pwc-archive/papers-with-abstracts huggingface dataset:
Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures
Authors: Ze Jin, David S. Matteson
Date: 2018-05-17 00:00:00
Tasks: Bayesian Optimization, global-optimization

We apply both distance-based (Jin and Matteson, 2017) and kernel-based
(Pfister et al., 2016) mutual dependence measures to independent component
analysis (ICA), and generalize dCovICA (Matteson and Tsay, 2017) to MDMICA,
minimizing empirical dependence measures as an objective function in both
deflation and parallel manners. Solving this minimization problem, we introduce
Latin hypercube sampling (LHS) (McKay et al., 2000), and a global optimization
method, Bayesian optimization (BO) (Mockus, 1994) to improve the initialization
of the Newton-type local optimization method. The performance of MDMICA is
evaluated in various simulation studies and an image data example. When the ICA
model is correct, MDMICA achieves competitive results compared to existing
approaches. When the ICA model is misspecified, the estimated independent
components are less mutually dependent than the observed components using
MDMICA, while they are prone to be even more mutually dependent than the
observed components using other approaches.
Automatic Data Registration of Geostationary Payloads for Meteorological Applications at ISRO
Authors: Jignesh S. Bhatt, N. Padmanabhan
Date: 2018-05-17 00:00:00
Tasks: Weather Forecasting

The launch of KALPANA-1 satellite in the year 2002 heralded the establishment
of an indigenous operational payload for meteorological predictions. This was
further enhanced in the year 2003 with the launching of INSAT-3A satellite. The
software for generating products from the data of these two satellites was
taken up subsequently in the year 2004 and the same was installed at the Indian
Meteorological Department, New Delhi in January 2006. Registration has been one
of the most fundamental operations to generate almost all the data products
from the remotely sensed data. Registration is a challenging task due to
inevitable radiometric and geometric distortions during the acquisition
process. Besides the presence of clouds makes the problem more complicated. In
this paper, we present an algorithm for multitemporal and multiband
registration. In addition, India facing reference boundaries for the CCD data
of INSAT-3A have also been generated. The complete implementation is made up of
the following steps: 1) automatic identification of the ground control points
(GCPs) in the sensed data, 2) finding the optimal transformation model based on
the match-points, and 3) resampling the transformed imagery to the reference
coordinates. The proposed algorithm is demonstrated using the real datasets
from KALPANA-1 and INSAT-3A. Both KALAPANA-1 and INSAT-3A have recently been
decommissioned due to lack of fuel, however, the experience gained from them
have given rise to a series of meteorological satellites and associated
software; like INSAT-3D series which give continuous weather forecasting for
the country. This paper is not so much focused on the theory (widely available
in the literature) but concentrates on the implementation of operational
software.
Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions
Authors: William B. Haskell, Wenjie Huang, Huifu Xu
Date: 2018-05-17 00:00:00
Tasks: Attribute

Decision maker's preferences are often captured by some choice functions
which are used to rank prospects. In this paper, we consider ambiguity in
choice functions over a multi-attribute prospect space. Our main result is a
robust preference model where the optimal decision is based on the worst-case
choice function from an ambiguity set constructed through preference
elicitation with pairwise comparisons of prospects. Differing from existing
works in the area, our focus is on quasi-concave choice functions rather than
concave functions and this enables us to cover a wide range of utility/risk
preference problems including multi-attribute expected utility and $S$-shaped
aspirational risk preferences. The robust choice function is increasing and
quasi-concave but not necessarily translation invariant, a key property of
monetary risk measures. We propose two approaches based respectively on the
support functions and level functions of quasi-concave functions to develop
tractable formulations of the maximin preference robust optimization model. The
former gives rise to a mixed integer linear programming problem whereas the
latter is equivalent to solving a sequence of convex risk minimization
problems. To assess the effectiveness of the proposed robust preference
optimization model and numerical schemes, we apply them to a security budget
allocation problem and report some preliminary results from experiments.
Exact Sampling of Determinantal Point Processes without Eigendecomposition
Authors: Claire Launay, Bruno Galerne, Agnès Desolneux
Date: 2018-02-23 00:00:00
Tasks: Diversity, Point Processes

