pwc-archive/papers-with-abstracts huggingface dataset:
Deep Embedding for Spatial Role Labeling
Authors: Oswaldo Ludwig, Xiao Liu, Parisa Kordjamshidi, Marie-Francine Moens
Date: 2016-03-28 00:00:00

This paper introduces the visually informed embedding of word (VIEW), a
continuous vector representation for a word extracted from a deep neural model
trained using the Microsoft COCO data set to forecast the spatial arrangements
between visual objects, given a textual description. The model is composed of a
deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory
(LSTM) network, the latter being preceded by an embedding layer. The VIEW is
applied to transferring multimodal background knowledge to Spatial Role
Labeling (SpRL) algorithms, which recognize spatial relations between objects
mentioned in the text. This work also contributes with a new method to select
complementary features and a fine-tuning method for MLP that improves the $F1$
measure in classifying the words into spatial roles. The VIEW is evaluated with
the Task 3 of SemEval-2013 benchmark data set, SpaceEval.
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm
Authors: Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
Date: 2016-02-22 00:00:00

This work provides improved guarantees for streaming principle component
analysis (PCA). Given $A_1, \ldots, A_n\in \mathbb{R}^{d\times d}$ sampled
independently from distributions satisfying $\mathbb{E}[A_i] = \Sigma$ for
$\Sigma \succeq \mathbf{0}$, this work provides an $O(d)$-space linear-time
single-pass streaming algorithm for estimating the top eigenvector of $\Sigma$.
The algorithm nearly matches (and in certain cases improves upon) the accuracy
obtained by the standard batch method that computes top eigenvector of the
empirical covariance $\frac{1}{n} \sum_{i \in [n]} A_i$ as analyzed by the
matrix Bernstein inequality. Moreover, to achieve constant accuracy, our
algorithm improves upon the best previous known sample complexities of
streaming algorithms by either a multiplicative factor of $O(d)$ or
$1/\mathrm{gap}$ where $\mathrm{gap}$ is the relative distance between the top
two eigenvalues of $\Sigma$.
  These results are achieved through a novel analysis of the classic Oja's
algorithm, one of the oldest and most popular algorithms for streaming PCA. In
particular, this work shows that simply picking a random initial point $w_0$
and applying the update rule $w_{i + 1} = w_i + \eta_i A_i w_i$ suffices to
accurately estimate the top eigenvector, with a suitable choice of $\eta_i$. We
believe our result sheds light on how to efficiently perform streaming PCA both
in theory and in practice and we hope that our analysis may serve as the basis
for analyzing many variants and extensions of streaming PCA.
Learning-based Compressive Subsampling
Authors: Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett, Baran Gözcü, Ilija Bogunovic, Volkan Cevher
Date: 2015-10-21 00:00:00
Tasks: Combinatorial Optimization

The problem of recovering a structured signal $\mathbf{x} \in \mathbb{C}^p$
from a set of dimensionality-reduced linear measurements $\mathbf{b} = \mathbf
{A}\mathbf {x}$ arises in a variety of applications, such as medical imaging,
spectroscopy, Fourier optics, and computerized tomography. Due to computational
and storage complexity or physical constraints imposed by the problem, the
measurement matrix $\mathbf{A} \in \mathbb{C}^{n \times p}$ is often of the
form $\mathbf{A} = \mathbf{P}_{\Omega}\boldsymbol{\Psi}$ for some orthonormal
basis matrix $\boldsymbol{\Psi}\in \mathbb{C}^{p \times p}$ and subsampling
operator $\mathbf{P}_{\Omega}: \mathbb{C}^{p} \rightarrow \mathbb{C}^{n}$ that
selects the rows indexed by $\Omega$. This raises the fundamental question of
how best to choose the index set $\Omega$ in order to optimize the recovery
performance. Previous approaches to addressing this question rely on
non-uniform \emph{random} subsampling using application-specific knowledge of
the structure of $\mathbf{x}$. In this paper, we instead take a principled
learning-based approach in which a \emph{fixed} index set is chosen based on a
set of training signals $\mathbf{x}_1,\dotsc,\mathbf{x}_m$. We formulate
combinatorial optimization problems seeking to maximize the energy captured in
these signals in an average-case or worst-case sense, and we show that these
can be efficiently solved either exactly or approximately via the
identification of modularity and submodularity structures. We provide both
deterministic and statistical theoretical guarantees showing how the resulting
measurement matrices perform on signals differing from the training signals,
and we provide numerical examples showing our approach to be effective on a
variety of data sets.
Provable approximation properties for deep neural networks
Authors: Uri Shaham, Alexander Cloninger, Ronald R. Coifman
Date: 2015-09-24 00:00:00

