pwc-archive/papers-with-abstracts huggingface dataset:
Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction
Authors: Benjamin Hepp, Matthias Nießner, Otmar Hilliges
Date: 2017-05-25 00:00:00

We introduce a new method that efficiently computes a set of viewpoints and
trajectories for high-quality 3D reconstructions in outdoor environments. Our
goal is to automatically explore an unknown area, and obtain a complete 3D scan
of a region of interest (e.g., a large building). Images from a commodity RGB
camera, mounted on an autonomously navigated quadcopter, are fed into a
multi-view stereo reconstruction pipeline that produces high-quality results
but is computationally expensive. In this setting, the scanning result is
constrained by the restricted flight time of quadcopters. To this end, we
introduce a novel optimization strategy that respects these constraints by
maximizing the information gain from sparsely-sampled view points while
limiting the total travel distance of the quadcopter. At the core of our method
lies a hierarchical volumetric representation that allows the algorithm to
distinguish between unknown, free, and occupied space. Furthermore, our
information gain based formulation leverages this representation to handle
occlusions in an efficient manner. In addition to the surface geometry, we
utilize the free-space information to avoid obstacles and determine
collision-free flight paths. Our tool can be used to specify the region of
interest and to plan trajectories. We demonstrate our method by obtaining a
number of compelling 3D reconstructions, and provide a thorough quantitative
evaluation showing improvement over previous state-of-the-art and regular
patterns.
Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning
Authors: Wufeng Xue, Ali Islam, Mousumi Bhaduri, Shuo Li
Date: 2017-05-25 00:00:00
Tasks: Feature Engineering, regression

Cardiac indices estimation is of great importance during identification and
diagnosis of cardiac disease in clinical routine. However, estimation of
multitype cardiac indices with consistently reliable and high accuracy is still
a great challenge due to the high variability of cardiac structures and
complexity of temporal dynamics in cardiac MR sequences. While efforts have
been devoted into cardiac volumes estimation through feature engineering
followed by a independent regression model, these methods suffer from the
vulnerable feature representation and incompatible regression model. In this
paper, we propose a semi-automated method for multitype cardiac indices
estimation. After manual labelling of two landmarks for ROI cropping, an
integrated deep neural network Indices-Net is designed to jointly learn the
representation and regression models. It comprises two tightly-coupled
networks: a deep convolution autoencoder (DCAE) for cardiac image
representation, and a multiple output convolution neural network (CNN) for
indices regression. Joint learning of the two networks effectively enhances the
expressiveness of image representation with respect to cardiac indices, and the
compatibility between image representation and indices regression, thus leading
to accurate and reliable estimations for all the cardiac indices.
  When applied with five-fold cross validation on MR images of 145 subjects,
Indices-Net achieves consistently low estimation error for LV wall thicknesses
(1.44$\pm$0.71mm) and areas of cavity and myocardium (204$\pm$133mm$^2$). It
outperforms, with significant error reductions, segmentation method (55.1% and
17.4%) and two-phase direct volume-only methods (12.7% and 14.6%) for wall
thicknesses and areas, respectively. These advantages endow the proposed method
a great potential in clinical cardiac function assessment.
Latent Geometry and Memorization in Generative Models
Authors: Matt Feiszli
Date: 2017-05-25 00:00:00
Tasks: Memorization

It can be difficult to tell whether a trained generative model has learned to
generate novel examples or has simply memorized a specific set of outputs. In
published work, it is common to attempt to address this visually, for example
by displaying a generated example and its nearest neighbor(s) in the training
set (in, for example, the L2 metric). As any generative model induces a
probability density on its output domain, we propose studying this density
directly. We first study the geometry of the latent representation and
generator, relate this to the output density, and then develop techniques to
compute and inspect the output density. As an application, we demonstrate that
"memorization" tends to a density made of delta functions concentrated on the
memorized examples. We note that without first understanding the geometry, the
measurement would be essentially impossible to make.
Neural Models for Documents with Metadata
Authors: Dallas Card, Chenhao Tan, Noah A. Smith
Date: 2017-05-25 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Topic Models, Variational Inference

Most real-world document collections involve various types of metadata, such
as author, source, and date, and yet the most commonly-used approaches to
modeling text corpora ignore this information. While specialized models have
been developed for particular applications, few are widely used in practice, as
customization typically requires derivation of a custom inference algorithm. In
this paper, we build on recent advances in variational inference methods and
propose a general neural framework, based on topic models, to enable flexible
incorporation of metadata and allow for rapid exploration of alternative
models. Our approach achieves strong performance, with a manageable tradeoff
between perplexity, coherence, and sparsity. Finally, we demonstrate the
potential of our framework through an exploration of a corpus of articles about
US immigration.
Implicit Regularization in Matrix Factorization
Authors: Suriya Gunasekar, Blake Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
Date: 2017-05-25 00:00:00
Proceeding: NeurIPS 2017 12

