pwc-archive/papers-with-abstracts huggingface dataset:
Generalized linear mixing model accounting for endmember variability
Authors: Tales Imbiriba, Ricardo Augusto Borsoi, José Carlos Moreira Bermudez
Date: 2017-10-20 00:00:00
Tasks: model

Endmember variability is an important factor for accurately unveiling vital
information relating the pure materials and their distribution in hyperspectral
images. Recently, the extended linear mixing model (ELMM) has been proposed as
a modification of the linear mixing model (LMM) to consider endmember
variability effects resulting mainly from illumination changes. In this paper,
we further generalize the ELMM leading to a new model (GLMM) to account for
more complex spectral distortions where different wavelength intervals can be
affected unevenly. We also extend the existing methodology to jointly estimate
the variability and the abundances for the GLMM. Simulations with real and
synthetic data show that the unmixing process can benefit from the extra
flexibility introduced by the GLMM.
Scene Parsing with Global Context Embedding
Authors: Wei-Chih Hung, Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu, Ming-Hsuan Yang
Date: 2017-10-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Scene Parsing

We present a scene parsing method that utilizes global context information
based on both the parametric and non- parametric models. Compared to previous
methods that only exploit the local relationship between objects, we train a
context network based on scene similarities to generate feature representations
for global contexts. In addition, these learned features are utilized to
generate global and spatial priors for explicit classes inference. We then
design modules to embed the feature representations and the priors into the
segmentation network as additional global context cues. We show that the
proposed method can eliminate false positives that are not compatible with the
global context representations. Experiments on both the MIT ADE20K and PASCAL
Context datasets show that the proposed method performs favorably against
existing methods.
Solving the "false positives" problem in fraud prediction
Authors: Roy Wedge, James Max Kanter, Santiago Moral Rubio, Sergio Iglesias Perez, Kalyan Veeramachaneni
Date: 2017-10-20 00:00:00
Tasks: Automated Feature Engineering, Feature Engineering, Prediction, valid

In this paper, we present an automated feature engineering based approach to
dramatically reduce false positives in fraud prediction. False positives plague
the fraud prediction industry. It is estimated that only 1 in 5 declared as
fraud are actually fraud and roughly 1 in every 6 customers have had a valid
transaction declined in the past year. To address this problem, we use the Deep
Feature Synthesis algorithm to automatically derive behavioral features based
on the historical data of the card associated with a transaction. We generate
237 features (>100 behavioral patterns) for each transaction, and use a random
forest to learn a classifier. We tested our machine learning model on data from
a large multinational bank and compared it to their existing solution. On an
unseen data of 1.852 million transactions, we were able to reduce the false
positives by 54% and provide a savings of 190K euros. We also assess how to
deploy this solution, and whether it necessitates streaming computation for
real time scoring. We found that our solution can maintain similar benefits
even when historical features are computed once every 7 days.
Low Precision RNNs: Quantizing RNNs Without Losing Accuracy
Authors: Supriya Kapur, Asit Mishra, Debbie Marr
Date: 2017-10-20 00:00:00
Tasks: Quantization

Similar to convolution neural networks, recurrent neural networks (RNNs)
typically suffer from over-parameterization. Quantizing bit-widths of weights
and activations results in runtime efficiency on hardware, yet it often comes
at the cost of reduced accuracy. This paper proposes a quantization approach
that increases model size with bit-width reduction. This approach will allow
networks to perform at their baseline accuracy while still maintaining the
benefits of reduced precision and overall model size reduction.
On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms
Authors: Nicolas Garcia Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso
Date: 2017-10-20 00:00:00