Determinantal point processes (DPPs) enable the modeling of repulsion: they provide diverse sets of points. The repulsion is encoded in a kernel $K$ that can be seen as a matrix storing the similarity between points. The diversity comes from the fact that the inclusion probability of a subset is equal to the determinant of a submatrice of $K$. The exact algorithm to sample DPPs uses the spectral decomposition of $K$, a computation that becomes costly when dealing with a high number of points. Here, we present an alternative exact algorithm in the discrete setting that avoids the eigenvalues and the eigenvectors computation. Instead, it relies on Cholesky decompositions. This is a two steps strategy: first, it samples a Bernoulli point process with an appropriate distribution, then it samples the target DPP distribution through a thinning procedure. Not only is the method used here innovative, but this algorithm can be competitive with the original algorithm or even faster for some applications specified here.
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
Authors: Luke Vilnis, Xiang Li, Shikhar Murty, Andrew McCallum
Date: 2018-05-17 00:00:00
Proceeding: ACL 2018 7
Tasks: Inductive Bias, Knowledge Graphs, World Knowledge

Embedding methods which enforce a partial order or lattice structure over the
concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a
natural way to model transitive relational data (e.g. entailment graphs).
However, OE learns a deterministic knowledge base, limiting expressiveness of
queries and the ability to use uncertainty for both prediction and learning
(e.g. learning from expectations). Probabilistic extensions of OE (Lai and
Hockenmaier, 2017) have provided the ability to somewhat calibrate these
denotational probabilities while retaining the consistency and inductive bias
of ordered models, but lack the ability to model the negative correlations
found in real-world knowledge. In this work we show that a broad class of
models that assign probability measures to OE can never capture negative
correlation, which motivates our construction of a novel box lattice and
accompanying probability measure to capture anticorrelation and even disjoint
concepts, while still providing the benefits of probabilistic modeling, such as
the ability to perform rich joint and conditional queries over arbitrary sets
of concepts, and both learning from and predicting calibrated uncertainty. We
show improvements over previous approaches in modeling the Flickr and WordNet
entailment graphs, and investigate the power of the model.
Contextual Search via Intrinsic Volumes
Authors: Renato Paes Leme, Jon Schneider
Date: 2018-04-09 00:00:00
Tasks: Decision Making

We study the problem of contextual search, a multidimensional generalization
of binary search that captures many problems in contextual decision-making. In
contextual search, a learner is trying to learn the value of a hidden vector $v
\in [0,1]^d$. Every round the learner is provided an adversarially-chosen
context $u_t \in \mathbb{R}^d$, submits a guess $p_t$ for the value of $\langle
u_t, v\rangle$, learns whether $p_t < \langle u_t, v\rangle$, and incurs loss
$\ell(\langle u_t, v\rangle, p_t)$ (for some loss function $\ell$). The
learner's goal is to minimize their total loss over the course of $T$ rounds.
  We present an algorithm for the contextual search problem for the symmetric
loss function $\ell(\theta, p) = |\theta - p|$ that achieves $O_{d}(1)$ total
loss. We present a new algorithm for the dynamic pricing problem (which can be
realized as a special case of the contextual search problem) that achieves
$O_{d}(\log \log T)$ total loss, improving on the previous best known upper
bounds of $O_{d}(\log T)$ and matching the known lower bounds (up to a
polynomial dependence on $d$). Both algorithms make significant use of ideas
from the field of integral geometry, most notably the notion of intrinsic
volumes of a convex set. To the best of our knowledge this is the first
application of intrinsic volumes to algorithm design.
Generative networks as inverse problems with Scattering transforms
Authors: Tomás Angles, Stéphane Mallat
Date: 2018-05-17 00:00:00
Proceeding: ICLR 2018 1

Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs)
provide impressive image generations from Gaussian white noise, but the
underlying mathematics are not well understood. We compute deep convolutional
network generators by inverting a fixed embedding operator. Therefore, they do
not require to be optimized with a discriminator or an encoder. The embedding
is Lipschitz continuous to deformations so that generators transform linear
interpolations between input white noise vectors into deformations between
output images. This embedding is computed with a wavelet Scattering transform.
Numerical experiments demonstrate that the resulting Scattering generators have
similar properties as GANs or VAEs, without learning a discriminative network
or an encoder.
Stability of Stochastic Approximations with `Controlled Markov' Noise and Temporal Difference Learning
Authors: Arunselvan Ramaswamy, Shalabh Bhatnagar
Date: 2015-04-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We are interested in understanding stability (almost sure boundedness) of
stochastic approximation algorithms (SAs) driven by a `controlled Markov'
process. Analyzing this class of algorithms is important, since many
reinforcement learning (RL) algorithms can be cast as SAs driven by a
`controlled Markov' process. In this paper, we present easily verifiable
sufficient conditions for stability and convergence of SAs driven by a
`controlled Markov' process. Many RL applications involve continuous state
spaces. While our analysis readily ensures stability for such continuous state
applications, traditional analyses do not. As compared to literature, our
analysis presents a two-fold generalization (a) the Markov process may evolve
in a continuous state space and (b) the process need not be ergodic under any
given stationary policy. Temporal difference learning (TD) is an important
policy evaluation method in reinforcement learning. The theory developed
herein, is used to analyze generalized $TD(0)$, an important variant of TD. Our
theory is also used to analyze a TD formulation of supervised learning for
forecasting problems.
Optimization of Transfer Learning for Sign Language Recognition Targeting Mobile Platform
Authors: Dhruv Rathi
Date: 2018-05-17 00:00:00
Tasks: Sign Language Recognition, Transfer Learning

The target of this research is to experiment, iterate and recommend a system
that is successful in recognition of American Sign Language (ASL). It is a
challenging as well as an interesting problem that if solved will bring a leap
in social and technological aspects alike. In this paper, we propose a
real-time recognizer of ASL based on a mobile platform, so that it will have
more accessibility and provides an ease of use. The technique implemented is
Transfer Learning of new data of Hand gestures for alphabets in ASL to be
modelled on various pre-trained high- end models and optimize the best model to
run on a mobile platform considering the various limitations of the same during
optimization. The data used consists of 27,455 images of 24 alphabets of ASL.
The optimized model when ran over a memory-efficient mobile application,
provides an accuracy of 95.03% of accurate recognition with an average
recognition time of 2.42 seconds. This method ensures considerable
discrimination in accuracy and recognition time than the previous research.
A Formulation of Recursive Self-Improvement and Its Possible Efficiency
Authors: Wenyi Wang
Date: 2018-05-17 00:00:00

Recursive self-improving (RSI) systems have been dreamed of since the early
days of computer science and artificial intelligence. However, many existing
studies on RSI systems remain philosophical, and lacks clear formulation and
results. In this paper, we provide a formal definition for one class of RSI
systems, and then demonstrate the existence of computable and efficient RSI
systems on a restricted version. We use simulation to empirically show that we
achieve logarithmic runtime complexity with respect to the size of the search
space, and these results suggest it is possible to achieve an efficient
recursive self-improvement.
Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data
Authors: Chan Woo Lee, Kyu Ye Song, Ji-Hoon Jeong, Woo Yong Choi
Date: 2018-05-17 00:00:00
Proceeding: WS 2018 7
Tasks: Emotion Recognition, Multimodal Emotion Recognition

Emotion recognition has become a popular topic of interest, especially in the
field of human computer interaction. Previous works involve unimodal analysis
of emotion, while recent efforts focus on multi-modal emotion recognition from
vision and speech. In this paper, we propose a new method of learning about the
hidden representations between just speech and text data using convolutional
attention networks. Compared to the shallow model which employs simple
concatenation of feature vectors, the proposed attention model performs much
better in classifying emotion from speech and text data contained in the
CMU-MOSEI dataset.
Accelerating Nonnegative Matrix Factorization Algorithms using Extrapolation
Authors: Andersen Man Shun Ang, Nicolas Gillis
Date: 2018-05-17 00:00:00

