance of BN on multilayer perceptrons and convolutional
neural networks. Furthermore, we consistently improve the accuracy of residual
networks on CIFAR-10, CIFAR-100, and ImageNet.
A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment
Authors: Tomi Kinnunen, Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Zhen-Hua Ling
Date: 2018-04-23 00:00:00
Tasks: Benchmarking, Speaker Verification, Voice Conversion

Voice conversion (VC) aims at conversion of speaker characteristic without
altering content. Due to training data limitations and modeling imperfections,
it is difficult to achieve believable speaker mimicry without introducing
processing artifacts; performance assessment of VC, therefore, usually involves
both speaker similarity and quality evaluation by a human panel. As a
time-consuming, expensive, and non-reproducible process, it hinders rapid
prototyping of new VC technology. We address artifact assessment using an
alternative, objective approach leveraging from prior work on spoofing
countermeasures (CMs) for automatic speaker verification. Therein, CMs are used
for rejecting `fake' inputs such as replayed, synthetic or converted speech but
their potential for automatic speech artifact assessment remains unknown. This
study serves to fill that gap. As a supplement to subjective results for the
2018 Voice Conversion Challenge (VCC'18) data, we configure a standard
constant-Q cepstral coefficient CM to quantify the extent of processing
artifacts. Equal error rate (EER) of the CM, a confusability index of VC
samples with real human speech, serves as our artifact measure. Two clusters of
VCC'18 entries are identified: low-quality ones with detectable artifacts (low
EERs), and higher quality ones with less artifacts. None of the VCC'18 systems,
however, is perfect: all EERs are < 30 % (the `ideal' value would be 50 %). Our
preliminary findings suggest potential of CMs outside of their original
application, as a supplemental optimization and benchmarking tool to enhance VC
technology.
Efficient Pose Tracking from Natural Features in Standard Web Browsers
Authors: Fabian Göttl, Philipp Gagel, Jens Grubert
Date: 2018-04-23 00:00:00
Tasks: Pose Estimation, Pose Tracking

Computer Vision-based natural feature tracking is at the core of modern
Augmented Reality applications. Still, Web-based Augmented Reality typically
relies on location-based sensing (using GPS and orientation sensors) or
marker-based approaches to solve the pose estimation problem.
  We present an implementation and evaluation of an efficient natural feature
tracking pipeline for standard Web browsers using HTML5 and WebAssembly. Our
system can track image targets at real-time frame rates tablet PCs (up to 60
Hz) and smartphones (up to 25 Hz).
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
Authors: Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K. Fishman, Alan L. Yuille
Date: 2018-04-23 00:00:00
Tasks: Organ Segmentation

Accurate and robust segmentation of abdominal organs on CT is essential for
many clinical applications such as computer-aided diagnosis and computer-aided
surgery. But this task is challenging due to the weak boundaries of organs, the
complexity of the background, and the variable sizes of different organs. To
address these challenges, we introduce a novel framework for multi-organ
segmentation by using organ-attention networks with reverse connections
(OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output
estimates which are combined by statistical fusion exploiting structural
similarity. OAN is a two-stage deep convolutional network, where deep network
features from the first stage are combined with the original image, in a second
stage, to reduce the complex background and enhance the discriminative
information for the target organs. RCs are added to the first stage to give the
lower layers semantic information thereby enabling them to adapt to the sizes
of different organs. Our networks are trained on 2D views enabling us to use
holistic information and allowing efficient computation. To compensate for the
limited cross-sectional information of the original 3D volumetric CT,
multi-sectional images are reconstructed from the three different 2D view
directions. Then we combine the segmentation results from the different views
using statistical fusion, with a novel term relating the structural similarity
of the 2D views to the original 3D structure. To train the network and evaluate
results, 13 structures were manually annotated by four human raters and
confirmed by a senior expert on 236 normal cases. We tested our algorithm and
computed Dice-Sorensen similarity coefficients and surface distances for
evaluating our estimates of the 13 structures. Our experiments show that the
proposed approach outperforms 2D- and 3D-patch based state-of-the-art methods.
State Distribution-aware Sampling for Deep Q-learning
Authors: Weichao Li, Fuxian Huang, Xi Li, Gang Pan, Fei Wu
Date: 2018-04-23 00:00:00
Tasks: Atari Games, OpenAI Gym, Q-Learning, Reinforcement Learning