A popular approach to semi-supervised learning proceeds by endowing the input data with a graph structure in order to extract geometric information and incorporate it into a Bayesian framework. We introduce new theory that gives appropriate scalings of graph parameters that provably lead to a well-defined limiting posterior as the size of the unlabeled data set grows. Furthermore, we show that these consistency results have profound algorithmic implications. When consistency holds, carefully designed graph-based Markov chain Monte Carlo algorithms are proved to have a uniform spectral gap, independent of the number of unlabeled inputs. Several numerical experiments corroborate both the statistical consistency and the algorithmic scalability established by the theory.
Verb Pattern: A Probabilistic Semantic Representation on Verbs
Authors: Wanyun Cui, Xiyou Zhou, Hangyu Lin, Yanghua Xiao, Haixun Wang, Seung-won Hwang, Wei Wang
Date: 2017-10-20 00:00:00
Tasks: Specificity

Verbs are important in semantic understanding of natural language.
Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on
verbs' roles. These roles are too coarse to represent verbs' semantics. In this
paper, we introduce verb patterns to represent verbs' semantics, such that each
pattern corresponds to a single semantic of the verb. First we analyze the
principles for verb patterns: generality and specificity. Then we propose a
nonparametric model based on description length. Experimental results prove the
high effectiveness of verb patterns. We further apply verb patterns to
context-aware conceptualization, to show that verb patterns are helpful in
semantic-related tasks.
Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition
Authors: Earnest E. Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar
Date: 2017-10-20 00:00:00
Tasks: All

We present an unconstrained ear recognition framework that outperforms
state-of-the-art systems in different publicly available image databases. To
this end, we developed CNN-based solutions for ear normalization and
description, we used well-known handcrafted descriptors, and we fused learned
and handcrafted features to improve recognition. We designed a two-stage
landmark detector that successfully worked under untrained scenarios. We used
the results generated to perform a geometric image normalization that boosted
the performance of all evaluated descriptors. Our CNN descriptor outperformed
other CNN-based works in the literature, specially in more difficult scenarios.
The fusion of learned and handcrafted matchers appears to be complementary as
it achieved the best performance in all experiments. The obtained results
outperformed all other reported results for the UERC challenge, which contains
the most difficult database nowadays.
Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework
Authors: Andreas Stöckel, Aaron R. Voelker, Chris Eliasmith
Date: 2017-10-20 00:00:00
Tasks: Translation

The mathematical model underlying the Neural Engineering Framework (NEF)
expresses neuronal input as a linear combination of synaptic currents. However,
in biology, synapses are not perfect current sources and are thus nonlinear.
Detailed synapse models are based on channel conductances instead of currents,
which require independent handling of excitatory and inhibitory synapses. This,
in particular, significantly affects the influence of inhibitory signals on the
neuronal dynamics. In this technical report we first summarize the relevant
portions of the NEF and conductance-based synapse models. We then discuss a
na\"ive translation between populations of LIF neurons with current- and
conductance-based synapses based on an estimation of an average membrane
potential. Experiments show that this simple approach works relatively well for
feed-forward communication channels, yet performance degrades for NEF networks
describing more complex dynamics, such as integration.
Sparse Coding and Autoencoders
Authors: Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu, Tejaswini Ganapathy, Ashish Arora, Sang Chin, Trac. D. Tran
Date: 2017-08-12 00:00:00
Tasks: Dictionary Learning

In "Dictionary Learning" one tries to recover incoherent matrices $A^* \in
\mathbb{R}^{n \times h}$ (typically overcomplete and whose columns are assumed
to be normalized) and sparse vectors $x^* \in \mathbb{R}^h$ with a small
support of size $h^p$ for some $0 <p < 1$ while having access to observations
$y \in \mathbb{R}^n$ where $y = A^*x^*$. In this work we undertake a rigorous
analysis of whether gradient descent on the squared loss of an autoencoder can
solve the dictionary learning problem. The "Autoencoder" architecture we
consider is a $\mathbb{R}^n \rightarrow \mathbb{R}^n$ mapping with a single
ReLU activation layer of size $h$.
  Under very mild distributional assumptions on $x^*$, we prove that the norm
of the expected gradient of the standard squared loss function is
asymptotically (in sparse code dimension) negligible for all points in a small
neighborhood of $A^*$. This is supported with experimental evidence using
synthetic data. We also conduct experiments to suggest that $A^*$ is a local
minimum. Along the way we prove that a layer of ReLU gates can be set up to
automatically recover the support of the sparse codes. This property holds
independent of the loss function. We believe that it could be of independent
interest.
Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow
Authors: Andrii Riazanov, Yury Maximov, Michael Chertkov
Date: 2017-10-20 00:00:00
Tasks: Model Optimization

