pwc-archive/papers-with-abstracts huggingface dataset:
A Natural Language Query Interface for Searching Personal Information on Smartwatches
Authors: Reza Rawassizadeh, Chelsea Dobbins, Manouchehr Nourizadeh, Zahra Ghamchili, Michael Pazzani
Date: 2016-11-22 00:00:00
Tasks: Natural Language Queries

Currently, personal assistant systems, run on smartphones and use natural
language interfaces. However, these systems rely mostly on the web for finding
information. Mobile and wearable devices can collect an enormous amount of
contextual personal data such as sleep and physical activities. These
information objects and their applications are known as quantified-self, mobile
health or personal informatics, and they can be used to provide a deeper
insight into our behavior. To our knowledge, existing personal assistant
systems do not support all types of quantified-self queries. In response to
this, we have undertaken a user study to analyze a set of "textual
questions/queries" that users have used to search their quantified-self or
mobile health data. Through analyzing these questions, we have constructed a
light-weight natural language based query interface, including a text parser
algorithm and a user interface, to process the users' queries that have been
used for searching quantified-self information. This query interface has been
designed to operate on small devices, i.e. smartwatches, as well as augmenting
the personal assistant systems by allowing them to process end users' natural
language queries about their quantified-self data.
A New Approach to Laplacian Solvers and Flow Problems
Authors: Patrick Rebeschini, Sekhar Tatikonda
Date: 2016-11-22 00:00:00

This paper investigates the behavior of the Min-Sum message passing scheme to
solve systems of linear equations in the Laplacian matrices of graphs and to
compute electric flows. Voltage and flow problems involve the minimization of
quadratic functions and are fundamental primitives that arise in several
domains. Algorithms that have been proposed are typically centralized and
involve multiple graph-theoretic constructions or sampling mechanisms that make
them difficult to implement and analyze. On the other hand, message passing
routines are distributed, simple, and easy to implement. In this paper we
establish a framework to analyze Min-Sum to solve voltage and flow problems. We
characterize the error committed by the algorithm on general weighted graphs in
terms of hitting times of random walks defined on the computation trees that
support the operations of the algorithms with time. For $d$-regular graphs with
equal weights, we show that the convergence of the algorithms is controlled by
the total variation distance between the distributions of non-backtracking
random walks defined on the original graph that start from neighboring nodes.
The framework that we introduce extends the analysis of Min-Sum to settings
where the contraction arguments previously considered in the literature (based
on the assumption of walk summability or scaled diagonal dominance) can not be
used, possibly in the presence of constraints.
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent
Authors: Qinqing Zheng, John Lafferty
Date: 2016-05-23 00:00:00
Tasks: Matrix Completion

We address the rectangular matrix completion problem by lifting the unknown
matrix to a positive semidefinite matrix in higher dimension, and optimizing a
nonconvex objective over the semidefinite factor using a simple gradient
descent scheme. With $O( \mu r^2 \kappa^2 n \max(\mu, \log n))$ random
observations of a $n_1 \times n_2$ $\mu$-incoherent matrix of rank $r$ and
condition number $\kappa$, where $n = \max(n_1, n_2)$, the algorithm linearly
converges to the global optimum with high probability.
Cascaded Neural Networks with Selective Classifiers and its evaluation using Lung X-ray CT Images
Authors: Masaharu Sakamoto, Hiroki Nakano
Date: 2016-11-22 00:00:00
Tasks: Lung Nodule Detection, Sensitivity

Lung nodule detection is a class imbalanced problem because nodules are found
with much lower frequency than non-nodules. In the class imbalanced problem,
conventional classifiers tend to be overwhelmed by the majority class and
ignore the minority class. We therefore propose cascaded convolutional neural
networks to cope with the class imbalanced problem. In the proposed approach,
cascaded convolutional neural networks that perform as selective classifiers
filter out obvious non-nodules. Successively, a convolutional neural network
trained with a balanced data set calculates nodule probabilities. The proposed
method achieved the detection sensitivity of 85.3% and 90.7% at 1 and 4 false
positives per scan in FROC curve, respectively.
Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization
Authors: Vitaly Feldman, Cristobal Guzman, Santosh Vempala
Date: 2015-12-30 00:00:00
Tasks: BIG-bench Machine Learning