We study implicit regularization when optimizing an underdetermined quadratic
objective over a matrix $X$ with gradient descent on a factorization of $X$. We
conjecture and provide empirical and theoretical evidence that with small
enough step sizes and initialization close enough to the origin, gradient
descent on a full dimensional factorization converges to the minimum nuclear
norm solution.
Semantics derived automatically from language corpora contain human-like biases
Authors: Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan
Date: 2016-08-25 00:00:00
Tasks: BIG-bench Machine Learning, Ethics, Sociology

Artificial intelligence and machine learning are in a period of astounding
growth. However, there are concerns that these technologies may be used, either
with or without intention, to perpetuate the prejudice and unfairness that
unfortunately characterizes many human institutions. Here we show for the first
time that human-like semantic biases result from the application of standard
machine learning to ordinary language---the same sort of language humans are
exposed to every day. We replicate a spectrum of standard human biases as
exposed by the Implicit Association Test and other well-known psychological
studies. We replicate these using a widely used, purely statistical
machine-learning model---namely, the GloVe word embedding---trained on a corpus
of text from the Web. Our results indicate that language itself contains
recoverable and accurate imprints of our historic biases, whether these are
morally neutral as towards insects or flowers, problematic as towards race or
gender, or even simply veridical, reflecting the {\em status quo} for the
distribution of gender with respect to careers or first names. These
regularities are captured by machine learning along with the rest of semantics.
In addition to our empirical findings concerning language, we also contribute
new methods for evaluating bias in text, the Word Embedding Association Test
(WEAT) and the Word Embedding Factual Association Test (WEFAT). Our results
have implications not only for AI and machine learning, but also for the fields
of psychology, sociology, and human ethics, since they raise the possibility
that mere exposure to everyday language can account for the biases we replicate
here.
Geometric Methods for Robust Data Analysis in High Dimension
Authors: Joseph Anderson
Date: 2017-05-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Machine learning and data analysis now finds both scientific and industrial
application in biology, chemistry, geology, medicine, and physics. These
applications rely on large quantities of data gathered from automated sensors
and user input. Furthermore, the dimensionality of many datasets is extreme:
more details are being gathered about single user interactions or sensor
readings. All of these applications encounter problems with a common theme: use
observed data to make inferences about the world. Our work obtains the first
provably efficient algorithms for Independent Component Analysis (ICA) in the
presence of heavy-tailed data. The main tool in this result is the centroid
body (a well-known topic in convex geometry), along with optimization and
random walks for sampling from a convex body. This is the first algorithmic use
of the centroid body and it is of independent theoretical interest, since it
effectively replaces the estimation of covariance from samples, and is more
generally accessible.
  This reduction relies on a non-linear transformation of samples from such an
intersection of halfspaces (i.e. a simplex) to samples which are approximately
from a linearly transformed product distribution. Through this transformation
of samples, which can be done efficiently, one can then use an ICA algorithm to
recover the vertices of the intersection of halfspaces.
  Finally, we again use ICA as an algorithmic primitive to construct an
efficient solution to the widely-studied problem of learning the parameters of
a Gaussian mixture model. Our algorithm again transforms samples from a
Gaussian mixture model into samples which fit into the ICA model and, when
processed by an ICA algorithm, result in recovery of the mixture parameters.
Our algorithm is effective even when the number of Gaussians in the mixture
grows polynomially with the ambient dimension
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Authors: Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Date: 2017-05-25 00:00:00
Tasks: Bayesian Optimisation, Thompson Sampling

We design and analyse variations of the classical Thompson sampling (TS)
procedure for Bayesian optimisation (BO) in settings where function evaluations
are expensive, but can be performed in parallel. Our theoretical analysis shows
that a direct application of the sequential Thompson sampling algorithm in
either synchronous or asynchronous parallel settings yields a surprisingly
powerful result: making $n$ evaluations distributed among $M$ workers is
essentially equivalent to performing $n$ evaluations in sequence. Further, by
modeling the time taken to complete a function evaluation, we show that, under
a time constraint, asynchronously parallel TS achieves asymptotically lower
regret than both the synchronous and sequential versions. These results are
complemented by an experimental analysis, showing that asynchronous TS
outperforms a suite of existing parallel BO algorithms in simulations and in a
hyper-parameter tuning application in convolutional neural networks. In
addition to these, the proposed procedure is conceptually and computationally
much simpler than existing work for parallel BO.
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
Authors: Zecheng Xie, Zenghui Sun, Lianwen Jin, Hao Ni, Terry Lyons
Date: 2016-10-09 00:00:00
Tasks: Handwritten Chinese Text Recognition, Language Modeling, Language Modelling, Segmentation

