pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression
Authors: Joseph Futoma, Mark Sendak, C. Blake Cameron, Katherine Heller
Date: 2016-08-16 00:00:00
Tasks: Management, Variational Inference

Prediction of the future trajectory of a disease is an important challenge
for personalized medicine and population health management. However, many
complex chronic diseases exhibit large degrees of heterogeneity, and
furthermore there is not always a single readily available biomarker to
quantify disease severity. Even when such a clinical variable exists, there are
often additional related biomarkers routinely measured for patients that may
better inform the predictions of their future disease state. To this end, we
propose a novel probabilistic generative model for multivariate longitudinal
data that captures dependencies between multivariate trajectories. We use a
Gaussian process based regression model for each individual trajectory, and
build off ideas from latent class models to induce dependence between their
mean functions. We fit our method using a scalable variational inference
algorithm to a large dataset of longitudinal electronic patient health records,
and find that it improves dynamic predictions compared to a recent state of the
art method. Our local accountable care organization then uses the model
predictions during chart reviews of high risk patients with chronic kidney
disease.
Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements
Authors: Keith Levin, Vince Lyzinski
Date: 2016-03-12 00:00:00
Tasks: Dimensionality Reduction

Manifold learning and dimensionality reduction techniques are ubiquitous in
science and engineering, but can be computationally expensive procedures when
applied to large data sets or when similarities are expensive to compute. To
date, little work has been done to investigate the tradeoff between
computational resources and the quality of learned representations. We present
both theoretical and experimental explorations of this question. In particular,
we consider Laplacian eigenmaps embeddings based on a kernel matrix, and
explore how the embeddings behave when this kernel matrix is corrupted by
occlusion and noise. Our main theoretical result shows that under modest noise
and occlusion assumptions, we can (with high probability) recover a good
approximation to the Laplacian eigenmaps embedding based on the uncorrupted
kernel matrix. Our results also show how regularization can aid this
approximation. Experimentally, we explore the effects of noise and occlusion on
Laplacian eigenmaps embeddings of two real-world data sets, one from speech
processing and one from neuroscience, as well as a synthetic data set.
Conformalized density- and distance-based anomaly detection in time-series data
Authors: Evgeny Burnaev, Vladislav Ishimtsev
Date: 2016-08-16 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Time Series, Time Series Analysis

Anomalies (unusual patterns) in time-series data give essential, and often
actionable information in critical situations. Examples can be found in such
fields as healthcare, intrusion detection, finance, security and flight safety.
In this paper we propose new conformalized density- and distance-based anomaly
detection algorithms for a one-dimensional time-series data. The algorithms use
a combination of a feature extraction method, an approach to assess a score
whether a new observation differs significantly from a previously observed
data, and a probabilistic interpretation of this score based on the conformal
paradigm.
A novel transfer learning method based on common space mapping and weighted domain matching
Authors: Ru-Ze Liang, Wei Xie, Weizhi Li, Hongqi Wang, Jim Jing-Yan Wang, Lisa Taylor
Date: 2016-08-16 00:00:00
Tasks: domain classification, General Classification, Transfer Learning

In this paper, we propose a novel learning framework for the problem of
domain transfer learning. We map the data of two domains to one single common
space, and learn a classifier in this common space. Then we adapt the common
classifier to the two domains by adding two adaptive functions to it
respectively. In the common space, the target domain data points are weighted
and matched to the target domain in term of distributions. The weighting terms
of source domain data points and the target domain classification responses are
also regularized by the local reconstruction coefficients. The novel transfer
learning framework is evaluated over some benchmark cross-domain data sets, and
it outperforms the existing state-of-the-art transfer learning methods.
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
Authors: Ru-Ze Liang, Wei Xie, Weizhi Li, Xin Du, Jim Jing-Yan Wang, Jingbin Wang
Date: 2016-06-07 00:00:00
Tasks: Prediction, regression, Structured Prediction

We propose a novel semi-supervised structured output prediction method based
on local linear regression in this paper. The existing semi-supervise
structured output prediction methods learn a global predictor for all the data
points in a data set, which ignores the differences of local distributions of
the data set, and the effects to the structured output prediction. To solve
this problem, we propose to learn the missing structured outputs and local
predictors for neighborhoods of different data points jointly. Using the local
linear regression strategy, in the neighborhood of each data point, we propose
to learn a local linear predictor by minimizing both the complexity of the
predictor and the upper bound of the structured prediction loss. The
minimization problem is solved by sub-gradient descent algorithms. We conduct
experiments over two benchmark data sets, and the results show the advantages
of the proposed method.
Does Multimodality Help Human and Machine for Translation and Image Captioning?
Authors: Ozan Caglayan, Walid Aransa, Yaxing Wang, Marc Masana, Mercedes García-Martínez, Fethi Bougares, Loïc Barrault, Joost Van de Weijer
Date: 2016-05-30 00:00:00
Proceeding: WS 2016 8
Tasks: Image Captioning, Image Description, Machine Translation, Multimodal Machine Translation, Translation

