pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Node Attributes for Incomplete Relational Data
Authors: He Zhao, Lan Du, Wray Buntine
Date: 2017-06-14 00:00:00
Proceeding: ICML 2017 8
Tasks: Community Detection, Link Prediction, Prediction

Relational data are usually highly incomplete in practice, which inspires us
to leverage side information to improve the performance of community detection
and link prediction. This paper presents a Bayesian probabilistic approach that
incorporates various kinds of node attributes encoded in binary form in
relational models with Poisson likelihood. Our method works flexibly with both
directed and undirected relational networks. The inference can be done by
efficient Gibbs sampling which leverages sparsity of both networks and node
attributes. Extensive experiments show that our models achieve the
state-of-the-art link prediction results, especially with highly incomplete
relational data.
Homotopy Analysis for Tensor PCA
Authors: Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi
Date: 2016-10-28 00:00:00
Tasks: global-optimization

Developing efficient and guaranteed nonconvex algorithms has been an
important challenge in modern machine learning. Algorithms with good empirical
performance such as stochastic gradient descent often lack theoretical
guarantees. In this paper, we analyze the class of homotopy or continuation
methods for global optimization of nonconvex functions. These methods start
from an objective function that is efficient to optimize (e.g. convex), and
progressively modify it to obtain the required objective, and the solutions are
passed along the homotopy path. For the challenging problem of tensor PCA, we
prove global convergence of the homotopy method in the "high noise" regime. The
signal-to-noise requirement for our algorithm is tight in the sense that it
matches the recovery guarantee for the best degree-4 sum-of-squares algorithm.
In addition, we prove a phase transition along the homotopy path for tensor
PCA. This allows to simplify the homotopy method to a local search algorithm,
viz., tensor power iterations, with a specific initialization and a noise
injection procedure, while retaining the theoretical guarantees.
Saliency detection by aggregating complementary background template with optimization framework
Authors: Chenxing Xia, Hanling Zhang, Xiuju Gao
Date: 2017-06-14 00:00:00
Tasks: Saliency Detection

This paper proposes an unsupervised bottom-up saliency detection approach by
aggregating complementary background template with refinement. Feature vectors
are extracted from each superpixel to cover regional color, contrast and
texture information. By using these features, a coarse detection for salient
region is realized based on background template achieved by different
combinations of boundary regions instead of only treating four boundaries as
background. Then, by ranking the relevance of the image nodes with foreground
cues extracted from the former saliency map, we obtain an improved result.
Finally, smoothing operation is utilized to refine the foreground-based
saliency map to improve the contrast between salient and non-salient regions
until a close to binary saliency map is reached. Experimental results show that
the proposed algorithm generates more accurate saliency maps and performs
favorably against the state-off-the-art saliency detection methods on four
publicly available datasets.
An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functions
Authors: Alexander R. Luedtke, Marco Carone, Mark J. Van Der Laan
Date: 2015-10-14 00:00:00

We present a novel family of nonparametric omnibus tests of the hypothesis
that two unknown but estimable functions are equal in distribution when applied
to the observed data structure. We developed these tests, which represent a
generalization of the maximum mean discrepancy tests described in Gretton et
al. [2006], using recent developments from the higher-order pathwise
differentiability literature. Despite their complex derivation, the associated
test statistics can be expressed rather simply as U-statistics. We study the
asymptotic behavior of the proposed tests under the null hypothesis and under
both fixed and local alternatives. We provide examples to which our tests can
be applied and show that they perform well in a simulation study. As an
important special case, our proposed tests can be used to determine whether an
unknown function, such as the conditional average treatment effect, is equal to
zero almost surely.
Image-to-Markup Generation with Coarse-to-Fine Attention
Authors: Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush
Date: 2016-09-16 00:00:00
Proceeding: ICML 2017 8
Tasks: Decoder, Optical Character Recognition (OCR)