Belief Propagation algorithms are instruments used broadly to solve graphical
model optimization and statistical inference problems. In the general case of a
loopy Graphical Model, Belief Propagation is a heuristic which is quite
successful in practice, even though its empirical success, typically, lacks
theoretical guarantees. This paper extends the short list of special cases
where correctness and/or convergence of a Belief Propagation algorithm is
proven. We generalize formulation of Min-Sum Network Flow problem by relaxing
the flow conservation (balance) constraints and then proving that the Belief
Propagation algorithm converges to the exact result.
What Automated Planning can do for Business Process Management
Authors: Andrea Marrella
Date: 2017-09-29 00:00:00
Tasks: Management

Business Process Management (BPM) is a central element of today
organizations. Despite over the years its main focus has been the support of
processes in highly controlled domains, nowadays many domains of interest to
the BPM community are characterized by ever-changing requirements,
unpredictable environments and increasing amounts of data that influence the
execution of process instances. Under such dynamic conditions, BPM systems must
increase their level of automation to provide the reactivity and flexibility
necessary for process management. On the other hand, the Artificial
Intelligence (AI) community has concentrated its efforts on investigating
dynamic domains that involve active control of computational entities and
physical devices (e.g., robots, software agents, etc.). In this context,
Automated Planning, which is one of the oldest areas in AI, is conceived as a
model-based approach to synthesize autonomous behaviours in automated way from
a model. In this paper, we discuss how automated planning techniques can be
leveraged to enable new levels of automation and support for business
processing, and we show some concrete examples of their successful application
to the different stages of the BPM life cycle.
SEGCloud: Semantic Segmentation of 3D Point Clouds
Authors: Lyne P. Tchapmi, Christopher B. Choy, Iro Armeni, JunYoung Gwak, Silvio Savarese
Date: 2017-10-20 00:00:00
Tasks: Semantic Segmentation

3D semantic scene labeling is fundamental to agents operating in the real
world. In particular, labeling raw 3D point sets from sensors provides
fine-grained semantics. Recent works leverage the capabilities of Neural
Networks (NNs), but are limited to coarse voxel predictions and do not
explicitly enforce global consistency. We present SEGCloud, an end-to-end
framework to obtain 3D point-level segmentation that combines the advantages of
NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields
(FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are
transferred back to the raw 3D points via trilinear interpolation. Then the
FC-CRF enforces global consistency and provides fine-grained semantics on the
points. We implement the latter as a differentiable Recurrent NN to allow joint
optimization. We evaluate the framework on two indoor and two outdoor 3D
datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance
comparable or superior to the state-of-the-art on all datasets.
Real-time Convolutional Neural Networks for Emotion and Gender Classification
Authors: Octavio Arriaga, Matias Valdenegro-Toro, Paul Plöger
Date: 2017-10-20 00:00:00
Tasks: Emotion Classification, Face Detection, Gender Classification, Gender Prediction, General Classification

In this paper we propose an implement a general convolutional neural network
(CNN) building framework for designing real-time CNNs. We validate our models
by creating a real-time vision system which accomplishes the tasks of face
detection, gender classification and emotion classification simultaneously in
one blended step using our proposed CNN architecture. After presenting the
details of the training procedure setup we proceed to evaluate on standard
benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66%
in the FER-2013 emotion dataset. Along with this we also introduced the very
recent real-time enabled guided back-propagation visualization technique.
Guided back-propagation uncovers the dynamics of the weight changes and
evaluates the learned features. We argue that the careful implementation of
modern CNN architectures, the use of the current regularization methods and the
visualization of previously hidden features are necessary in order to reduce
the gap between slow performances and real-time architectures. Our system has
been validated by its deployment on a Care-O-bot 3 robot used during
RoboCup@Home competitions. All our code, demos and pre-trained architectures
have been released under an open-source license in our public repository.
Characterization of Gradient Dominance and Regularity Conditions for Neural Networks
Authors: Yi Zhou, Yingbin Liang
Date: 2017-10-18 00:00:00

