pwc-archive/papers-with-abstracts huggingface dataset:
Linear dynamical neural population models through nonlinear embeddings
Authors: Yuanjun Gao, Evan Archer, Liam Paninski, John P. Cunningham
Date: 2016-05-26 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Variational Inference

A body of recent work in modeling neural activity focuses on recovering
low-dimensional latent features that capture the statistical structure of
large-scale neural populations. Most such approaches have focused on linear
generative models, where inference is computationally tractable. Here, we
propose fLDS, a general class of nonlinear generative models that permits the
firing rate of each neuron to vary as an arbitrary smooth function of a latent,
linear dynamical state. This extra flexibility allows the model to capture a
richer set of neural variability than a purely linear model, but retains an
easily visualizable low-dimensional latent space. To fit this class of
non-conjugate models we propose a variational inference scheme, along with a
novel approximate posterior capable of capturing rich temporal correlations
across time. We show that our techniques permit inference in a wide class of
generative models.We also show in application to two neural datasets that,
compared to state-of-the-art neural population models, fLDS captures a much
larger proportion of neural variability with a small number of latent
dimensions, providing superior predictive performance and interpretability.
Energy-efficient Machine Learning in Silicon: A Communications-inspired Approach
Authors: Naresh R. Shanbhag
Date: 2016-10-25 00:00:00
Tasks: BIG-bench Machine Learning, Position

This position paper advocates a communications-inspired approach to the
design of machine learning systems on energy-constrained embedded `always-on'
platforms. The communications-inspired approach has two versions - 1) a
deterministic version where existing low-power communication IC design methods
are repurposed, and 2) a stochastic version referred to as Shannon-inspired
statistical information processing employing information-based metrics,
statistical error compensation (SEC), and retraining-based methods to implement
ML systems on stochastic circuit/device fabrics operating at the limits of
energy-efficiency. The communications-inspired approach has the potential to
fully leverage the opportunities afforded by ML algorithms and applications in
order to address the challenges inherent in their deployment on
energy-constrained platforms.
Verifier Theory and Unverifiability
Authors: Roman V. Yampolskiy
Date: 2016-09-01 00:00:00
Tasks: Automated Theorem Proving, General Classification

Despite significant developments in Proof Theory, surprisingly little
attention has been devoted to the concept of proof verifier. In particular, the
mathematical community may be interested in studying different types of proof
verifiers (people, programs, oracles, communities, superintelligences) as
mathematical objects. Such an effort could reveal their properties, their
powers and limitations (particularly in human mathematicians), minimum and
maximum complexity, as well as self-verification and self-reference issues. We
propose an initial classification system for verifiers and provide some
rudimentary analysis of solved and open problems in this important domain. Our
main contribution is a formal introduction of the notion of unverifiability,
for which the paper could serve as a general citation in domains of theorem
proving, as well as software and AI verification.
Statistical Machine Translation for Indian Languages: Mission Hindi 2
Authors: Raj Nath Patel, Prakash B. Pimpale
Date: 2016-10-25 00:00:00
Tasks: Machine Translation, Translation

This paper presents Centre for Development of Advanced Computing Mumbai's
(CDACM) submission to NLP Tools Contest on Statistical Machine Translation in
Indian Languages (ILSMT) 2015 (collocated with ICON 2015). The aim of the
contest was to collectively explore the effectiveness of Statistical Machine
Translation (SMT) while translating within Indian languages and between English
and Indian languages. In this paper, we report our work on all five language
pairs, namely Bengali-Hindi (\bnhi), Marathi-Hindi (\mrhi), Tamil-Hindi
(\tahi), Telugu-Hindi (\tehi), and English-Hindi (\enhi) for Health, Tourism,
and General domains. We have used suffix separation, compound splitting and
preordering prior to SMT training and testing.
Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures
Authors: Roman V. Yampolskiy, M. S. Spellchecker
Date: 2016-10-25 00:00:00

In this work, we present and analyze reported failures of artificially
intelligent systems and extrapolate our analysis to future AIs. We suggest that
both the frequency and the seriousness of future AI failures will steadily
increase. AI Safety can be improved based on ideas developed by cybersecurity
experts. For narrow AIs safety failures are at the same, moderate, level of
criticality as in cybersecurity, however for general AI, failures have a
fundamentally different impact. A single failure of a superintelligent system
may cause a catastrophic event without a chance for recovery. The goal of
cybersecurity is to reduce the number of successful attacks on the system; the
goal of AI Safety is to make sure zero attacks succeed in bypassing the safety
mechanisms. Unfortunately, such a level of performance is unachievable. Every
security system will eventually fail; there is no such thing as a 100% secure
system.
Process Discovery using Inductive Miner and Decomposition
Authors: Raji Ghawi
Date: 2016-10-25 00:00:00
Tasks: General Classification