Stochastic convex optimization, where the objective is the expectation of a
random convex function, is an important and widely used method with numerous
applications in machine learning, statistics, operations research and other
areas. We study the complexity of stochastic convex optimization given only
statistical query (SQ) access to the objective function. We show that
well-known and popular first-order iterative methods can be implemented using
only statistical queries. For many cases of interest we derive nearly matching
upper and lower bounds on the estimation (sample) complexity including linear
optimization in the most general setting. We then present several consequences
for machine learning, differential privacy and proving concrete lower bounds on
the power of convex optimization based methods.
  The key ingredient of our work is SQ algorithms and lower bounds for
estimating the mean vector of a distribution over vectors supported on a convex
body in $\mathbb{R}^d$. This natural problem has not been previously studied
and we show that our solutions can be used to get substantially improved SQ
versions of Perceptron and other online algorithms for learning halfspaces.
Max-Margin Deep Generative Models for (Semi-)Supervised Learning
Authors: Chongxuan Li, Jun Zhu, Bo Zhang
Date: 2016-11-22 00:00:00
Tasks: Missing Labels

Deep generative models (DGMs) are effective on learning multilayered
representations of complex data and performing inference of input data by
exploring the generative ability. However, it is relatively insufficient to
empower the discriminative ability of DGMs on making accurate predictions. This
paper presents max-margin deep generative models (mmDGMs) and a
class-conditional variant (mmDCGMs), which explore the strongly discriminative
principle of max-margin learning to improve the predictive performance of DGMs
in both supervised and semi-supervised learning, while retaining the generative
capability. In semi-supervised learning, we use the predictions of a max-margin
classifier as the missing labels instead of performing full posterior inference
for efficiency; we also introduce additional max-margin and label-balance
regularization terms of unlabeled data for effectiveness. We develop an
efficient doubly stochastic subgradient algorithm for the piecewise linear
objectives in different settings. Empirical results on various datasets
demonstrate that: (1) max-margin learning can significantly improve the
prediction performance of DGMs and meanwhile retain the generative ability; (2)
in supervised learning, mmDGMs are competitive to the best fully discriminative
networks when employing convolutional neural networks as the generative and
recognition models; and (3) in semi-supervised learning, mmDCGMs can perform
efficient inference and achieve state-of-the-art classification results on
several benchmarks.
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Authors: Sarah Tan, Matvey Soloviev, Giles Hooker, Martin T. Wells
Date: 2016-11-22 00:00:00
Tasks: Prototype Selection

Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative points for each class -- prototypes. We introduce a new distance for Gradient Boosted Tree models, and propose new, adaptive prototype selection methods with theoretical guarantees, with the flexibility to choose a different number of prototypes in each class. We demonstrate our methods on random forests and gradient boosted trees, showing that the prototypes can perform as well as or even better than the original tree ensemble when used as a nearest-prototype classifier. In a user study, humans were better at predicting the output of a tree ensemble classifier when using prototypes than when using Shapley values, a popular feature attribution method. Hence, prototypes present a viable alternative to feature-based explanations for tree ensembles.
Convolutional Gated Recurrent Networks for Video Segmentation
Authors: Mennatullah Siam, Sepehr Valipour, Martin Jagersand, Nilanjan Ray
Date: 2016-11-16 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