This paper presents the systems developed by LIUM and CVC for the WMT16
Multimodal Machine Translation challenge. We explored various comparative
methods, namely phrase-based systems and attentional recurrent neural networks
models trained using monomodal or multimodal data. We also performed a human
evaluation in order to estimate the usefulness of multimodal data for human
machine translation and image description generation. Our systems obtained the
best results for both tasks according to the automatic evaluation metrics BLEU
and METEOR.
Weakly Supervised Object Localization Using Size Estimates
Authors: Miaojing Shi, Vittorio Ferrari
Date: 2016-08-15 00:00:00
Tasks: Object, Object Localization, Weakly-Supervised Object Localization

We present a technique for weakly supervised object localization (WSOL),
building on the observation that WSOL algorithms usually work better on images
with bigger objects. Instead of training the object detector on the entire
training set at the same time, we propose a curriculum learning strategy to
feed training images into the WSOL learning loop in an order from images
containing bigger objects down to smaller ones. To automatically determine the
order, we train a regressor to estimate the size of the object given the whole
image as input. Furthermore, we use these size estimates to further improve the
re-localization step of WSOL by assigning weights to object proposals according
to how close their size matches the estimated object size. We demonstrate the
effectiveness of using size order and size weighting on the challenging PASCAL
VOC 2007 dataset, where we achieve a significant improvement over existing
state-of-the-art WSOL techniques.
Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations
Authors: Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene
Date: 2016-08-16 00:00:00
Tasks: Bayesian Optimization

A novel efficient method for computing the Knowledge-Gradient policy for
Continuous Parameters (KGCP) for deterministic optimization is derived. The
differences with Expected Improvement (EI), a popular choice for Bayesian
optimization of deterministic engineering simulations, are explored. Both
policies and the Upper Confidence Bound (UCB) policy are compared on a number
of benchmark functions including a problem from structural dynamics. It is
empirically shown that KGCP has similar performance as the EI policy for many
problems, but has better convergence properties for complex (multi-modal)
optimization problems as it emphasizes more on exploration when the model is
confident about the shape of optimal regions. In addition, the relationship
between Maximum Likelihood Estimation (MLE) and slice sampling for estimation
of the hyperparameters of the underlying models, and the complexity of the
problem at hand, is studied.
The Bayesian Low-Rank Determinantal Point Process Mixture Model
Authors: Mike Gartrell, Ulrich Paquet, Noam Koenigstein
Date: 2016-08-15 00:00:00
Tasks: Point Processes, Product Recommendation

Determinantal point processes (DPPs) are an elegant model for encoding
probabilities over subsets, such as shopping baskets, of a ground set, such as
an item catalog. They are useful for a number of machine learning tasks,
including product recommendation. DPPs are parametrized by a positive
semi-definite kernel matrix. Recent work has shown that using a low-rank
factorization of this kernel provides remarkable scalability improvements that
open the door to training on large-scale datasets and computing online
recommendations, both of which are infeasible with standard DPP models that use
a full-rank kernel. In this paper we present a low-rank DPP mixture model that
allows us to represent the latent structure present in observed subsets as a
mixture of a number of component low-rank DPPs, where each component DPP is
responsible for representing a portion of the observed data. The mixture model
allows us to effectively address the capacity constraints of the low-rank DPP
model. We present an efficient and scalable Markov Chain Monte Carlo (MCMC)
learning algorithm for our model that uses Gibbs sampling and stochastic
gradient Hamiltonian Monte Carlo (SGHMC). Using an evaluation on several
real-world product recommendation datasets, we show that our low-rank DPP
mixture model provides substantially better predictive performance than is
possible with a single low-rank or full-rank DPP, and significantly better
performance than several other competing recommendation methods in many cases.
Free Lunch for Optimisation under the Universal Distribution
Authors: Tom Everitt, Tor Lattimore, Marcus Hutter
Date: 2016-08-16 00:00:00

Function optimisation is a major challenge in computer science. The No Free
Lunch theorems state that if all functions with the same histogram are assumed
to be equally probable then no algorithm outperforms any other in expectation.
We argue against the uniform assumption and suggest a universal prior exists
for which there is a free lunch, but where no particular class of functions is
favoured over another. We also prove upper and lower bounds on the size of the
free lunch.
Dopamine modulation of prefrontal delay activity-reverberatory activity and sharpness of tuning curves
Authors: Gabriele Scheler, Jean-Marc Fellous
Date: 2016-08-16 00:00:00