This report presents a submission to the Process Discovery Contest. The
contest is dedicated to the assessment of tools and techniques that discover
business process models from event logs. The objective is to compare the
efficiency of techniques to discover process models that provide a proper
balance between "overfitting" and "underfitting". In the context of the Process
Discovery Contest, process discovery is turned into a classification task with
a training set and a test set; where a process model needs to decide whether
traces are fitting or not. In this report, we first show how we use two
discovery techniques, namely: Inductive Miner and Decomposition, to discover
process models from the training set using ProM tool. Second, we show how we
use replay results to 1) check the rediscoverability of models, and to 2)
classify unseen traces (in test logs) as fitting or not. Then, we discuss the
classification results of validation logs, the complexity of discovered models,
and their impact on the selection of models for submission. The report ends
with the pictures of the submitted process models.
PATH: Person Authentication using Trace Histories
Authors: Upal Mahbub, Rama Chellappa
Date: 2016-10-25 00:00:00

In this paper, a solution to the problem of Active Authentication using trace
histories is addressed. Specifically, the task is to perform user verification
on mobile devices using historical location traces of the user as a function of
time. Considering the movement of a human as a Markovian motion, a modified
Hidden Markov Model (HMM)-based solution is proposed. The proposed method,
namely the Marginally Smoothed HMM (MSHMM), utilizes the marginal probabilities
of location and timing information of the observations to smooth-out the
emission probabilities while training. Hence, it can efficiently handle
unforeseen observations during the test phase. The verification performance of
this method is compared to a sequence matching (SM) method , a Markov
Chain-based method (MC) and an HMM with basic Laplace Smoothing (HMM-lap).
Experimental results using the location information of the UMD Active
Authentication Dataset-02 (UMDAA02) and the GeoLife dataset are presented. The
proposed MSHMM method outperforms the compared methods in terms of equal error
rate (EER). Additionally, the effects of different parameters on the proposed
method are discussed.
Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm
Authors: Seth D. Billings, Ayushi Sinha, Austin Reiter, Simon Leonard, Masaru Ishii, Gregory D. Hager, Russell H. Taylor
Date: 2016-10-25 00:00:00

Functional endoscopic sinus surgery (FESS) is a surgical procedure used to
treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming
the preferred choice of treatment due to its minimally invasive nature.
However, due to the limited field of view of the endoscope, surgeons rely on
navigation systems to guide them within the nasal cavity. State of the art
navigation systems report registration accuracy of over 1mm, which is large
compared to the size of the nasal airways. We present an anatomically
constrained video-CT registration algorithm that incorporates multiple video
features. Our algorithm is robust in the presence of outliers. We also test our
algorithm on simulated and in-vivo data, and test its accuracy against
degrading initializations.
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
Authors: Upal Mahbub, Sayantan Sarkar, Vishal M. Patel, Rama Chellappa
Date: 2016-10-25 00:00:00
Tasks: Face Detection, Face Verification, User Identification

In this paper, automated user verification techniques for smartphones are
investigated. A unique non-commercial dataset, the University of Maryland
Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication
research is introduced. This paper focuses on three sensors - front camera,
touch sensor and location service while providing a general description for
other modalities. Benchmark results for face detection, face verification,
touch-based user identification and location-based next-place prediction are
presented, which indicate that more robust methods fine-tuned to the mobile
platform are needed to achieve satisfactory verification accuracy. The dataset
will be made available to the research community for promoting additional
research.
Sequence Segmentation Using Joint RNN and Structured Prediction Models
Authors: Yossi Adi, Joseph Keshet, Emily Cibelli, Matthew Goldrick
Date: 2016-10-25 00:00:00
Tasks: Prediction, Segmentation, Structured Prediction

We describe and analyze a simple and effective algorithm for sequence
segmentation applied to speech processing tasks. We propose a neural
architecture that is composed of two modules trained jointly: a recurrent
neural network (RNN) module and a structured prediction model. The RNN outputs
are considered as feature functions to the structured model. The overall model
is trained with a structured loss function which can be designed to the given
segmentation task. We demonstrate the effectiveness of our method by applying
it to two simple tasks commonly used in phonetic studies: word segmentation and
voice onset time segmentation. Results sug- gest the proposed model is superior
to previous methods, ob- taining state-of-the-art results on the tested
datasets.
Generalization Bounds for Weighted Automata
Authors: Borja Balle, Mehryar Mohri
Date: 2016-10-25 00:00:00
Tasks: Generalization Bounds