We discuss approximation of functions using deep neural nets. Given a
function $f$ on a $d$-dimensional manifold $\Gamma \subset \mathbb{R}^m$, we
construct a sparsely-connected depth-4 neural network and bound its error in
approximating $f$. The size of the network depends on dimension and curvature
of the manifold $\Gamma$, the complexity of $f$, in terms of its wavelet
description, and only weakly on the ambient dimension $m$. Essentially, our
network computes wavelet functions, which are computed from Rectified Linear
Units (ReLU)
Sparse Activity and Sparse Connectivity in Supervised Learning
Authors: Markus Thom, Günther Palm
Date: 2016-03-28 00:00:00
Tasks: Classification, General Classification, Image Classification

Sparseness is a useful regularizer for learning in a wide range of
applications, in particular in neural networks. This paper proposes a model
targeted at classification tasks, where sparse activity and sparse connectivity
are used to enhance classification capabilities. The tool for achieving this is
a sparseness-enforcing projection operator which finds the closest vector with
a pre-defined sparseness for any given vector. In the theoretical part of this
paper, a comprehensive theory for such a projection is developed. In
conclusion, it is shown that the projection is differentiable almost everywhere
and can thus be implemented as a smooth neuronal transfer function. The entire
model can hence be tuned end-to-end using gradient-based methods. Experiments
on the MNIST database of handwritten digits show that classification
performance can be boosted by sparse activity or sparse connectivity. With a
combination of both, performance can be significantly better compared to
classical non-sparse approaches.
Temporally coherent 4D reconstruction of complex dynamic scenes
Authors: Armin Mustafa, Hansung Kim, Jean-yves Guillemaut, Adrian Hilton
Date: 2016-03-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: 4D reconstruction, Camera Calibration, Scene Segmentation, Segmentation, Semantic Segmentation

This paper presents an approach for reconstruction of 4D temporally coherent
models of complex dynamic scenes. No prior knowledge is required of scene
structure or camera calibration allowing reconstruction from multiple moving
cameras. Sparse-to-dense temporal correspondence is integrated with joint
multi-view segmentation and reconstruction to obtain a complete 4D
representation of static and dynamic objects. Temporal coherence is exploited
to overcome visual ambiguities resulting in improved reconstruction of complex
scenes. Robust joint segmentation and reconstruction of dynamic objects is
achieved by introducing a geodesic star convexity constraint. Comparative
evaluation is performed on a variety of unstructured indoor and outdoor dynamic
scenes with hand-held cameras and multiple people. This demonstrates
reconstruction of complete temporally coherent 4D scene models with improved
nonrigid object segmentation and shape reconstruction.
GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation
Authors: Mishal Almazrooie, Mogana Vadiveloo, Rosni Abdullah
Date: 2016-01-01 00:00:00
Tasks: Clustering, GPU, Image Segmentation, Semantic Segmentation

In this paper, a fast and practical GPU-based implementation of Fuzzy
C-Means(FCM) clustering algorithm for image segmentation is proposed. First, an
extensive analysis is conducted to study the dependency among the image pixels
in the algorithm for parallelization. The proposed GPU-based FCM has been
tested on digital brain simulated dataset to segment white matter(WM), gray
matter(GM) and cerebrospinal fluid (CSF) soft tissue regions. The execution
time of the sequential FCM is 519 seconds for an image dataset with the size of
1MB. While the proposed GPU-based FCM requires only 2.33 seconds for the
similar size of image dataset. An estimated 245-fold speedup is measured for
the data size of 40 KB on a CUDA device that has 448 processors.
Hierarchical Gaussian Mixture Model with Objects Attached to Terminal and Non-terminal Dendrogram Nodes
Authors: Łukasz P. Olech, Mariusz Paradowski
Date: 2016-03-28 00:00:00
Tasks: Clustering