Semantic segmentation has recently witnessed major progress, where fully
convolutional neural networks have shown to perform well. However, most of the
previous work focused on improving single image segmentation. To our knowledge,
no prior work has made use of temporal video information in a recurrent
network. In this paper, we introduce a novel approach to implicitly utilize
temporal data in videos for online semantic segmentation. The method relies on
a fully convolutional network that is embedded into a gated recurrent
architecture. This design receives a sequence of consecutive video frames and
outputs the segmentation of the last frame. Convolutional gated recurrent
networks are used for the recurrent part to preserve spatial connectivities in
the image. Our proposed method can be applied in both online and batch
segmentation. This architecture is tested for both binary and semantic video
segmentation tasks. Experiments are conducted on the recent benchmarks in
SegTrack V2, Davis, CityScapes, and Synthia. Using recurrent fully
convolutional networks improved the baseline network performance in all of our
experiments. Namely, 5% and 3% improvement of F-measure in SegTrack2 and Davis
respectively, 5.7% improvement in mean IoU in Synthia and 3.5% improvement in
categorical mean IoU in CityScapes. The performance of the RFCN network depends
on its baseline fully convolutional network. Thus RFCN architecture can be seen
as a method to improve its baseline segmentation network by exploiting
spatiotemporal information in videos.
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Authors: Sebastian Pölsterl, Nassir Navab, Amin Katouzian
Date: 2016-11-21 00:00:00
Tasks: Survival Analysis

Survival analysis is a fundamental tool in medical research to identify
predictors of adverse events and develop systems for clinical decision support.
In order to leverage large amounts of patient data, efficient optimisation
routines are paramount. We propose an efficient training algorithm for the
kernel survival support vector machine (SSVM). We directly optimise the primal
objective function and employ truncated Newton optimisation and order statistic
trees to significantly lower computational costs compared to previous training
algorithms, which require $O(n^4)$ space and $O(p n^6)$ time for datasets with
$n$ samples and $p$ features. Our results demonstrate that our proposed
optimisation scheme allows analysing data of a much larger scale with no loss
in prediction performance. Experiments on synthetic and 5 real-world datasets
show that our technique outperforms existing kernel SSVM formulations if the
amount of right censoring is high ($\geq85\%$), and performs comparably
otherwise.
Context Encoders: Feature Learning by Inpainting
Authors: Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros
Date: 2016-04-25 00:00:00
Proceeding: CVPR 2016 6

We present an unsupervised visual feature learning algorithm driven by
context-based pixel prediction. By analogy with auto-encoders, we propose
Context Encoders -- a convolutional neural network trained to generate the
contents of an arbitrary image region conditioned on its surroundings. In order
to succeed at this task, context encoders need to both understand the content
of the entire image, as well as produce a plausible hypothesis for the missing
part(s). When training context encoders, we have experimented with both a
standard pixel-wise reconstruction loss, as well as a reconstruction plus an
adversarial loss. The latter produces much sharper results because it can
better handle multiple modes in the output. We found that a context encoder
learns a representation that captures not just appearance but also the
semantics of visual structures. We quantitatively demonstrate the effectiveness
of our learned features for CNN pre-training on classification, detection, and
segmentation tasks. Furthermore, context encoders can be used for semantic
inpainting tasks, either stand-alone or as initialization for non-parametric
methods.
Quasi-Recurrent Neural Networks
Authors: James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher
Date: 2016-11-05 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentiment Analysis, Sentiment Classification, Setting-1/4, Translation

Recurrent neural networks are a powerful tool for modeling sequential data,
but the dependence of each timestep's computation on the previous timestep's
output limits parallelism and makes RNNs unwieldy for very long sequences. We
introduce quasi-recurrent neural networks (QRNNs), an approach to neural
sequence modeling that alternates convolutional layers, which apply in parallel
across timesteps, and a minimalist recurrent pooling function that applies in
parallel across channels. Despite lacking trainable recurrent layers, stacked
QRNNs have better predictive accuracy than stacked LSTMs of the same hidden
size. Due to their increased parallelism, they are up to 16 times faster at
train and test time. Experiments on language modeling, sentiment
classification, and character-level neural machine translation demonstrate
these advantages and underline the viability of QRNNs as a basic building block
for a variety of sequence tasks.
Revenue Forecasting for Enterprise Products
Authors: Amita Gajewar, Gagan Bansal
Date: 2016-11-21 00:00:00

