pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
Authors: Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang
Date: 2015-11-14 00:00:00
Tasks: parameter estimation, regression, valid

We study parameter estimation and asymptotic inference for sparse nonlinear
regression. More specifically, we assume the data are given by $y = f( x^\top
\beta^* ) + \epsilon$, where $f$ is nonlinear. To recover $\beta^*$, we propose
an $\ell_1$-regularized least-squares estimator. Unlike classical linear
regression, the corresponding optimization problem is nonconvex because of the
nonlinearity of $f$. In spite of the nonconvexity, we prove that under mild
conditions, every stationary point of the objective enjoys an optimal
statistical rate of convergence. In addition, we provide an efficient algorithm
that provably converges to a stationary point. We also access the uncertainty
of the obtained estimator. Specifically, based on any stationary point of the
objective, we construct valid hypothesis tests and confidence intervals for the
low dimensional components of the high-dimensional parameter $\beta^*$.
Detailed numerical results are provided to back up our theory.
Semantic Object Parsing with Local-Global Long Short-Term Memory
Authors: Xiaodan Liang, Xiaohui Shen, Donglai Xiang, Jiashi Feng, Liang Lin, Shuicheng Yan
Date: 2015-11-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Memorization, Position

Semantic object parsing is a fundamental task for understanding objects in
detail in computer vision community, where incorporating multi-level contextual
information is critical for achieving such fine-grained pixel-level
recognition. Prior methods often leverage the contextual information through
post-processing predicted confidence maps. In this work, we propose a novel
deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly
incorporate short-distance and long-distance spatial dependencies into the
feature learning over all pixel positions. In each LG-LSTM layer, local
guidance from neighboring positions and global guidance from the whole image
are imposed on each position to better exploit complex local and global
contextual information. Individual LSTMs for distinct spatial dimensions are
also utilized to intrinsically capture various spatial layouts of semantic
parts in the images, yielding distinct hidden and memory cells of each position
for each dimension. In our parsing approach, several LG-LSTM layers are stacked
and appended to the intermediate convolutional layers to directly enhance
visual features, allowing network parameters to be learned in an end-to-end
way. The long chains of sequential computation by stacked LG-LSTM layers also
enable each pixel to sense a much larger region for inference benefiting from
the memorization of previous dependencies in all positions along all
dimensions. Comprehensive evaluations on three public datasets well demonstrate
the significant superiority of our LG-LSTM over other state-of-the-art methods.
Solving Jigsaw Puzzles with Linear Programming
Authors: Rui Yu, Chris Russell, Lourdes Agapito
Date: 2015-11-13 00:00:00
Tasks: Position

We propose a novel Linear Program (LP) based formula- tion for solving jigsaw
puzzles. We formulate jigsaw solving as a set of successive global convex
relaxations of the stan- dard NP-hard formulation, that can describe both
jigsaws with pieces of unknown position and puzzles of unknown po- sition and
orientation. The main contribution and strength of our approach comes from the
LP assembly strategy. In contrast to existing greedy methods, our LP solver
exploits all the pairwise matches simultaneously, and computes the position of
each piece/component globally. The main ad- vantages of our LP approach
include: (i) a reduced sensi- tivity to local minima compared to greedy
approaches, since our successive approximations are global and convex and (ii)
an increased robustness to the presence of mismatches in the pairwise matches
due to the use of a weighted L1 penalty. To demonstrate the effectiveness of
our approach, we test our algorithm on public jigsaw datasets and show that it
outperforms state-of-the-art methods.
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
Authors: William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert van Panhuis, Eric Xing
Date: 2015-11-13 00:00:00
Tasks: Gaussian Processes

We present a scalable Gaussian process model for identifying and
characterizing smooth multidimensional changepoints, and automatically learning
changes in expressive covariance structure. We use Random Kitchen Sink features
to flexibly define a change surface in combination with expressive spectral
mixture kernels to capture the complex statistical structure. Finally, through
the use of novel methods for additive non-separable kernels, we can scale the
model to large datasets. We demonstrate the model on numerical and real world
data, including a large spatio-temporal disease dataset where we identify
previously unknown heterogeneous changes in space and time.
Deep Reflectance Maps
Authors: Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Efstratios Gavves, Tinne Tuytelaars
Date: 2015-11-13 00:00:00
Proceeding: CVPR 2016 6