The past decade has witnessed a successful application of deep learning to
solving many challenging problems in machine learning and artificial
intelligence. However, the loss functions of deep neural networks (especially
nonlinear networks) are still far from being well understood from a theoretical
aspect. In this paper, we enrich the current understanding of the landscape of
the square loss functions for three types of neural networks. Specifically,
when the parameter matrices are square, we provide an explicit characterization
of the global minimizers for linear networks, linear residual networks, and
nonlinear networks with one hidden layer. Then, we establish two quadratic
types of landscape properties for the square loss of these neural networks,
i.e., the gradient dominance condition within the neighborhood of their full
rank global minimizers, and the regularity condition along certain directions
and within the neighborhood of their global minimizers. These two landscape
properties are desirable for the optimization around the global minimizers of
the loss function for these neural networks.
Spoken Language Biomarkers for Detecting Cognitive Impairment
Authors: Tuka Alhanai, Rhoda Au, James Glass
Date: 2017-10-20 00:00:00

In this study we developed an automated system that evaluates speech and
language features from audio recordings of neuropsychological examinations of
92 subjects in the Framingham Heart Study. A total of 265 features were used in
an elastic-net regularized binomial logistic regression model to classify the
presence of cognitive impairment, and to select the most predictive features.
We compared performance with a demographic model from 6,258 subjects in the
greater study cohort (0.79 AUC), and found that a system that incorporated both
audio and text features performed the best (0.92 AUC), with a True Positive
Rate of 29% (at 0% False Positive Rate) and a good model fit (Hosmer-Lemeshow
test > 0.05). We also found that decreasing pitch and jitter, shorter segments
of speech, and responses phrased as questions were positively associated with
cognitive impairment.
$k$-means as a variational EM approximation of Gaussian mixture models
Authors: Jörg Lücke, Dennis Forster
Date: 2017-04-16 00:00:00
Tasks: Clustering

We show that $k$-means (Lloyd's algorithm) is obtained as a special case when truncated variational EM approximations are applied to Gaussian Mixture Models (GMM) with isotropic Gaussians. In contrast to the standard way to relate $k$-means and GMMs, the provided derivation shows that it is not required to consider Gaussians with small variances or the limit case of zero variances. There are a number of consequences that directly follow from our approach: (A) $k$-means can be shown to increase a free energy associated with truncated distributions and this free energy can directly be reformulated in terms of the $k$-means objective; (B) $k$-means generalizations can directly be derived by considering the 2nd closest, 3rd closest etc. cluster in addition to just the closest one; and (C) the embedding of $k$-means into a free energy framework allows for theoretical interpretations of other $k$-means generalizations in the literature. In general, truncated variational EM provides a natural and rigorous quantitative link between $k$-means-like clustering and GMM clustering algorithms which may be very relevant for future theoretical and empirical studies.
Learning compressed representations of blood samples time series with missing data
Authors: Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen
Date: 2017-10-20 00:00:00
Tasks: General Classification, Missing Values, Time Series, Time Series Analysis