Recent electrophysiological experiments have shown that dopamine (D1)
modulation of pyramidal cells in prefrontal cortex reduces spike frequency
adaptation and enhances NMDA transmission. Using four models, from
multicompartmental to integrate and fire, we examine the effects of these
modulations on sustained (delay) activity in a reverberatory network. We find
that D1 modulation may enable robust network bistability yielding selective
reverberation among cells that code for a particular item or location. We
further show that the tuning curve of such cells is sharpened, and that
signal-to-noise ratio is increased. We postulate that D1 modulation affects the
tuning of "memory fields" and yield efficient distributed dynamic
representations.
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
Authors: Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston
Date: 2016-08-15 00:00:00
Tasks: 3D Point Cloud Classification, Classification, General Classification, Object

When working with three-dimensional data, choice of representation is key. We
explore voxel-based models, and present evidence for the viability of
voxellated representations in applications including shape modeling and object
classification. Our key contributions are methods for training voxel-based
variational autoencoders, a user interface for exploring the latent space
learned by the autoencoder, and a deep convolutional neural network
architecture for object classification. We address challenges unique to
voxel-based representations, and empirically evaluate our models on the
ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the
state of the art for object classification.
Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras Calibration
Authors: Chunping Zhang, Zhe Ji, Qing Wang
Date: 2016-08-16 00:00:00
Tasks: 3D Reconstruction, Vocal Bursts Valence Prediction

The plenoptic camera can capture both angular and spatial information of the
rays, enabling 3D reconstruction by single exposure. The geometry of the
recovered scene structure is affected by the calibration of the plenoptic
camera significantly. In this paper, we propose a novel unconstrained
two-parallel-plane (TPP) model with 7 parameters to describe a 4D light field.
By reconstructing scene points from ray-ray association, a 3D projective
transformation is deduced to establish the relationship between the scene
structure and the TPP parameters. Based on the transformation, we simplify the
focused plenoptic camera as a TPP model and calibrate its intrinsic parameters.
Our calibration method includes a close-form solution and a nonlinear
optimization by minimizing re-projection error. Experiments on both simulated
data and real scene data verify the performance of the calibration on the
focused plenoptic camera.
A Light-powered, Always-On, Smart Camera with Compressed Domain Gesture Detection
Authors: Anvesha A, Shaojie Xu, Ningyuan Cao, Justin Romberg, Arijit Raychowdhury
Date: 2016-05-26 00:00:00
Tasks: Dynamic Time Warping, Gesture Recognition, Point Tracking

In this paper we propose an energy-efficient camera-based gesture recognition
system powered by light energy for "always on" applications. Low energy
consumption is achieved by directly extracting gesture features from the
compressed measurements, which are the block averages and the linear
combinations of the image sensor's pixel values. The gestures are recognized
using a nearest-neighbour (NN) classifier followed by Dynamic Time Warping
(DTW). The system has been implemented on an Analog Devices Black Fin ULP
vision processor and powered by PV cells whose output is regulated by TI's
DC-DC buck converter with Maximum Power Point Tracking (MPPT). Measured data
reveals that with only 400 compressed measurements (768x compression ratio) per
frame, the system is able to recognize key wake-up gestures with greater than
80% accuracy and only 95mJ of energy per frame. Owing to its fully self-powered
operation, the proposed system can find wide applications in "always-on" vision
systems such as in surveillance, robotics and consumer electronics with
touch-less operation.
SenTion: A framework for Sensing Facial Expressions
Authors: Rahul Islam, Karan Ahuja, Sandip Karmakar, Ferdous Barbhuiya
Date: 2016-08-16 00:00:00
Tasks: CPU, Facial Expression Recognition, Facial Expression Recognition (FER), GPU

Facial expressions are an integral part of human cognition and communication,
and can be applied in various real life applications. A vital precursor to
accurate expression recognition is feature extraction. In this paper, we
propose SenTion: A framework for sensing facial expressions. We propose a novel
person independent and scale invariant method of extracting Inter Vector Angles
(IVA) as geometric features, which proves to be robust and reliable across
databases. SenTion employs a novel framework of combining geometric (IVA's) and
appearance based features (Histogram of Gradients) to create a hybrid model,
that achieves state of the art recognition accuracy. We evaluate the
performance of SenTion on two famous face expression data set, namely: CK+ and
JAFFE; and subsequently evaluate the viability of facial expression systems by
a user study. Extensive experiments showed that SenTion framework yielded
dramatic improvements in facial expression recognition and could be employed in
real-world applications with low resolution imaging and minimal computational
resources in real-time, achieving 15-18 fps on a 2.4 GHz CPU with no GPU.
Learning values across many orders of magnitude
Authors: Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver
Date: 2016-02-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most learning algorithms are not invariant to the scale of the function that
is being approximated. We propose to adaptively normalize the targets used in
learning. This is useful in value-based reinforcement learning, where the
magnitude of appropriate value approximations can change over time when we
update the policy of behavior. Our main motivation is prior work on learning to
play Atari games, where the rewards were all clipped to a predetermined range.
This clipping facilitates learning across many different games with a single
learning algorithm, but a clipped reward function can result in qualitatively
different behavior. Using the adaptive normalization we can remove this
domain-specific heuristic without diminishing overall performance.
Author Identification using Multi-headed Recurrent Neural Networks
Authors: Douglas Bagnall
Date: 2015-06-16 00:00:00
Tasks: Language Modeling, Language Modelling