We present a neural encoder-decoder model to convert images into
presentational markup based on a scalable coarse-to-fine attention mechanism.
Our method is evaluated in the context of image-to-LaTeX generation, and we
introduce a new dataset of real-world rendered mathematical expressions paired
with LaTeX markup. We show that unlike neural OCR techniques using CTC-based
models, attention-based approaches can tackle this non-standard OCR task. Our
approach outperforms classical mathematical OCR systems by a large margin on
in-domain rendered data, and, with pretraining, also performs well on
out-of-domain handwritten data. To reduce the inference complexity associated
with the attention-based approaches, we introduce a new coarse-to-fine
attention layer that selects a support region before applying attention.
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
Authors: Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong
Date: 2016-10-15 00:00:00
Proceeding: ICML 2017 8
Tasks: Clustering, Deep Learning, Dimensionality Reduction

Most learning approaches treat dimensionality reduction (DR) and clustering
separately (i.e., sequentially), but recent research has shown that optimizing
the two tasks jointly can substantially improve the performance of both. The
premise behind the latter genre is that the data samples are obtained via
linear transformation of latent representations that are easy to cluster; but
in practice, the transformation from the latent space to the data can be more
complicated. In this work, we assume that this transformation is an unknown and
possibly nonlinear function. To recover the `clustering-friendly' latent
representations and to better cluster the data, we propose a joint DR and
K-means clustering approach in which DR is accomplished via learning a deep
neural network (DNN). The motivation is to keep the advantages of jointly
optimizing the two tasks, while exploiting the deep neural network's ability to
approximate any nonlinear function. This way, the proposed approach can work
well for a broad class of generative models. Towards this end, we carefully
design the DNN structure and the associated joint optimization criterion, and
propose an effective and scalable algorithm to handle the formulated
optimization problem. Experiments using different real datasets are employed to
showcase the effectiveness of the proposed approach.
Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization
Authors: Humam Alwassel, Fabian Caba Heilbron, Bernard Ghanem
Date: 2017-06-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Localization, Action Spotting, Temporal Action Localization

State-of-the-art temporal action detectors inefficiently search the entire
video for specific actions. Despite the encouraging progress these methods
achieve, it is crucial to design automated approaches that only explore parts
of the video which are the most relevant to the actions being searched for. To
address this need, we propose the new problem of action spotting in video,
which we define as finding a specific action in a video while observing a small
portion of that video. Inspired by the observation that humans are extremely
efficient and accurate in spotting and finding action instances in video, we
propose Action Search, a novel Recurrent Neural Network approach that mimics
the way humans spot actions. Moreover, to address the absence of data recording
the behavior of human annotators, we put forward the Human Searches dataset,
which compiles the search sequences employed by human annotators spotting
actions in the AVA and THUMOS14 datasets. We consider temporal action
localization as an application of the action spotting problem. Experiments on
the THUMOS14 dataset reveal that our model is not only able to explore the
video efficiently (observing on average 17.3% of the video) but it also
accurately finds human activities with 30.8% mAP.
The Price of Differential Privacy For Online Learning
Authors: Naman Agarwal, Karan Singh
Date: 2017-01-27 00:00:00
Proceeding: ICML 2017 8
Tasks: Multi-Armed Bandits

We design differentially private algorithms for the problem of online linear
optimization in the full information and bandit settings with optimal
$\tilde{O}(\sqrt{T})$ regret bounds. In the full-information setting, our
results demonstrate that $\epsilon$-differential privacy may be ensured for
free -- in particular, the regret bounds scale as
$O(\sqrt{T})+\tilde{O}\left(\frac{1}{\epsilon}\right)$. For bandit linear
optimization, and as a special case, for non-stochastic multi-armed bandits,
the proposed algorithm achieves a regret of
$\tilde{O}\left(\frac{1}{\epsilon}\sqrt{T}\right)$, while the previously known
best regret bound was
$\tilde{O}\left(\frac{1}{\epsilon}T^{\frac{2}{3}}\right)$.
Online Convolutional Dictionary Learning for Multimodal Imaging
Authors: Kevin Degraux, Ulugbek S. Kamilov, Petros T. Boufounos, Dehong Liu
Date: 2017-06-13 00:00:00
Tasks: Dictionary Learning