Online handwritten Chinese text recognition (OHCTR) is a challenging problem
as it involves a large-scale character set, ambiguous segmentation, and
variable-length input sequences. In this paper, we exploit the outstanding
capability of path signature to translate online pen-tip trajectories into
informative signature feature maps using a sliding window-based method,
successfully capturing the analytic and geometric properties of pen strokes
with strong local invariance and robustness. A multi-spatial-context fully
convolutional recurrent network (MCFCRN) is proposed to exploit the multiple
spatial contexts from the signature feature maps and generate a prediction
sequence while completely avoiding the difficult segmentation problem.
Furthermore, an implicit language model is developed to make predictions based
on semantic context within a predicting feature sequence, providing a new
perspective for incorporating lexicon constraints and prior knowledge about a
certain language in the recognition procedure. Experiments on two standard
benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with
correct rates of 97.10% and 97.15%, respectively, which are significantly
better than the best result reported thus far in the literature.
Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network
Authors: Sultan Imangaliyev, Monique H. van der Veen, Catherine M. C. Volgenant, Bruno G. Loos, Bart J. F. Keijser, Wim Crielaard, Evgeni Levin
Date: 2017-05-25 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Images are an important data source for diagnosis and treatment of oral
diseases. The manual classification of images may lead to misdiagnosis or
mistreatment due to subjective errors. In this paper an image classification
model based on Convolutional Neural Network is applied to Quantitative
Light-induced Fluorescence images. The deep neural network outperforms other
state of the art shallow classification models in predicting labels derived
from three different dental plaque assessment scores. The model directly
benefits from multi-channel representation of the images resulting in improved
performance when, besides the Red colour channel, additional Green and Blue
colour channels are used.
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs
Authors: Jean Maillard, Stephen Clark, Dani Yogatama
Date: 2017-05-25 00:00:00
Proceeding: ICLR 2018 1
Tasks: Natural Language Inference, Reverse Dictionary, Sentence, Sentence Embeddings

We introduce a neural network that represents sentences by composing their
words according to induced binary parse trees. We use Tree-LSTM as our
composition function, applied along a tree structure found by a fully
differentiable natural language chart parser. Our model simultaneously
optimises both the composition function and the parser, thus eliminating the
need for externally-provided parse trees which are normally required for
Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised
with respect to the parse trees. As it is fully differentiable, our model is
easily trained with an off-the-shelf gradient descent method and
backpropagation. We demonstrate that it achieves better performance compared to
various supervised Tree-LSTM architectures on a textual entailment task and a
reverse dictionary task.
Investigation of Using VAE for i-Vector Speaker Verification
Authors: Timur Pekhovsky, Maxim Korenevsky
Date: 2017-05-25 00:00:00
Tasks: Speaker Recognition, Speaker Verification

New system for i-vector speaker recognition based on variational autoencoder
(VAE) is investigated. VAE is a promising approach for developing accurate deep
nonlinear generative models of complex data. Experiments show that VAE provides
speaker embedding and can be effectively trained in an unsupervised manner. LLR
estimate for VAE is developed. Experiments on NIST SRE 2010 data demonstrate
its correctness. Additionally, we show that the performance of VAE-based system
in the i-vectors space is close to that of the diagonal PLDA. Several
interesting results are also observed in the experiments with $\beta$-VAE. In
particular, we found that for $\beta\ll 1$, VAE can be trained to capture the
features of complex input data distributions in an effective way, which is hard
to obtain in the standard VAE ($\beta=1$).
Jeffrey's prior sampling of deep sigmoidal networks
Authors: Lorien X. Hayden, Alexander A. Alemi, Paul H. Ginsparg, James P. Sethna
Date: 2017-05-25 00:00:00
Tasks: Denoising

Neural networks have been shown to have a remarkable ability to uncover low
dimensional structure in data: the space of possible reconstructed images form
a reduced model manifold in image space. We explore this idea directly by
analyzing the manifold learned by Deep Belief Networks and Stacked Denoising
Autoencoders using Monte Carlo sampling. The model manifold forms an only
slightly elongated hyperball with actual reconstructed data appearing
predominantly on the boundaries of the manifold. In connection with the results
we present, we discuss problems of sampling high-dimensional manifolds as well
as recent work [M. Transtrum, G. Hart, and P. Qiu, Submitted (2014)] discussing
the relation between high dimensional geometry and model reduction.
Empirically Grounded Agent-Based Models of Innovation Diffusion: A Critical Review
Authors: Haifeng Zhang, Yevgeniy Vorobeychik
Date: 2016-08-30 00:00:00
Tasks: Marketing, Sociology