A hierarchical clustering algorithm based on Gaussian mixture model is
presented. The key difference to regular hierarchical mixture models is the
ability to store objects in both terminal and nonterminal nodes. Upper levels
of the hierarchy contain sparsely distributed objects, while lower levels
contain densely represented ones. As it was shown by experiments, this ability
helps in noise detection (modelling). Furthermore, compared to regular
hierarchical mixture model, the presented method generates more compact
dendrograms with higher quality measured by adopted F-measure.
Hierarchy of Groups Evaluation Using Different F-score Variants
Authors: Michał Spytkowski, Łukasz P. Olech, Halina Kwaśnicka
Date: 2016-03-28 00:00:00
Tasks: Clustering

The paper presents a cursory examination of clustering, focusing on a rarely
explored field of hierarchy of clusters. Based on this, a short discussion of
clustering quality measures is presented and the F-score measure is examined
more deeply. As there are no attempts to assess the quality for hierarchies of
clusters, three variants of the F-Score based index are presented: classic,
hierarchical and partial order. The partial order index is the authors'
approach to the subject. Conducted experiments show the properties of the
considered measures. In conclusions, the strong and weak sides of each variant
are presented.
Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention
Authors: Linlin Chao, Jian-Hua Tao, Minghao Yang, Ya Li, Zhengqi Wen
Date: 2016-03-28 00:00:00
Tasks: Classification, Emotion Recognition, General Classification

This paper focuses on two key problems for audio-visual emotion recognition
in the video. One is the audio and visual streams temporal alignment for
feature level fusion. The other one is locating and re-weighting the perception
attentions in the whole audio-visual stream for better recognition. The Long
Short Term Memory Recurrent Neural Network (LSTM-RNN) is employed as the main
classification architecture. Firstly, soft attention mechanism aligns the audio
and visual streams. Secondly, seven emotion embedding vectors, which are
corresponding to each classification emotion type, are added to locate the
perception attentions. The locating and re-weighting process is also based on
the soft attention mechanism. The experiment results on EmotiW2015 dataset and
the qualitative analysis show the efficiency of the proposed two techniques.
LSTM-based Deep Learning Models for Non-factoid Answer Selection
Authors: Ming Tan, Cicero Dos Santos, Bing Xiang, Bo-Wen Zhou
Date: 2015-11-12 00:00:00
Tasks: Answer Selection, Deep Learning

In this paper, we apply a general deep learning (DL) framework for the answer
selection task, which does not depend on manually defined features or
linguistic tools. The basic framework is to build the embeddings of questions
and answers based on bidirectional long short-term memory (biLSTM) models, and
measure their closeness by cosine similarity. We further extend this basic
model in two directions. One direction is to define a more composite
representation for questions and answers by combining convolutional neural
network with the basic framework. The other direction is to utilize a simple
but efficient attention mechanism in order to generate the answer
representation according to the question context. Several variations of models
are provided. The models are examined by two datasets, including TREC-QA and
InsuranceQA. Experimental results demonstrate that the proposed models
substantially outperform several strong baselines.
Non-Greedy L21-Norm Maximization for Principal Component Analysis
Authors: Feiping Nie, Heng Huang
Date: 2016-03-28 00:00:00