Computational imaging methods that can exploit multiple modalities have the
potential to enhance the capabilities of traditional sensing systems. In this
paper, we propose a new method that reconstructs multimodal images from their
linear measurements by exploiting redundancies across different modalities. Our
method combines a convolutional group-sparse representation of images with
total variation (TV) regularization for high-quality multimodal imaging. We
develop an online algorithm that enables the unsupervised learning of
convolutional dictionaries on large-scale datasets that are typical in such
applications. We illustrate the benefit of our approach in the context of joint
intensity-depth imaging.
Automatic Localization of Deep Stimulation Electrodes Using Trajectory-based Segmentation Approach
Authors: Roger Gomez Nieto, Andres Marino Alvarez Meza, Julian David Echeverry Correa, Alvaro Angel Orozco Gutierrez
Date: 2017-06-13 00:00:00
Tasks: Computed Tomography (CT), Medical Image Segmentation

Parkinson's disease (PD) is a degenerative condition of the nervous system,
which manifests itself primarily as muscle stiffness, hypokinesia,
bradykinesia, and tremor. In patients suffering from advanced stages of PD,
Deep Brain Stimulation neurosurgery (DBS) is the best alternative to medical
treatment, especially when they become tolerant to the drugs. This surgery
produces a neuronal activity, a result from electrical stimulation, whose
quantification is known as Volume of Tissue Activated (VTA). To locate
correctly the VTA in the cerebral volume space, one should be aware exactly the
location of the tip of the DBS electrodes, as well as their spatial projection.
  In this paper, we automatically locate DBS electrodes using a threshold-based
medical imaging segmentation methodology, determining the optimal value of this
threshold adaptively. The proposed methodology allows the localization of DBS
electrodes in Computed Tomography (CT) images, with high noise tolerance, using
automatic threshold detection methods.
On Optimistic versus Randomized Exploration in Reinforcement Learning
Authors: Ian Osband, Benjamin Van Roy
Date: 2017-06-13 00:00:00
Tasks: Computational Efficiency, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We discuss the relative merits of optimistic and randomized approaches to
exploration in reinforcement learning. Optimistic approaches presented in the
literature apply an optimistic boost to the value estimate at each state-action
pair and select actions that are greedy with respect to the resulting
optimistic value function. Randomized approaches sample from among
statistically plausible value functions and select actions that are greedy with
respect to the random sample. Prior computational experience suggests that
randomized approaches can lead to far more statistically efficient learning. We
present two simple analytic examples that elucidate why this is the case. In
principle, there should be optimistic approaches that fare well relative to
randomized approaches, but that would require intractable computation.
Optimistic approaches that have been proposed in the literature sacrifice
statistical efficiency for the sake of computational efficiency. Randomized
approaches, on the other hand, may enable simultaneous statistical and
computational efficiency.
Collaborative vehicle routing: a survey
Authors: Margaretha Gansterer, Richard F. Hartl
Date: 2017-06-13 00:00:00
Tasks: Survey

In horizontal collaborations, carriers form coalitions in order to perform
parts of their logistics operations jointly. By exchanging transportation
requests among each other, they can operate more efficiently and in a more
sustainable way. Collaborative vehicle routing has been extensively discussed
in the literature. We identify three major streams of research: (i) centralized
collaborative planning, (ii) decentralized planning without auctions, and (ii)
auction-based decentralized planning. For each of them we give a structured
overview on the state of knowledge and discuss future research directions.
Classification-based Financial Markets Prediction using Deep Neural Networks
Authors: Matthew Dixon, Diego Klabjan, Jin Hoon Bang
Date: 2016-03-29 00:00:00
Tasks: Algorithmic Trading, Classification, General Classification, Prediction