Clinical measurements collected over time are naturally represented as
multivariate time series (MTS), which often contain missing data. An
autoencoder can learn low dimensional vectorial representations of MTS that
preserve important data characteristics, but cannot deal explicitly with
missing data. In this work, we propose a new framework that combines an
autoencoder with the Time series Cluster Kernel (TCK), a kernel that accounts
for missingness patterns in MTS. Via kernel alignment, we incorporate TCK in
the autoencoder to improve the learned representations in presence of missing
data. We consider a classification problem of MTS with missing values,
representing blood samples of patients with surgical site infection. With our
approach, rather than with a standard autoencoder, we learn representations in
low dimensions that can be classified better.
HDR image reconstruction from a single exposure using deep CNNs
Authors: Gabriel Eilertsen, Joel Kronander, Gyorgy Denes, Rafał K. Mantiuk, Jonas Unger
Date: 2017-10-20 00:00:00
Tasks: HDR Reconstruction, Image Reconstruction, Inverse-Tone-Mapping

Camera sensors can only capture a limited range of luminance simultaneously,
and in order to create high dynamic range (HDR) images a set of different
exposures are typically combined. In this paper we address the problem of
predicting information that have been lost in saturated image areas, in order
to enable HDR reconstruction from a single exposure. We show that this problem
is well-suited for deep learning algorithms, and propose a deep convolutional
neural network (CNN) that is specifically designed taking into account the
challenges in predicting HDR values. To train the CNN we gather a large dataset
of HDR images, which we augment by simulating sensor saturation for a range of
cameras. To further boost robustness, we pre-train the CNN on a simulated HDR
dataset created from a subset of the MIT Places database. We demonstrate that
our approach can reconstruct high-resolution visually convincing HDR results in
a wide range of situations, and that it generalizes well to reconstruction of
images captured with arbitrary and low-end cameras that use unknown camera
response functions and post-processing. Furthermore, we compare to existing
methods for HDR expansion, and show high quality results also for image based
lighting. Finally, we evaluate the results in a subjective experiment performed
on an HDR display. This shows that the reconstructed HDR images are visually
convincing, with large improvements as compared to existing methods.
Anticipating Daily Intention using On-Wrist Motion Triggered Sensing
Authors: Tz-Ying Wu, Ting-An Chien, Cheng-Sheng Chan, Chan-Wei Hu, Min Sun
Date: 2017-10-20 00:00:00
Proceeding: ICCV 2017 10

Anticipating human intention by observing one's actions has many
applications. For instance, picking up a cellphone, then a charger (actions)
implies that one wants to charge the cellphone (intention). By anticipating the
intention, an intelligent system can guide the user to the closest power
outlet. We propose an on-wrist motion triggered sensing system for anticipating
daily intentions, where the on-wrist sensors help us to persistently observe
one's actions. The core of the system is a novel Recurrent Neural Network (RNN)
and Policy Network (PN), where the RNN encodes visual and motion observation to
anticipate intention, and the PN parsimoniously triggers the process of visual
observation to reduce computation requirement. We jointly trained the whole
network using policy gradient and cross-entropy loss. To evaluate, we collect
the first daily "intention" dataset consisting of 2379 videos with 34
intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%,
97.56% accuracy on three users while processing only 29% of the visual
observation on average.
A retrieval-based dialogue system utilizing utterance and context embeddings
Authors: Alexander Bartl, Gerasimos Spanakis
Date: 2017-10-16 00:00:00
Tasks: Decoder, Retrieval

Finding semantically rich and computer-understandable representations for
textual dialogues, utterances and words is crucial for dialogue systems (or
conversational agents), as their performance mostly depends on understanding
the context of conversations. Recent research aims at finding distributed
vector representations (embeddings) for words, such that semantically similar
words are relatively close within the vector-space. Encoding the "meaning" of
text into vectors is a current trend, and text can range from words, phrases
and documents to actual human-to-human conversations. In recent research
approaches, responses have been generated utilizing a decoder architecture,
given the vector representation of the current conversation. In this paper, the
utilization of embeddings for answer retrieval is explored by using
Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor
(ANN) model, to find similar conversations in a corpus and rank possible
candidates. Experimental results on the well-known Ubuntu Corpus (in English)
and a customer service chat dataset (in Dutch) show that, in combination with a
candidate selection method, retrieval-based approaches outperform generative
ones and reveal promising future research directions towards the usability of
such a system.