For any business, planning is a continuous process, and typically
business-owners focus on making both long-term planning aligned with a
particular strategy as well as short-term planning that accommodates the
dynamic market situations. An ability to perform an accurate financial forecast
is crucial for effective planning. In this paper, we focus on providing an
intelligent and efficient solution that will help in forecasting revenue using
machine learning algorithms. We experiment with three different revenue
forecasting models, and here we provide detailed insights into the methodology
and their relative performance measured on real finance data. As a real-world
application of our models, we partner with Microsoft's Finance organization
(department that reports Microsoft's finances) to provide them a guidance on
the projected revenue for upcoming quarters.
Coherent Dialogue with Attention-based Language Models
Authors: Hongyuan Mei, Mohit Bansal, Matthew R. Walter
Date: 2016-11-21 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Reranking

We model coherent conversation continuation via RNN-based dialogue models
equipped with a dynamic attention mechanism. Our attention-RNN language model
dynamically increases the scope of attention on the history as the conversation
continues, as opposed to standard attention (or alignment) models with a fixed
input scope in a sequence-to-sequence model. This allows each generated word to
be associated with the most relevant words in its corresponding conversation
history. We evaluate the model on two popular dialogue datasets, the
open-domain MovieTriples dataset and the closed-domain Ubuntu Troubleshoot
dataset, and achieve significant improvements over the state-of-the-art and
baselines on several metrics, including complementary diversity-based metrics,
human evaluation, and qualitative visualizations. We also show that a vanilla
RNN with dynamic attention outperforms more complex memory models (e.g., LSTM
and GRU) by allowing for flexible, long-distance memory. We promote further
coherence via topic modeling-based reranking.
Spatial contrasting for deep unsupervised learning
Authors: Elad Hoffer, Itay Hubara, Nir Ailon
Date: 2016-11-21 00:00:00

Convolutional networks have marked their place over the last few years as the
best performing model for various visual tasks. They are, however, most suited
for supervised learning from large amounts of labeled data. Previous attempts
have been made to use unlabeled data to improve model performance by applying
unsupervised techniques. These attempts require different architectures and
training methods. In this work we present a novel approach for unsupervised
training of Convolutional networks that is based on contrasting between spatial
regions within images. This criterion can be employed within conventional
neural networks and trained using standard techniques such as SGD and
back-propagation, thus complementing supervised methods.
Neuron's Eye View: Inferring Features of Complex Stimuli from Neural Responses
Authors: Xin, Chen, Jeffrey M Beck, John M. Pearson
Date: 2015-12-04 00:00:00
Tasks: Bayesian Inference, State Space Models

Experiments that study neural encoding of stimuli at the level of individual
neurons typically choose a small set of features present in the world ---
contrast and luminance for vision, pitch and intensity for sound --- and
assemble a stimulus set that systematically varies along these dimensions.
Subsequent analysis of neural responses to these stimuli typically focuses on
regression models, with experimenter-controlled features as predictors and
spike counts or firing rates as responses. Unfortunately, this approach
requires knowledge in advance about the relevant features coded by a given
population of neurons. For domains as complex as social interaction or natural
movement, however, the relevant feature space is poorly understood, and an
arbitrary \emph{a priori} choice of features may give rise to confirmation
bias. Here, we present a Bayesian model for exploratory data analysis that is
capable of automatically identifying the features present in unstructured
stimuli based solely on neuronal responses. Our approach is unique within the
class of latent state space models of neural activity in that it assumes that
firing rates of neurons are sensitive to multiple discrete time-varying
features tied to the \emph{stimulus}, each of which has Markov (or semi-Markov)
dynamics. That is, we are modeling neural activity as driven by multiple
simultaneous stimulus features rather than intrinsic neural dynamics. We derive
a fast variational Bayesian inference algorithm and show that it correctly
recovers hidden features in synthetic data, as well as ground-truth stimulus
features in a prototypical neural dataset. To demonstrate the utility of the
algorithm, we also apply it to cluster neural responses and demonstrate
successful recovery of features corresponding to monkeys and faces in the image
set.
Robust end-to-end deep audiovisual speech recognition
Authors: Ramon Sanabria, Florian Metze, Fernando de la Torre
Date: 2016-11-21 00:00:00
Tasks: speech-recognition, Speech Recognition