Undoing the image formation process and therefore decomposing appearance into
its intrinsic properties is a challenging task due to the under-constraint
nature of this inverse problem. While significant progress has been made on
inferring shape, materials and illumination from images only, progress in an
unconstrained setting is still limited. We propose a convolutional neural
architecture to estimate reflectance maps of specular materials in natural
lighting conditions. We achieve this in an end-to-end learning formulation that
directly predicts a reflectance map from the image itself. We show how to
improve estimates by facilitating additional supervision in an indirect scheme
that first predicts surface orientation and afterwards predicts the reflectance
map by a learning-based sparse data interpolation.
  In order to analyze performance on this difficult task, we propose a new
challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg)
using both synthetic and real images. Furthermore, we show the application of
our method to a range of image-based editing tasks on real images.
Large Scale Artificial Neural Network Training Using Multi-GPUs
Authors: Linnan Wang, Wei Wu, Jianxiong Xiao, Yang Yi
Date: 2015-11-13 00:00:00
Tasks: GPU

This paper describes a method for accelerating large scale Artificial Neural
Networks (ANN) training using multi-GPUs by reducing the forward and backward
passes to matrix multiplication. We propose an out-of-core multi-GPU matrix
multiplication and integrate the algorithm with the ANN training. The
experiments demonstrate that our matrix multiplication algorithm achieves
linear speedup on multiple inhomogeneous GPUs. The full paper of this project
can be found at [1].
Standard methods for inexpensive pollen loads authentication by means of computer vision and machine learning
Authors: Manuel Chica, Pascual Campoy
Date: 2015-11-13 00:00:00
Tasks: BIG-bench Machine Learning

We present a complete methodology for authenticating local bee pollen against
fraudulent samples using image processing and machine learning techniques. The
proposed standard methods do not need expensive equipment such as advanced
microscopes and can be used for a preliminary fast rejection of unknown pollen
types. The system is able to rapidly reject the non-local pollen samples with
inexpensive hardware and without the need to send the product to the
laboratory. Methods are based on the color properties of bee pollen loads
images and the use of one-class classifiers which are appropriate to reject
unknown pollen samples when there is limited data about them. The validation of
the method is carried out by authenticating Spanish bee pollen types.
Experimentation shows that the proposed methods can obtain an overall
authentication accuracy of 94%. We finally illustrate the user interaction with
the software in some practical cases by showing the developed application
prototype.
Exploiting an Oracle that Reports AUC Scores in Machine Learning Contests
Authors: Jacob Whitehill
Date: 2015-06-03 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Object Recognition

In machine learning contests such as the ImageNet Large Scale Visual
Recognition Challenge and the KDD Cup, contestants can submit candidate
solutions and receive from an oracle (typically the organizers of the
competition) the accuracy of their guesses compared to the ground-truth labels.
One of the most commonly used accuracy metrics for binary classification tasks
is the Area Under the Receiver Operating Characteristics Curve (AUC). In this
paper we provide proofs-of-concept of how knowledge of the AUC of a set of
guesses can be used, in two different kinds of attacks, to improve the accuracy
of those guesses. On the other hand, we also demonstrate the intractability of
one kind of AUC exploit by proving that the number of possible binary labelings
of $n$ examples for which a candidate solution obtains a AUC score of $c$ grows
exponentially in $n$, for every $c\in (0,1)$.
LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
Authors: Steven C. H. Hoi, Xiongwei Wu, Hantang Liu, Yue Wu, Huiqiong Wang, Hui Xue, Qiang Wu
Date: 2015-11-08 00:00:00
Tasks: Logo Recognition, object-detection, Object Detection

Logo detection from images has many applications, particularly for brand
recognition and intellectual property protection. Most existing studies for
logo recognition and detection are based on small-scale datasets which are not
comprehensive enough when exploring emerging deep learning techniques. In this
paper, we introduce "LOGO-Net", a large-scale logo image database for logo
detection and brand recognition from real-world product images. To facilitate
research, LOGO-Net has two datasets: (i)"logos-18" consists of 18 logo classes,
10 brands, and 16,043 logo objects, and (ii) "logos-160" consists of 160 logo
classes, 100 brands, and 130,608 logo objects. We describe the ideas and
challenges for constructing such a large-scale database. Another key
contribution of this work is to apply emerging deep learning techniques for
logo detection and brand recognition tasks, and conduct extensive experiments
by exploring several state-of-the-art deep region-based convolutional networks
techniques for object detection tasks. The LOGO-net will be released at
http://logo-net.org/
Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network
Authors: Khalid Raza, Mansaf Alam
Date: 2014-08-22 00:00:00