In this paper, we propose a general framework to accelerate significantly the
algorithms for nonnegative matrix factorization (NMF). This framework is
inspired from the extrapolation scheme used to accelerate gradient methods in
convex optimization and from the method of parallel tangents. However, the use
of extrapolation in the context of the two-block exact coordinate descent
algorithms tackling the non-convex NMF problems is novel. We illustrate the
performance of this approach on two state-of-the-art NMF algorithms, namely,
accelerated hierarchical alternating least squares (A-HALS) and alternating
nonnegative least squares (ANLS), using synthetic, image and document data
sets.
Covariance-Insured Screening
Authors: Kevin He, Jian Kang, Hyokyoung Grace Hong, Ji Zhu, Yanming Li, Huazhen Lin, Han Xu, Yi Li
Date: 2018-05-17 00:00:00

Modern bio-technologies have produced a vast amount of high-throughput data
with the number of predictors far greater than the sample size. In order to
identify more novel biomarkers and understand biological mechanisms, it is
vital to detect signals weakly associated with outcomes among
ultrahigh-dimensional predictors. However, existing screening methods, which
typically ignore correlation information, are likely to miss these weak
signals. By incorporating the inter-feature dependence, we propose a
covariance-insured screening methodology to identify predictors that are
jointly informative but only marginally weakly associated with outcomes. The
validity of the method is examined via extensive simulations and real data
studies for selecting potential genetic factors related to the onset of cancer.
FSSD: Feature Fusion Single Shot Multibox Detector
Authors: Zuoxin Li, Lu Yang, Fuqiang Zhou
Date: 2017-12-04 00:00:00
Tasks: GPU, object-detection, Object Detection

SSD (Single Shot Multibox Detector) is one of the best object detection algorithms with both high accuracy and fast speed. However, SSD's feature pyramid detection method makes it hard to fuse the features from different scales. In this paper, we proposed FSSD (Feature Fusion Single Shot Multibox Detector), an enhanced SSD with a novel and lightweight feature fusion module which can improve the performance significantly over SSD with just a little speed drop. In the feature fusion module, features from different layers with different scales are concatenated together, followed by some down-sampling blocks to generate new feature pyramid, which will be fed to multibox detectors to predict the final detection results. On the Pascal VOC 2007 test, our network can achieve 82.7 mAP (mean average precision) at the speed of 65.8 FPS (frame per second) with the input size 300$\times$300 using a single Nvidia 1080Ti GPU. In addition, our result on COCO is also better than the conventional SSD with a large margin. Our FSSD outperforms a lot of state-of-the-art object detection algorithms in both aspects of accuracy and speed. Code is available at https://github.com/lzx1413/CAFFE_SSD/tree/fssd.
Cross-Target Stance Classification with Self-Attention Networks
Authors: Chang Xu, Cecile Paris, Surya Nepal, Ross Sparks
Date: 2018-05-17 00:00:00
Proceeding: ACL 2018 7
Tasks: Classification, General Classification, Stance Classification

In stance classification, the target on which the stance is made defines the
boundary of the task, and a classifier is usually trained for prediction on the
same target. In this work, we explore the potential for generalizing
classifiers between different targets, and propose a neural model that can
apply what has been learned from a source target to a destination target. We
show that our model can find useful information shared between relevant targets
which improves generalization in certain scenarios.
Supervisory Control of Probabilistic Discrete Event Systems under Partial Observation
Authors: Weilin Deng, Jingkai Yang, Daowen Qiu
Date: 2018-05-17 00:00:00