A critical and challenging problem in reinforcement learning is how to learn
the state-action value function from the experience replay buffer and
simultaneously keep sample efficiency and faster convergence to a high quality
solution. In prior works, transitions are uniformly sampled at random from the
replay buffer or sampled based on their priority measured by
temporal-difference (TD) error. However, these approaches do not fully take
into consideration the intrinsic characteristics of transition distribution in
the state space and could result in redundant and unnecessary TD updates,
slowing down the convergence of the learning procedure. To overcome this
problem, we propose a novel state distribution-aware sampling method to balance
the replay times for transitions with skew distribution, which takes into
account both the occurrence frequencies of transitions and the uncertainty of
state-action values. Consequently, our approach could reduce the unnecessary TD
updates and increase the TD updates for state-action value with more
uncertainty, making the experience replay more effective and efficient.
Extensive experiments are conducted on both classic control tasks and Atari
2600 games based on OpenAI gym platform and the experimental results
demonstrate the effectiveness of our approach in comparison with the standard
DQN approach.
Path Planning in Support of Smart Mobility Applications using Generative Adversarial Networks
Authors: Mehdi Mohammadi, Ala Al-Fuqaha, Jun-Seok Oh
Date: 2018-04-23 00:00:00
Tasks: Autonomous Vehicles, Navigate

This paper describes and evaluates the use of Generative Adversarial Networks
(GANs) for path planning in support of smart mobility applications such as
indoor and outdoor navigation applications, individualized wayfinding for
people with disabilities (e.g., vision impairments, physical disabilities,
etc.), path planning for evacuations, robotic navigations, and path planning
for autonomous vehicles. We propose an architecture based on GANs to recommend
accurate and reliable paths for navigation applications. The proposed system
can use crowd-sourced data to learn the trajectories and infer new ones. The
system provides users with generated paths that help them navigate from their
local environment to reach a desired location. As a use case, we experimented
with the proposed method in support of a wayfinding application in an indoor
environment. Our experiments assert that the generated paths are correct and
reliable. The accuracy of the classification task for the generated paths is up
to 99% and the quality of the generated paths has a mean opinion score of 89%.
CatBoost: unbiased boosting with categorical features
Authors: Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin
Date: 2017-06-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Clustering, Dimensionality Reduction, General Classification

This paper presents the key algorithmic techniques behind CatBoost, a new
gradient boosting toolkit. Their combination leads to CatBoost outperforming
other publicly available boosting implementations in terms of quality on a
variety of datasets. Two critical algorithmic advances introduced in CatBoost
are the implementation of ordered boosting, a permutation-driven alternative to
the classic algorithm, and an innovative algorithm for processing categorical
features. Both techniques were created to fight a prediction shift caused by a
special kind of target leakage present in all currently existing
implementations of gradient boosting algorithms. In this paper, we provide a
detailed analysis of this problem and demonstrate that proposed algorithms
solve it effectively, leading to excellent empirical results.
Developing Synthesis Flows Without Human Knowledge
Authors: Cunxi Yu, Houping Xiao, Giovanni De Micheli
Date: 2018-04-16 00:00:00

Design flows are the explicit combinations of design transformations,
primarily involved in synthesis, placement and routing processes, to accomplish
the design of Integrated Circuits (ICs) and System-on-Chip (SoC). Mostly, the
flows are developed based on the knowledge of the experts. However, due to the
large search space of design flows and the increasing design complexity,
developing Intellectual Property (IP)-specific synthesis flows providing high
Quality of Result (QoR) is extremely challenging. This work presents a fully
autonomous framework that artificially produces design-specific synthesis flows
without human guidance and baseline flows, using Convolutional Neural Network
(CNN). The demonstrations are made by successfully designing logic synthesis
flows of three large scaled designs.
The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
Authors: Ole-Christoffer Granmo
Date: 2018-04-04 00:00:00
Tasks: Image Classification, Unity