This paper studies the problem of learning weighted automata from a finite
labeled training sample. We consider several general families of weighted
automata defined in terms of three different measures: the norm of an
automaton's weights, the norm of the function computed by an automaton, or the
norm of the corresponding Hankel matrix. We present new data-dependent
generalization guarantees for learning weighted automata expressed in terms of
the Rademacher complexity of these families. We further present upper bounds on
these Rademacher complexities, which reveal key new data-dependent terms
related to the complexity of learning weighted automata.
Maxmin convolutional neural networks for image classification
Authors: Michael Blot, Matthieu Cord, Nicolas Thome
Date: 2016-10-25 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Open-Ended Question Answering

Convolutional neural networks (CNN) are widely used in computer vision,
especially in image classification. However, the way in which information and
invariance properties are encoded through in deep CNN architectures is still an
open question. In this paper, we propose to modify the standard convo- lutional
block of CNN in order to transfer more information layer after layer while
keeping some invariance within the net- work. Our main idea is to exploit both
positive and negative high scores obtained in the convolution maps. This behav-
ior is obtained by modifying the traditional activation func- tion step before
pooling. We are doubling the maps with spe- cific activations functions, called
MaxMin strategy, in order to achieve our pipeline. Extensive experiments on two
classical datasets, MNIST and CIFAR-10, show that our deep MaxMin convolutional
net outperforms standard CNN.
Directed expected utility networks
Authors: Manuele Leonelli, Jim Q. Smith
Date: 2016-08-02 00:00:00

A variety of statistical graphical models have been defined to represent the
conditional independences underlying a random vector of interest. Similarly,
many different graphs embedding various types of preferential independences, as
for example conditional utility independence and generalized additive
independence, have more recently started to appear. In this paper we define a
new graphical model, called a directed expected utility network, whose edges
depict both probabilistic and utility conditional independences. These embed a
very flexible class of utility models, much larger than those usually conceived
in standard influence diagrams. Our graphical representation, and various
transformations of the original graph into a tree structure, are then used to
guide fast routines for the computation of a decision problem's expected
utilities. We show that our routines generalize those usually utilized in
standard influence diagrams' evaluations under much more restrictive
conditions. We then proceed with the construction of a directed expected
utility network to support decision makers in the domain of household food
security.
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
Authors: Julien Mairal
Date: 2016-05-20 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: image-classification, Image Classification, Image Super-Resolution, Super-Resolution

In this paper, we introduce a new image representation based on a multilayer
kernel machine. Unlike traditional kernel methods where data representation is
decoupled from the prediction task, we learn how to shape the kernel with
supervision. We proceed by first proposing improvements of the
recently-introduced convolutional kernel networks (CKNs) in the context of
unsupervised learning; then, we derive backpropagation rules to take advantage
of labeled training data. The resulting model is a new type of convolutional
neural network, where optimizing the filters at each layer is equivalent to
learning a linear subspace in a reproducing kernel Hilbert space (RKHS). We
show that our method achieves reasonably competitive performance for image
classification on some standard "deep learning" datasets such as CIFAR-10 and
SVHN, and also for image super-resolution, demonstrating the applicability of
our approach to a large variety of image-related tasks.
Improving historical spelling normalization with bi-directional LSTMs and multi-task learning
Authors: Marcel Bollmann, Anders Søgaard
Date: 2016-10-25 00:00:00
Proceeding: COLING 2016 12
Tasks: Multi-Task Learning

Natural-language processing of historical documents is complicated by the
abundance of variant spellings and lack of annotated data. A common approach is
to normalize the spelling of historical words to modern forms. We explore the
suitability of a deep neural network architecture for this task, particularly a
deep bi-LSTM network applied on a character level. Our model compares well to
previously established normalization algorithms when evaluated on a diverse set
of texts from Early New High German. We show that multi-task learning with
additional normalization data can improve our model's performance further.
Record Counting in Historical Handwritten Documents with Convolutional Neural Networks
Authors: Samuele Capobianco, Simone Marinai
Date: 2016-10-24 00:00:00

In this paper, we investigate the use of Convolutional Neural Networks for
counting the number of records in historical handwritten documents. With this
work we demonstrate that training the networks only with synthetic images
allows us to perform a near perfect evaluation of the number of records printed
on historical documents. The experiments have been performed on a benchmark
dataset composed by marriage records and outperform previous results on this
dataset.
How Document Pre-processing affects Keyphrase Extraction Performance
Authors: Florian Boudin, Hugo Mougard, Damien Cram
Date: 2016-10-25 00:00:00
Proceeding: WS 2016 12
Tasks: Articles, Keyphrase Extraction

The SemEval-2010 benchmark dataset has brought renewed attention to the task
of automatic keyphrase extraction. This dataset is made up of scientific
articles that were automatically converted from PDF format to plain text and
thus require careful preprocessing so that irrevelant spans of text do not
negatively affect keyphrase extraction performance. In previous work, a wide
range of document preprocessing techniques were described but their impact on
the overall performance of keyphrase extraction models is still unexplored.
Here, we re-assess the performance of several keyphrase extraction models and
measure their robustness against increasingly sophisticated levels of document
preprocessing.
Backdoors into Heterogeneous Classes of SAT and CSP
Authors: Serge Gaspers, Neeldhara Misra, Sebastian Ordyniak, Stefan Szeider, Stanislav Živný
Date: 2015-09-18 00:00:00