Systems biology is an emerging interdisciplinary area of research that
focuses on study of complex interactions in a biological system, such as gene
regulatory networks. The discovery of gene regulatory networks leads to a wide
range of applications, such as pathways related to a disease that can unveil in
what way the disease acts and provide novel tentative drug targets. In
addition, the development of biological models from discovered networks or
pathways can help to predict the responses to disease and can be much useful
for the novel drug development and treatments. The inference of regulatory
networks from biological data is still in its infancy stage. This paper
proposes a recurrent neural network (RNN) based gene regulatory network (GRN)
model hybridized with generalized extended Kalman filter for weight update in
backpropagation through time training algorithm. The RNN is a complex neural
network that gives a better settlement between the biological closeness and
mathematical flexibility to model GRN. The RNN is able to capture complex,
non-linear and dynamic relationship among variables. Gene expression data are
inherently noisy and Kalman filter performs well for estimation even in noisy
data. Hence, non-linear version of Kalman filter, i.e., generalized extended
Kalman filter has been applied for weight update during network training. The
developed model has been applied on DNA SOS repair network, IRMA network, and
two synthetic networks from DREAM Challenge. We compared our results with other
state-of-the-art techniques that show superiority of our model. Further, 5%
Gaussian noise has been added in the dataset and result of the proposed model
shows negligible effect of noise on the results.
Volume-based Semantic Labeling with Signed Distance Functions
Authors: Tommaso Cavallari, Luigi Di Stefano
Date: 2015-11-13 00:00:00
Tasks: Segmentation, Semantic Segmentation, Simultaneous Localization and Mapping

Research works on the two topics of Semantic Segmentation and SLAM
(Simultaneous Localization and Mapping) have been following separate tracks.
Here, we link them quite tightly by delineating a category label fusion
technique that allows for embedding semantic information into the dense map
created by a volume-based SLAM algorithm such as KinectFusion. Accordingly, our
approach is the first to provide a semantically labeled dense reconstruction of
the environment from a stream of RGB-D images. We validate our proposal using a
publicly available semantically annotated RGB-D dataset and a) employing ground
truth labels, b) corrupting such annotations with synthetic noise, c) deploying
a state of the art semantic segmentation algorithm based on Convolutional
Neural Networks.
An Adaptive Data Representation for Robust Point-Set Registration and Merging
Authors: Dylan Campbell, Lars Petersson
Date: 2015-11-13 00:00:00
Proceeding: ICCV 2015 12

This paper presents a framework for rigid point-set registration and merging
using a robust continuous data representation. Our point-set representation is
constructed by training a one-class support vector machine with a Gaussian
radial basis function kernel and subsequently approximating the output function
with a Gaussian mixture model. We leverage the representation's sparse
parametrisation and robustness to noise, outliers and occlusions in an
efficient registration algorithm that minimises the L2 distance between our
support vector--parametrised Gaussian mixtures. In contrast, existing
techniques, such as Iterative Closest Point and Gaussian mixture approaches,
manifest a narrower region of convergence and are less robust to occlusions and
missing data, as demonstrated in the evaluation on a range of 2D and 3D
datasets. Finally, we present a novel algorithm, GMMerge, that parsimoniously
and equitably merges aligned mixture models, allowing the framework to be used
for reconstruction and mapping.
Learning to Answer Questions From Image Using Convolutional Neural Network
Authors: Lin Ma, Zhengdong Lu, Hang Li
Date: 2015-06-01 00:00:00
Tasks: General Classification, Question Answering, Sentence, Visual Question Answering (VQA)

In this paper, we propose to employ the convolutional neural network (CNN)
for the image question answering (QA). Our proposed CNN provides an end-to-end
framework with convolutional architectures for learning not only the image and
question representations, but also their inter-modal interactions to produce
the answer. More specifically, our model consists of three CNNs: one image CNN
to encode the image content, one sentence CNN to compose the words of the
question, and one multimodal convolution layer to learn their joint
representation for the classification in the space of candidate answer words.
We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA
datasets, which are two benchmark datasets for the image QA, with the
performances significantly outperforming the state-of-the-art.
Large-scale probabilistic predictors with and without guarantees of validity
Authors: Vladimir Vovk, Ivan Petej, Valentina Fedorova
Date: 2015-11-01 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: BIG-bench Machine Learning

This paper studies theoretically and empirically a method of turning
machine-learning algorithms into probabilistic predictors that automatically
enjoys a property of validity (perfect calibration) and is computationally
efficient. The price to pay for perfect calibration is that these probabilistic
predictors produce imprecise (in practice, almost precise for large data sets)
probabilities. When these imprecise probabilities are merged into precise
probabilities, the resulting predictors, while losing the theoretical property
of perfect calibration, are consistently more accurate than the existing
methods in empirical studies.
On Choosing Committees Based on Approval Votes in the Presence of Outliers
Authors: Palash Dey, Neeldhara Misra, Y. Narahari
Date: 2015-11-13 00:00:00