Although simple individually, artificial neurons provide state-of-the-art
performance when interconnected in deep networks. Unknown to many, there exists
an arguably even simpler and more versatile learning mechanism, namely, the
Tsetlin Automaton. Merely by means of a single integer as memory, it learns the
optimal action in stochastic environments through increment and decrement
operations. In this paper, we introduce the Tsetlin Machine, which solves
complex pattern recognition problems with easy-to-interpret propositional
formulas, composed by a collective of Tsetlin Automata. To eliminate the
longstanding problem of vanishing signal-to-noise ratio, the Tsetlin Machine
orchestrates the automata using a novel game. Our theoretical analysis
establishes that the Nash equilibria of the game align with the propositional
formulas that provide optimal pattern recognition accuracy. This translates to
learning without local optima, only global ones. We argue that the Tsetlin
Machine finds the propositional formula that provides optimal accuracy, with
probability arbitrarily close to unity. In five benchmarks, the Tsetlin Machine
provides competitive accuracy compared with SVMs, Decision Trees, Random
Forests, Naive Bayes Classifier, Logistic Regression, and Neural Networks. The
Tsetlin Machine further has an inherent computational advantage since both
inputs, patterns, and outputs are expressed as bits, while recognition and
learning rely on bit manipulation. The combination of accuracy,
interpretability, and computational simplicity makes the Tsetlin Machine a
promising tool for a wide range of domains. Being the first of its kind, we
believe the Tsetlin Machine will kick-start new paths of research, with a
potentially significant impact on the AI field and the applications of AI.
STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection
Authors: Sangmin Lee, Hak Gu Kim, Yong Man Ro
Date: 2018-04-23 00:00:00
Tasks: Anomaly Detection, Event Detection

In this paper, we propose a novel abnormal event detection method with
spatio-temporal adversarial networks (STAN). We devise a spatio-temporal
generator which synthesizes an inter-frame by considering spatio-temporal
characteristics with bidirectional ConvLSTM. A proposed spatio-temporal
discriminator determines whether an input sequence is real-normal or not with
3D convolutional layers. These two networks are trained in an adversarial way
to effectively encode spatio-temporal features of normal patterns. After the
learning, the generator and the discriminator can be independently used as
detectors, and deviations from the learned normal patterns are detected as
abnormalities. Experimental results show that the proposed method achieved
competitive performance compared to the state-of-the-art methods. Further, for
the interpretation, we visualize the location of abnormal events detected by
the proposed networks using a generator loss and discriminator gradients.
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism
Authors: Nicolas Weber, Florian Schmidt, Mathias Niepert, Felipe Huici
Date: 2018-04-23 00:00:00
Tasks: Deep Learning, Machine Translation, Object Recognition, Translation

Neural network frameworks such as PyTorch and TensorFlow are the workhorses
of numerous machine learning applications ranging from object recognition to
machine translation. While these frameworks are versatile and straightforward
to use, the training of and inference in deep neural networks is resource
(energy, compute, and memory) intensive. In contrast to recent works focusing
on algorithmic enhancements, we introduce BrainSlug, a framework that
transparently accelerates neural network workloads by changing the default
layer-by-layer processing to a depth-first approach, reducing the amount of
data required by the computations and thus improving the performance of the
available hardware caches. BrainSlug achieves performance improvements of up to
41.1% on CPUs and 35.7% on GPUs. These optimizations come at zero cost to the
user as they do not require hardware changes and only need tiny adjustments to
the software.
Convolutional capsule network for classification of breast cancer histology images
Authors: Tomas Iesmantas, Robertas Alzbutas
Date: 2018-04-23 00:00:00
Tasks: Classification, Classification Of Breast Cancer Histology Images, Deep Learning, General Classification, Sensitivity

Automatization of the diagnosis of any kind of disease is of great importance
and it's gaining speed as more and more deep learning solutions are applied to
different problems. One of such computer aided systems could be a decision
support too able to accurately differentiate between different types of breast
cancer histological images - normal tissue or carcinoma. In this paper authors
present a deep learning solution, based on convolutional capsule network for
classification of four types of images of breast tissue biopsy when hematoxylin
and eusin staining is applied. The cross-validation accuracy was achieved to be
0.87 with equaly high sensitivity.
A Stronger Foundation for Computer Science and P=NP
Authors: Mark Inman
Date: 2017-08-18 00:00:00

This article describes a Turing machine which can solve for $\beta^{'}$ which
is RE-complete. RE-complete problems are proven to be undecidable by Turing's
accepted proof on the Entscheidungsproblem. Thus, constructing a machine which
decides over $\beta^{'}$ implies inconsistency in ZFC. We then discover that
unrestricted use of the axiom of substitution can lead to hidden assumptions in
a certain class of proofs by contradiction. These hidden assumptions create an
implied axiom of incompleteness for ZFC. Later, we offer a restriction on the
axiom of substitution by introducing a new axiom which prevents impredicative
tautologies from producing theorems. Our discovery in regards to these
foundational arguments, disproves the SPACE hierarchy theorem which allows us
to solve the P vs NP problem using a TIME-SPACE equivalence oracle.