In this paper we extend the classical notion of strong and weak backdoor sets
for SAT and CSP by allowing that different instantiations of the backdoor
variables result in instances that belong to different base classes; the union
of the base classes forms a heterogeneous base class. Backdoor sets to
heterogeneous base classes can be much smaller than backdoor sets to
homogeneous ones, hence they are much more desirable but possibly harder to
find. We draw a detailed complexity landscape for the problem of detecting
strong and weak backdoor sets into heterogeneous base classes for SAT and CSP.
mdBrief - A Fast Online Adaptable, Distorted Binary Descriptor for Real-Time Applications Using Calibrated Wide-Angle Or Fisheye Cameras
Authors: Steffen Urban, Stefan Hinz
Date: 2016-10-25 00:00:00
Tasks: object-detection, Object Detection, Visual Odometry

Fast binary descriptors build the core for many vision based applications
with real-time demands like object detection, Visual Odometry or SLAM. Commonly
it is assumed, that the acquired images and thus the patches extracted around
keypoints originate from a perspective projection ignoring image distortion or
completely different types of projections such as omnidirectional or fisheye.
Usually the deviations from a perfect perspective projection are corrected by
undistortion. Latter, however, introduces severe artifacts if the cameras
field-of-view gets larger. In this paper, we propose a distorted and masked
version of the BRIEF descriptor for calibrated cameras. Instead of correcting
the distortion holistically, we distort the binary tests and thus adapt the
descriptor to different image regions.
A variational approach to path estimation and parameter inference of hidden diffusion processes
Authors: Tobias Sutter, Arnab Ganguly, Heinz Koeppl
Date: 2015-08-03 00:00:00

We consider a hidden Markov model, where the signal process, given by a
diffusion, is only indirectly observed through some noisy measurements. The
article develops a variational method for approximating the hidden states of
the signal process given the full set of observations. This, in particular,
leads to systematic approximations of the smoothing densities of the signal
process. The paper then demonstrates how an efficient inference scheme, based
on this variational approach to the approximation of the hidden states, can be
designed to estimate the unknown parameters of stochastic differential
equations. Two examples at the end illustrate the efficacy and the accuracy of
the presented method.
Deep Activity Recognition Models with Triaxial Accelerometers
Authors: Mohammad Abu Alsheikh, Ahmed Selim, Dusit Niyato, Linda Doyle, Shaowei Lin, Hwee-Pink Tan
Date: 2015-11-15 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Temporal Sequences

Despite the widespread installation of accelerometers in almost all mobile
phones and wearable devices, activity recognition using accelerometers is still
immature due to the poor recognition accuracy of existing recognition methods
and the scarcity of labeled training data. We consider the problem of human
activity recognition using triaxial accelerometers and deep learning paradigms.
This paper shows that deep activity recognition models (a) provide better
recognition accuracy of human activities, (b) avoid the expensive design of
handcrafted features in existing systems, and (c) utilize the massive unlabeled
acceleration samples for unsupervised feature extraction. Moreover, a hybrid
approach of deep learning and hidden Markov models (DL-HMM) is presented for
sequential activity recognition. This hybrid approach integrates the
hierarchical representations of deep activity recognition models with the
stochastic modeling of temporal sequences in the hidden Markov models. We show
substantial recognition improvement on real world datasets over
state-of-the-art methods of human activity recognition using triaxial
accelerometers.
Image Clustering without Ground Truth
Authors: Abhisek Dash, Sujoy Chatterjee, Tripti Prasad, Malay Bhattacharyya
Date: 2016-10-25 00:00:00
Tasks: Clustering, Clustering Ensemble, Image Clustering

Cluster analysis has become one of the most exercised research areas over the
past few decades in computer science. As a consequence, numerous clustering
algorithms have already been developed to find appropriate partitions of a set
of objects. Given multiple such clustering solutions, it is a challenging task
to obtain an ensemble of these solutions. This becomes more challenging when
the ground truth about the number of clusters is unavailable. In this paper, we
introduce a crowd-powered model to collect solutions of image clustering from
the general crowd and pose it as a clustering ensemble problem with variable
number of clusters. The varying number of clusters basically reflects the crowd
workers' perspective toward a particular set of objects. We allow a set of
crowd workers to independently cluster the images as per their perceptions. We
address the problem by finding out centroid of the clusters using an
appropriate distance measure and prioritize the likelihood of similarity of the
individual cluster sets. The effectiveness of the proposed method is
demonstrated by applying it on multiple artificial datasets obtained from
crowd.