Speech is one of the most effective ways of communication among humans. Even
though audio is the most common way of transmitting speech, very important
information can be found in other modalities, such as vision. Vision is
particularly useful when the acoustic signal is corrupted. Multi-modal speech
recognition however has not yet found wide-spread use, mostly because the
temporal alignment and fusion of the different information sources is
challenging.
  This paper presents an end-to-end audiovisual speech recognizer (AVSR), based
on recurrent neural networks (RNN) with a connectionist temporal classification
(CTC) loss function. CTC creates sparse "peaky" output activations, and we
analyze the differences in the alignments of output targets (phonemes or
visemes) between audio-only, video-only, and audio-visual feature
representations. We present the first such experiments on the large vocabulary
IBM ViaVoice database, which outperform previously published approaches on
phone accuracy in clean and noisy conditions.
Multiple-View Spectral Clustering for Group-wise Functional Community Detection
Authors: Nathan D. Cahill, Harmeet Singh, Chao Zhang, Daryl A. Corcoran, Alison M. Prengaman, Paul S. Wenger, John F. Hamilton, Peter Bajorski, Andrew M. Michael
Date: 2016-11-21 00:00:00
Tasks: Clustering, Community Detection, Functional Connectivity

Functional connectivity analysis yields powerful insights into our
understanding of the human brain. Group-wise functional community detection
aims to partition the brain into clusters, or communities, in which functional
activity is inter-regionally correlated in a common manner across a group of
subjects. In this article, we show how to use multiple-view spectral clustering
to perform group-wise functional community detection. In a series of
experiments on 291 subjects from the Human Connectome Project, we compare three
versions of multiple-view spectral clustering: MVSC (uniform weights), MVSCW
(weights based on subject-specific embedding quality), and AASC (weights
optimized along with the embedding) with the competing technique of Joint
Diagonalization of Laplacians (JDL). Results show that multiple-view spectral
clustering not only yields group-wise functional communities that are more
consistent than JDL when using randomly selected subsets of individual brains,
but it is several orders of magnitude faster than JDL.
RhoanaNet Pipeline: Dense Automatic Neural Annotation
Authors: Seymour Knowles-Barley, Verena Kaynig, Thouis Ray Jones, Alyssa Wilson, Joshua Morgan, Dongil Lee, Daniel Berger, Narayanan Kasthuri, Jeff W. Lichtman, Hanspeter Pfister
Date: 2016-11-21 00:00:00
Tasks: Segmentation

Reconstructing a synaptic wiring diagram, or connectome, from electron
microscopy (EM) images of brain tissue currently requires many hours of manual
annotation or proofreading (Kasthuri and Lichtman, 2010; Lichtman and Sanes,
2008; Seung, 2009). The desire to reconstruct ever larger and more complex
networks has pushed the collection of ever larger EM datasets. A cubic
millimeter of raw imaging data would take up 1 PB of storage and present an
annotation project that would be impractical without relying heavily on
automatic segmentation methods. The RhoanaNet image processing pipeline was
developed to automatically segment large volumes of EM data and ease the burden
of manual proofreading and annotation. Based on (Kaynig et al., 2015), we
updated every stage of the software pipeline to provide better throughput
performance and higher quality segmentation results. We used state of the art
deep learning techniques to generate improved membrane probability maps, and
Gala (Nunez-Iglesias et al., 2014) was used to agglomerate 2D segments into 3D
objects.
  We applied the RhoanaNet pipeline to four densely annotated EM datasets, two
from mouse cortex, one from cerebellum and one from mouse lateral geniculate
nucleus (LGN). All training and test data is made available for benchmark
comparisons. The best segmentation results obtained gave
$V^\text{Info}_\text{F-score}$ scores of 0.9054 and 09182 for the cortex
datasets, 0.9438 for LGN, and 0.9150 for Cerebellum.
  The RhoanaNet pipeline is open source software. All source code, training
data, test data, and annotations for all four benchmark datasets are available
at www.rhoana.org.