The supervisory control of probabilistic discrete event systems (PDESs) is
investigated under the assumptions that the supervisory controller (supervisor)
is probabilistic and has a partial observation. The probabilistic P-supervisor
is defined, which specifies a probability distribution on the control patterns
for each observation. The notions of the probabilistic controllability and
observability are proposed and demonstrated to be a necessary and sufficient
conditions for the existence of the probabilistic P-supervisors. Moreover, the
polynomial verification algorithms for the probabilistic controllability and
observability are put forward. In addition, the infimal probabilistic
controllable and observable superlanguage is introduced and computed as the
solution of the optimal control problem of PDESs. Several examples are
presented to illustrate the results obtained.
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling
Authors: Kaixuan Chen, Lina Yao, Xianzhi Wang, Dalin Zhang, Tao Gu, Zhiwen Yu, Zheng Yang
Date: 2018-05-17 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, valid

Multimodal features play a key role in wearable sensor-based human activity
recognition (HAR). Selecting the most salient features adaptively is a
promising way to maximize the effectiveness of multimodal sensor data. In this
regard, we propose a "collect fully and select wisely" principle as well as an
interpretable parallel recurrent model with convolutional attentions to improve
the recognition performance. We first collect modality features and the
relations between each pair of features to generate activity frames, and then
introduce an attention mechanism to select the most prominent regions from
activity frames precisely. The selected frames not only maximize the
utilization of valid features but also reduce the number of features to be
computed effectively. We further analyze the accuracy and interpretability of
the proposed model based on extensive experiments. The results show that our
model achieves competitive performance on two benchmarked datasets and works
well in real life scenarios.
Mad Max: Affine Spline Insights into Deep Learning
Authors: Randall Balestriero, Richard Baraniuk
Date: 2018-05-17 00:00:00
Tasks: Clustering, Deep Learning, General Classification, Memorization, Quantization

We build a rigorous bridge between deep networks (DNs) and approximation
theory via spline functions and operators. Our key result is that a large class
of DNs can be written as a composition of max-affine spline operators (MASOs),
which provide a powerful portal through which to view and analyze their inner
workings. For instance, conditioned on the input signal, the output of a MASO
DN can be written as a simple affine transformation of the input. This implies
that a DN constructs a set of signal-dependent, class-specific templates
against which the signal is compared via a simple inner product; we explore the
links to the classical theory of optimal classification via matched filters and
the effects of data memorization. Going further, we propose a simple penalty
term that can be added to the cost function of any DN learning algorithm to
force the templates to be orthogonal with each other; this leads to
significantly improved classification performance and reduced overfitting with
no change to the DN architecture. The spline partition of the input signal
space that is implicitly induced by a MASO directly links DNs to the theory of
vector quantization (VQ) and $K$-means clustering, which opens up new geometric
avenue to study how DNs organize signals in a hierarchical fashion. To validate
the utility of the VQ interpretation, we develop and validate a new distance
metric for signals and images that quantifies the difference between their VQ
encodings. (This paper is a significantly expanded version of A Spline Theory
of Deep Learning from ICML 2018.)
Adversarial Patch
Authors: Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, Justin Gilmer
Date: 2017-12-27 00:00:00

We present a method to create universal, robust, targeted adversarial image
patches in the real world. The patches are universal because they can be used
to attack any scene, robust because they work under a wide variety of
transformations, and targeted because they can cause a classifier to output any
target class. These adversarial patches can be printed, added to any scene,
photographed, and presented to image classifiers; even when the patches are
small, they cause the classifiers to ignore the other items in the scene and
report a chosen target class.
  To reproduce the results from the paper, our code is available at
https://github.com/tensorflow/cleverhans/tree/master/examples/adversarial_patch
Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model
Authors: Shivashankar Subramanian, Timothy Baldwin, Trevor Cohn
Date: 2018-05-17 00:00:00
Proceeding: ACL 2018 7
Tasks: regression

Online petitions are a cost-effective way for citizens to collectively engage
with policy-makers in a democracy. Predicting the popularity of a petition ---
commonly measured by its signature count --- based on its textual content has
utility for policy-makers as well as those posting the petition. In this work,
we model this task using CNN regression with an auxiliary ordinal regression
objective. We demonstrate the effectiveness of our proposed approach using UK
and US government petition datasets.