Principal Component Analysis (PCA) is one of the most important unsupervised
methods to handle high-dimensional data. However, due to the high computational
complexity of its eigen decomposition solution, it hard to apply PCA to the
large-scale data with high dimensionality. Meanwhile, the squared L2-norm based
objective makes it sensitive to data outliers. In recent research, the L1-norm
maximization based PCA method was proposed for efficient computation and being
robust to outliers. However, this work used a greedy strategy to solve the
eigen vectors. Moreover, the L1-norm maximization based objective may not be
the correct robust PCA formulation, because it loses the theoretical connection
to the minimization of data reconstruction error, which is one of the most
important intuitions and goals of PCA. In this paper, we propose to maximize
the L21-norm based robust PCA objective, which is theoretically connected to
the minimization of reconstruction error. More importantly, we propose the
efficient non-greedy optimization algorithms to solve our objective and the
more general L21-norm maximization problem with theoretically guaranteed
convergence. Experimental results on real world data sets show the
effectiveness of the proposed method for principal component analysis.
Towards Machine Intelligence
Authors: Kamil Rocki
Date: 2016-03-27 00:00:00

There exists a theory of a single general-purpose learning algorithm which
could explain the principles of its operation. This theory assumes that the
brain has some initial rough architecture, a small library of simple innate
circuits which are prewired at birth and proposes that all significant mental
algorithms can be learned. Given current understanding and observations, this
paper reviews and lists the ingredients of such an algorithm from both
architectural and functional perspectives.
Kernel Nonnegative Matrix Factorization Without the Curse of the Pre-image - Application to Unmixing Hyperspectral Images
Authors: Fei Zhu, Paul Honeine, Maya Kallas
Date: 2014-07-16 00:00:00
Tasks: blind source separation, Hyperspectral image analysis

The nonnegative matrix factorization (NMF) is widely used in signal and image
processing, including bio-informatics, blind source separation and
hyperspectral image analysis in remote sensing. A great challenge arises when
dealing with a nonlinear formulation of the NMF. Within the framework of kernel
machines, the models suggested in the literature do not allow the
representation of the factorization matrices, which is a fallout of the curse
of the pre-image. In this paper, we propose a novel kernel-based model for the
NMF that does not suffer from the pre-image problem, by investigating the
estimation of the factorization matrices directly in the input space. For
different kernel functions, we describe two schemes for iterative algorithms:
an additive update rule based on a gradient descent scheme and a multiplicative
update rule in the same spirit as in the Lee and Seung algorithm. Within the
proposed framework, we develop several extensions to incorporate constraints,
including sparseness, smoothness, and spatial regularization with a
total-variation-like penalty. The effectiveness of the proposed method is
demonstrated with the problem of unmixing hyperspectral images, using
well-known real images and results with state-of-the-art techniques.
DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination
Authors: Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Luc van Gool, Tinne Tuytelaars
Date: 2016-03-27 00:00:00

In this paper we are extracting surface reflectance and natural environmental
illumination from a reflectance map, i.e. from a single 2D image of a sphere of
one material under one illumination. This is a notoriously difficult problem,
yet key to various re-rendering applications. With the recent advances in
estimating reflectance maps from 2D images their further decomposition has
become increasingly relevant.
  To this end, we propose a Convolutional Neural Network (CNN) architecture to
reconstruct both material parameters (i.e. Phong) as well as illumination (i.e.
high-resolution spherical illumination maps), that is solely trained on
synthetic data. We demonstrate that decomposition of synthetic as well as real
photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the
first time, on Low Dynamic Range (LDR) as well. Results are compared to
previous approaches quantitatively as well as qualitatively in terms of
re-renderings where illumination, material, view or shape are changed.
U-CATCH: Using Color ATtribute of image patCHes in binary descriptors
Authors: Alisher Abdulkhaev, Ozgur Yilmaz
Date: 2016-03-14 00:00:00
Tasks: Attribute

In this study, we propose a simple yet very effective method for extracting color information through binary feature description framework. Our method expands the dimension of binary comparisons into RGB and YCbCr spaces, showing more than 100% matching improve ment compared to non-color binary descriptors for a wide range of hard-to-match cases. The proposed method is general and can be applied to any binary descriptor to make it color sensitive. It is faster than classical binary descriptors for RGB sampling due to the abandonment of grayscale conversion and has almost identical complexity (insignificant compared to smoothing operation) for YCbCr sampling.
Human Pose Estimation using Deep Consensus Voting
Authors: Ita Lifshitz, Ethan Fetaya, Shimon Ullman
Date: 2016-03-27 00:00:00
Tasks: Pose Estimation, Position