Recurrent neural networks (RNNs) are very good at modelling the flow of text,
but typically need to be trained on a far larger corpus than is available for
the PAN 2015 Author Identification task. This paper describes a novel approach
where the output layer of a character-level RNN language model is split into
several independent predictive sub-models, each representing an author, while
the recurrent layer is shared by all. This allows the recurrent layer to model
the language as a whole without over-fitting, while the outputs select aspects
of the underlying model that reflect their author's style. The method proves
competitive, ranking first in two of the four languages.
Authorship clustering using multi-headed recurrent neural networks
Authors: Douglas Bagnall
Date: 2016-08-16 00:00:00
Tasks: Clustering

A recurrent neural network that has been trained to separately model the
language of several documents by unknown authors is used to measure similarity
between the documents. It is able to find clues of common authorship even when
the documents are very short and about disparate topics. While it is easy to
make statistically significant predictions regarding authorship, it is
difficult to group documents into definite clusters with high accuracy.
Lecture Notes on Randomized Linear Algebra
Authors: Michael W. Mahoney
Date: 2016-08-16 00:00:00

These are lecture notes that are based on the lectures from a class I taught
on the topic of Randomized Linear Algebra (RLA) at UC Berkeley during the Fall
2013 semester.
A Geometrical Approach to Topic Model Estimation
Authors: Zheng Tracy Ke
Date: 2016-08-16 00:00:00
Tasks: model, Topic Models

In the probabilistic topic models, the quantity of interest---a low-rank
matrix consisting of topic vectors---is hidden in the text corpus matrix,
masked by noise, and the Singular Value Decomposition (SVD) is a potentially
useful tool for learning such a low-rank matrix. However, the connection
between this low-rank matrix and the singular vectors of the text corpus matrix
are usually complicated and hard to spell out, so how to use SVD for learning
topic models faces challenges. In this paper, we overcome the challenge by
revealing a surprising insight: there is a low-dimensional simplex structure
which can be viewed as a bridge between the low-rank matrix of interest and the
SVD of the text corpus matrix, and allows us to conveniently reconstruct the
former using the latter. Such an insight motivates a new SVD approach to
learning topic models, which we analyze with delicate random matrix theory and
derive the rate of convergence. We support our methods and theory numerically,
using both simulated data and real data.
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation
Authors: Kai Wei, Rishabh Iyer, Shengjie Wang, Wenruo Bai, Jeff Bilmes
Date: 2015-10-29 00:00:00
Proceeding: NeurIPS 2015
Tasks: Clustering, Distributed Optimization, Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

We study two mixed robust/average-case submodular partitioning problems that
we collectively call Submodular Partitioning. These problems generalize both
purely robust instances of the problem (namely max-min submodular fair
allocation (SFA) and min-max submodular load balancing (SLB) and also
generalize average-case instances (that is the submodular welfare problem (SWP)
and submodular multiway partition (SMP). While the robust versions have been
studied in the theory community, existing work has focused on tight
approximation guarantees, and the resultant algorithms are not, in general,
scalable to very large real-world applications. This is in contrast to the
average case, where most of the algorithms are scalable. In the present paper,
we bridge this gap, by proposing several new algorithms (including those based
on greedy, majorization-minimization, minorization-maximization, and relaxation
algorithms) that not only scale to large sizes but that also achieve
theoretical approximation guarantees close to the state-of-the-art, and in some
cases achieve new tight bounds. We also provide new scalable algorithms that
apply to additive combinations of the robust and average-case extreme
objectives. We show that these problems have many applications in machine
learning (ML). This includes: 1) data partitioning and load balancing for
distributed machine algorithms on parallel machines; 2) data clustering; and 3)
multi-label image segmentation with (only) Boolean submodular functions via
pixel partitioning. We empirically demonstrate the efficacy of our algorithms
on real-world problems involving data partitioning for distributed optimization
of standard machine learning objectives (including both convex and deep neural
network objectives), and also on purely unsupervised (i.e., no supervised or
semi-supervised learning, and no interactive segmentation) image segmentation.