Deep neural networks (DNNs) are powerful types of artificial neural networks
(ANNs) that use several hidden layers. They have recently gained considerable
attention in the speech transcription and image recognition community
(Krizhevsky et al., 2012) for their superior predictive properties including
robustness to overfitting. However their application to algorithmic trading has
not been previously researched, partly because of their computational
complexity. This paper describes the application of DNNs to predicting
financial market movement directions. In particular we describe the
configuration and training approach and then demonstrate their application to
backtesting a simple trading strategy over 43 different Commodity and FX future
mid-prices at 5-minute intervals. All results in this paper are generated using
a C++ implementation on the Intel Xeon Phi co-processor which is 11.4x faster
than the serial version and a Python strategy backtesting environment both of
which are available as open source code written by the authors.
Equivariance Through Parameter-Sharing
Authors: Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos
Date: 2017-02-27 00:00:00
Proceeding: ICML 2017 8
Tasks: Sensitivity

We propose to study equivariance in deep neural networks through parameter
symmetries. In particular, given a group $\mathcal{G}$ that acts discretely on
the input and output of a standard neural network layer $\phi_{W}: \Re^{M} \to
\Re^{N}$, we show that $\phi_{W}$ is equivariant with respect to
$\mathcal{G}$-action iff $\mathcal{G}$ explains the symmetries of the network
parameters $W$. Inspired by this observation, we then propose two
parameter-sharing schemes to induce the desirable symmetry on $W$. Our
procedures for tying the parameters achieve $\mathcal{G}$-equivariance and,
under some conditions on the action of $\mathcal{G}$, they guarantee
sensitivity to all other permutation groups outside $\mathcal{G}$.
Identifying Spatial Relations in Images using Convolutional Neural Networks
Authors: Mandar Haldekar, Ashwinkumar Ganesan, Tim Oates
Date: 2017-06-13 00:00:00

Traditional approaches to building a large scale knowledge graph have usually
relied on extracting information (entities, their properties, and relations
between them) from unstructured text (e.g. Dbpedia). Recent advances in
Convolutional Neural Networks (CNN) allow us to shift our focus to learning
entities and relations from images, as they build robust models that require
little or no pre-processing of the images. In this paper, we present an
approach to identify and extract spatial relations (e.g., The girl is standing
behind the table) from images using CNNs. Our research addresses two specific
challenges: providing insight into how spatial relations are learned by the
network and which parts of the image are used to predict these relations. We
use the pre-trained network VGGNet to extract features from an image and train
a Multi-layer Perceptron (MLP) on a set of synthetic images and the sun09
dataset to extract spatial relations. The MLP predicts spatial relations
without a bounding box around the objects or the space in the image depicting
the relation. To understand how the spatial relations are represented in the
network, a heatmap is overlayed on the image to show the regions that are
deemed important by the network. Also, we analyze the MLP to show the
relationship between the activation of consistent groups of nodes and the
prediction of a spatial relation. We show how the loss of these groups affects
the networks ability to identify relations.
Practical Gauss-Newton Optimisation for Deep Learning
Authors: Aleksandar Botev, Hippolyt Ritter, David Barber
Date: 2017-06-12 00:00:00
Proceeding: ICML 2017 8
Tasks: Deep Learning

We present an efficient block-diagonal ap- proximation to the Gauss-Newton
matrix for feedforward neural networks. Our result- ing algorithm is
competitive against state- of-the-art first order optimisation methods, with
sometimes significant improvement in optimisation performance. Unlike
first-order methods, for which hyperparameter tuning of the optimisation
parameters is often a labo- rious process, our approach can provide good
performance even when used with default set- tings. A side result of our work
is that for piecewise linear transfer functions, the net- work objective
function can have no differ- entiable local maxima, which may partially explain
why such transfer functions facilitate effective optimisation.
Lost Relatives of the Gumbel Trick
Authors: Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller
Date: 2017-06-13 00:00:00
Proceeding: ICML 2017 8