We study the computational complexity of committee selection problem for
several approval-based voting rules in the presence of outliers. Our first
result shows that outlier consideration makes committee selection problem
intractable for approval, net approval, and minisum approval voting rules. We
then study parameterized complexity of this problem with five natural
parameters, namely the target score, the size of the committee (and its dual
parameter, the number of candidates outside the committee), the number of
outliers (and its dual parameter, the number of non-outliers). For net approval
and minisum approval voting rules, we provide a dichotomous result, resolving
the parameterized complexity of this problem for all subsets of five natural
parameters considered (by showing either FPT or W[1]-hardness for all subsets
of parameters). For the approval voting rule, we resolve the parameterized
complexity of this problem for all subsets of parameters except one.
  We also study approximation algorithms for this problem. We show that there
does not exist any alpha(.) factor approximation algorithm for approval and net
approval voting rules, for any computable function alpha(.), unless P=NP. For
the minisum voting rule, we provide a pseudopolynomial (1+eps) factor
approximation algorithm.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
Authors: Fangyi Zhang, Jürgen Leitner, Michael Milford, Ben Upcroft, Peter Corke
Date: 2015-11-12 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a machine learning based system for controlling a
robotic manipulator with visual perception only. The capability to autonomously
learn robot controllers solely from raw-pixel images and without any prior
knowledge of configuration is shown for the first time. We build upon the
success of recent deep reinforcement learning and develop a system for learning
target reaching with a three-joint robot manipulator using external visual
observation. A Deep Q Network (DQN) was demonstrated to perform target reaching
after training in simulation. Transferring the network to real hardware and
real observation in a naive approach failed, but experiments show that the
network works when replacing camera images with synthetic images.
$k$-means: Fighting against Degeneracy in Sequential Monte Carlo with an Application to Tracking
Authors: Kai Fan, Katherine Heller
Date: 2015-11-13 00:00:00
Tasks: Clustering

For regular particle filter algorithm or Sequential Monte Carlo (SMC)
methods, the initial weights are traditionally dependent on the proposed
distribution, the posterior distribution at the current timestamp in the
sampled sequence, and the target is the posterior distribution of the previous
timestamp. This is technically correct, but leads to algorithms which usually
have practical issues with degeneracy, where all particles eventually collapse
onto a single particle. In this paper, we propose and evaluate using $k$ means
clustering to attack and even take advantage of this degeneracy. Specifically,
we propose a Stochastic SMC algorithm which initializes the set of $k$ means,
providing the initial centers chosen from the collapsed particles. To fight
against degeneracy, we adjust the regular SMC weights, mediated by cluster
proportions, and then correct them to retain the same expectation as before. We
experimentally demonstrate that our approach has better performance than
vanilla algorithms.
Deep Mean Maps
Authors: Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff Schneider
Date: 2015-11-13 00:00:00

The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision. Both of these methodologies have separately achieved a great deal of success in many computer vision tasks. However, there has been little work attempting to leverage the power of these to methodologies jointly. To this end, this paper presents the Deep Mean Maps (DMMs) framework, a novel family of methods to non-parametrically represent distributions of features in convolutional neural network models. DMMs are able to both classify images using the distribution of top-level features, and to tune the top-level features for performing this task. We show how to implement DMMs using a special mean map layer composed of typical CNN operations, making both forward and backward propagation simple. We illustrate the efficacy of DMMs at analyzing distributional patterns in image data in a synthetic data experiment. We also show that we extending existing deep architectures with DMMs improves the performance of existing CNNs on several challenging real-world datasets.
A Continuous-time Mutually-Exciting Point Process Framework for Prioritizing Events in Social Media
Authors: Mehrdad Farajtabar, Safoora Yousefi, Long Q. Tran, Le Song, Hongyuan Zha
Date: 2015-11-13 00:00:00

The overwhelming amount and rate of information update in online social media
is making it increasingly difficult for users to allocate their attention to
their topics of interest, thus there is a strong need for prioritizing news
feeds. The attractiveness of a post to a user depends on many complex
contextual and temporal features of the post. For instance, the contents of the
post, the responsiveness of a third user, and the age of the post may all have
impact. So far, these static and dynamic features has not been incorporated in
a unified framework to tackle the post prioritization problem. In this paper,
we propose a novel approach for prioritizing posts based on a feature modulated
multi-dimensional point process. Our model is able to simultaneously capture
textual and sentiment features, and temporal features such as self-excitation,
mutual-excitation and bursty nature of social interaction. As an evaluation, we
also curated a real-world conversational benchmark dataset crawled from
Facebook. In our experiments, we demonstrate that our algorithm is able to
achieve the-state-of-the-art performance in terms of analyzing, predicting, and
prioritizing events. In terms of interpretability of our method, we observe
that features indicating individual user profile and linguistic characteristics
of the events work best for prediction and prioritization of new events.