In this paper we consider the problem of human pose estimation from a single
still image. We propose a novel approach where each location in the image votes
for the position of each keypoint using a convolutional neural net. The voting
scheme allows us to utilize information from the whole image, rather than rely
on a sparse set of keypoint locations. Using dense, multi-target votes, not
only produces good keypoint predictions, but also enables us to compute
image-dependent joint keypoint probabilities by looking at consensus voting.
This differs from most previous methods where joint probabilities are learned
from relative keypoint locations and are independent of the image. We finally
combine the keypoints votes and joint probabilities in order to identify the
optimal pose configuration. We show our competitive performance on the MPII
Human Pose and Leeds Sports Pose datasets.
A Dual-Source Approach for 3D Pose Estimation from a Single Image
Authors: Hashim Yasin, Umar Iqbal, Björn Krüger, Andreas Weber, Juergen Gall
Date: 2015-09-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: 2D Pose Estimation, 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation, Pose Retrieval, Retrieval

One major challenge for 3D pose estimation from a single RGB image is the
acquisition of sufficient training data. In particular, collecting large
amounts of training data that contain unconstrained images and are annotated
with accurate 3D poses is infeasible. We therefore propose to use two
independent training sources. The first source consists of images with
annotated 2D poses and the second source consists of accurate 3D motion capture
data. To integrate both sources, we propose a dual-source approach that
combines 2D pose estimation with efficient and robust 3D pose retrieval. In our
experiments, we show that our approach achieves state-of-the-art results and is
even competitive when the skeleton structure of the two sources differ
substantially.
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
Authors: Ye Zhang, Stephen Roller, Byron Wallace
Date: 2016-03-03 00:00:00
Proceeding: NAACL 2016 6
Tasks: General Classification, Sentence, Sentence Classification, Word Embeddings

We introduce a novel, simple convolution neural network (CNN) architecture -
multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of
word embeddings for sentence classification. MGNC-CNN extracts features from
input embedding sets independently and then joins these at the penultimate
layer in the network to form a final feature vector. We then adopt a group
regularization strategy that differentially penalizes weights associated with
the subcomponents generated from the respective embedding sets. This model is
much simpler than comparable alternative architectures and requires
substantially less training time. Furthermore, it is flexible in that it does
not require input word embeddings to be of the same dimensionality. We show
that MGNC-CNN consistently outperforms baseline models.
Regularization Parameter Selection for a Bayesian Multi-Level Group Lasso Regression Model with Application to Imaging Genomics
Authors: Farouk S. Nathoo, Keelin Greenlaw, Mary Lesperance
Date: 2016-03-27 00:00:00
Tasks: regression

We investigate the choice of tuning parameters for a Bayesian multi-level
group lasso model developed for the joint analysis of neuroimaging and genetic
data. The regression model we consider relates multivariate phenotypes
consisting of brain summary measures (volumetric and cortical thickness values)
to single nucleotide polymorphism (SNPs) data and imposes penalization at two
nested levels, the first corresponding to genes and the second corresponding to
SNPs. Associated with each level in the penalty is a tuning parameter which
corresponds to a hyperparameter in the hierarchical Bayesian formulation.
Following previous work on Bayesian lassos we consider the estimation of tuning
parameters through either hierarchical Bayes based on hyperpriors and Gibbs
sampling or through empirical Bayes based on maximizing the marginal likelihood
using a Monte Carlo EM algorithm. For the specific model under consideration we
find that these approaches can lead to severe overshrinkage of the regression
parameter estimates in the high-dimensional setting or when the genetic effects
are weak. We demonstrate these problems through simulation examples and study
an approximation to the marginal likelihood which sheds light on the cause of
this problem. We then suggest an alternative approach based on the widely
applicable information criterion (WAIC), an asymptotic approximation to
leave-one-out cross-validation that can be computed conveniently within an MCMC
framework.