The Gumbel trick is a method to sample from a discrete probability
distribution, or to estimate its normalizing partition function. The method
relies on repeatedly applying a random perturbation to the distribution in a
particular way, each time solving for the most likely configuration. We derive
an entire family of related methods, of which the Gumbel trick is one member,
and show that the new methods have superior properties in several settings with
minimal additional computational cost. In particular, for the Gumbel trick to
yield computational benefits for discrete graphical models, Gumbel
perturbations on all configurations are typically replaced with so-called
low-rank perturbations. We show how a subfamily of our new methods adapts to
this setting, proving new upper and lower bounds on the log partition function
and deriving a family of sequential samplers for the Gibbs distribution.
Finally, we balance the discussion by showing how the simpler analytical form
of the Gumbel trick enables additional theoretical results.
Temporally Efficient Deep Learning with Spikes
Authors: Peter O'Connor, Efstratios Gavves, Max Welling
Date: 2017-06-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Learning

The vast majority of natural sensory data is temporally redundant. Video
frames or audio samples which are sampled at nearby points in time tend to have
similar values. Typically, deep learning algorithms take no advantage of this
redundancy to reduce computation. This can be an obscene waste of energy. We
present a variant on backpropagation for neural networks in which computation
scales with the rate of change of the data - not the rate at which we process
the data. We do this by having neurons communicate a combination of their
state, and their temporal change in state. Intriguingly, this simple
communication rule give rise to units that resemble biologically-inspired leaky
integrate-and-fire neurons, and to a weight-update rule that is equivalent to a
form of Spike-Timing Dependent Plasticity (STDP), a synaptic learning rule
observed in the brain. We demonstrate that on MNIST and a temporal variant of
MNIST, our algorithm performs about as well as a Multilayer Perceptron trained
with backpropagation, despite only communicating discrete values between
layers.
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
Authors: Joseph Futoma, Sanjay Hariharan, Katherine Heller
Date: 2017-06-13 00:00:00
Proceeding: ICML 2017 8
Tasks: Gaussian Processes, Time Series, Time Series Analysis

We present a scalable end-to-end classifier that uses streaming physiological
and medication data to accurately predict the onset of sepsis, a
life-threatening complication from infections that has high mortality and
morbidity. Our proposed framework models the multivariate trajectories of
continuous-valued physiological time series using multitask Gaussian processes,
seamlessly accounting for the high uncertainty, frequent missingness, and
irregular sampling rates typically associated with real clinical data. The
Gaussian process is directly connected to a black-box classifier that predicts
whether a patient will become septic, chosen in our case to be a recurrent
neural network to account for the extreme variability in the length of patient
encounters. We show how to scale the computations associated with the Gaussian
process in a manner so that the entire system can be discriminatively trained
end-to-end using backpropagation. In a large cohort of heterogeneous inpatient
encounters at our university health system we find that it outperforms several
baselines at predicting sepsis, and yields 19.4% and 55.5% improved areas under
the Receiver Operating Characteristic and Precision Recall curves as compared
to the NEWS score currently used by our hospital.
Contour and Centreline Tracking of Vessels from Angiograms using the Classical Image Processing Techniques
Authors: Tache Irina Andra
Date: 2017-06-13 00:00:00
Tasks: Contour Detection

This article deals with the problem of vessel edge and centerline detection
using classical image processing techniques due to their simpleness and
easiness to be implemented. The method is divided into four steps: the vessel
enhancement which implies a non-linear filtering proposed by Frangi, the
thresholding using Otsu method and the contour detection using the Canny edge
detector due to its good performances for the small vessels and the
morphological skeletonisation. The algorithms are tested on real data collected
from a cardiac catheterism laboratory and it is accurate for images with good
spatial resolution (512*512). The output image can be used for further
processing in order to find the vessel length or its radius.