Innovation diffusion has been studied extensively in a variety of
disciplines, including sociology, economics, marketing, ecology, and computer
science. Traditional literature on innovation diffusion has been dominated by
models of aggregate behavior and trends. However, the agent-based modeling
(ABM) paradigm is gaining popularity as it captures agent heterogeneity and
enables fine-grained modeling of interactions mediated by social and geographic
networks. While most ABM work on innovation diffusion is theoretical,
empirically grounded models are increasingly important, particularly in guiding
policy decisions. We present a critical review of empirically grounded
agent-based models of innovation diffusion, developing a categorization of this
research based on types of agent models as well as applications. By connecting
the modeling methodologies in the fields of information and innovation
diffusion, we suggest that the maximum likelihood estimation framework widely
used in the former is a promising paradigm for calibration of agent-based
models for innovation diffusion. Although many advances have been made to
standardize ABM methodology, we identify four major issues in model calibration
and validation, and suggest potential solutions.
Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment
Authors: Haizhen Wang, Ratthachat Chatpatanasiri, Pairote Sattayatham
Date: 2017-05-25 00:00:00
Tasks: Bayesian Inference, Econometrics, parameter estimation

On a daily investment decision in a security market, the price earnings (PE)
ratio is one of the most widely applied methods being used as a firm valuation
tool by investment experts. Unfortunately, recent academic developments in
financial econometrics and machine learning rarely look at this tool. In
practice, fundamental PE ratios are often estimated only by subjective expert
opinions. The purpose of this research is to formalize a process of fundamental
PE estimation by employing advanced dynamic Bayesian network (DBN) methodology.
The estimated PE ratio from our model can be used either as a information
support for an expert to make investment decisions, or as an automatic trading
system illustrated in experiments. Forward-backward inference and EM parameter
estimation algorithms are derived with respect to the proposed DBN structure.
Unlike existing works in literatures, the economic interpretation of our DBN
model is well-justified by behavioral finance evidences of volatility. A simple
but practical trading strategy is invented based on the result of Bayesian
inference. Extensive experiments show that our trading strategy equipped with
the inferenced PE ratios consistently outperforms standard investment
benchmarks.
Discriminative Metric Learning with Deep Forest
Authors: Lev V. Utkin, Mikhail A. Ryabinin
Date: 2017-05-25 00:00:00
Tasks: Metric Learning

A Discriminative Deep Forest (DisDF) as a metric learning algorithm is
proposed in the paper. It is based on the Deep Forest or gcForest proposed by
Zhou and Feng and can be viewed as a gcForest modification. The case of the
fully supervised learning is studied when the class labels of individual
training examples are known. The main idea underlying the algorithm is to
assign weights to decision trees in random forest in order to reduce distances
between objects from the same class and to increase them between objects from
different classes. The weights are training parameters. A specific objective
function which combines Euclidean and Manhattan distances and simplifies the
optimization problem for training the DisDF is proposed. The numerical
experiments illustrate the proposed distance metric algorithm.
Deep image representations using caption generators
Authors: Konda Reddy Mopuri, Vishal B. Athreya, R. Venkatesh Babu
Date: 2017-05-25 00:00:00
Tasks: Retrieval, Transfer Learning

Deep learning exploits large volumes of labeled data to learn powerful
models. When the target dataset is small, it is a common practice to perform
transfer learning using pre-trained models to learn new task specific
representations. However, pre-trained CNNs for image recognition are provided
with limited information about the image during training, which is label alone.
Tasks such as scene retrieval suffer from features learned from this weak
supervision and require stronger supervision to better understand the contents
of the image. In this paper, we exploit the features learned from caption
generating models to learn novel task specific image representations. In
particular, we consider the state-of-the art captioning system Show and
Tell~\cite{SnT-pami-2016} and the dense region description model
DenseCap~\cite{densecap-cvpr-2016}. We demonstrate that, owing to richer
supervision provided during the process of training, the features learned by
the captioning system perform better than those of CNNs. Further, we train a
siamese network with a modified pair-wise loss to fuse the features learned
by~\cite{SnT-pami-2016} and~\cite{densecap-cvpr-2016} and learn image
representations suitable for retrieval. Experiments show that the proposed
fusion exploits the complementary nature of the individual features and yields
state-of-the art retrieval results on benchmark datasets.