pwc-archive/papers-with-abstracts huggingface dataset:
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling
Authors: Spyros Gidaris, Nikos Komodakis
Date: 2016-12-14 00:00:00
Proceeding: CVPR 2017 7
Tasks: Disparity Estimation, Stereo Matching, Stereo Matching Hand, Structured Prediction

Pixel wise image labeling is an interesting and challenging problem with
great significance in the computer vision community. In order for a dense
labeling algorithm to be able to achieve accurate and precise results, it has
to consider the dependencies that exist in the joint space of both the input
and the output variables. An implicit approach for modeling those dependencies
is by training a deep neural network that, given as input an initial estimate
of the output labels and the input image, it will be able to predict a new
refined estimate for the labels. In this context, our work is concerned with
what is the optimal architecture for performing the label improvement task. We
argue that the prior approaches of either directly predicting new label
estimates or predicting residual corrections w.r.t. the initial labels with
feed-forward deep network architectures are sub-optimal. Instead, we propose a
generic architecture that decomposes the label improvement task to three steps:
1) detecting the initial label estimates that are incorrect, 2) replacing the
incorrect labels with new ones, and finally 3) refining the renewed labels by
predicting residual corrections w.r.t. them. Furthermore, we explore and
compare various other alternative architectures that consist of the
aforementioned Detection, Replace, and Refine components. We extensively
evaluate the examined architectures in the challenging task of dense disparity
estimation (stereo matching) and we report both quantitative and qualitative
results on three different datasets. Finally, our dense disparity estimation
network that implements the proposed generic architecture, achieves
state-of-the-art results in the KITTI 2015 test surpassing prior approaches by
a significant margin.
Scale-Free Online Learning
Authors: Francesco Orabona, Dávid Pál
Date: 2016-01-08 00:00:00

We design and analyze algorithms for online linear optimization that have
optimal regret and at the same time do not need to know any upper or lower
bounds on the norm of the loss vectors. Our algorithms are instances of the
Follow the Regularized Leader (FTRL) and Mirror Descent (MD) meta-algorithms.
We achieve adaptiveness to the norms of the loss vectors by scale invariance,
i.e., our algorithms make exactly the same decisions if the sequence of loss
vectors is multiplied by any positive constant. The algorithm based on FTRL
works for any decision set, bounded or unbounded. For unbounded decisions sets,
this is the first adaptive algorithm for online linear optimization with a
non-vacuous regret bound. In contrast, we show lower bounds on scale-free
algorithms based on MD on unbounded domains.
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network
Authors: Zeling Wu, Haoxiang Wang
Date: 2016-12-14 00:00:00
Tasks: Denoising, Super-Resolution

In this article, we propose a super-resolution method to resolve the problem
of image low spatial because of the limitation of imaging devices. We make use
of the strong non-linearity mapped ability of the back-propagation neural
networks(BPNN). Training sample images are got by undersampled method. The
elements chose as the inputs of the BPNN are pixels referred to Non-local
means(NL-Means). Making use of the self-similarity of the images, those inputs
are the pixels which are pixels gained from modified NL-means which is specific
for super-resolution. Besides, small change on core function of NL-means has
been applied in the method we use in this article so that we can have a clearer
edge in the shrunk image. Experimental results gained from the Peak Signal to
Noise Ratio(PSNR) and the Equivalent Number of Look(ENL), indicate that adding
the similar pixels as inputs will increase the results than not taking them
into consideration.
Incorporating Language Level Information into Acoustic Models
Authors: Peidong Wang, DeLiang Wang
Date: 2016-12-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper proposed a class of novel Deep Recurrent Neural Networks which can incorporate language-level information into acoustic models. For simplicity, we named these networks Recurrent Deep Language Networks (RDLNs). Multiple variants of RDLNs were considered, including two kinds of context information, two methods to process the context, and two methods to incorporate the language-level information. RDLNs provided possible methods to fine-tune the whole Automatic Speech Recognition (ASR) system in the acoustic modeling process.
Efficient phase retrieval based on dark fringe recognition with an ability of bypassing invalid fringes
Authors: Wen-Kai Yu, An-Dong Xiong, Xu-Ri Yao, Guang-Jie Zhai, Qing Zhao
Date: 2016-12-14 00:00:00
Tasks: Retrieval

This paper discusses the noisy phase retrieval problem: recovering a complex
image signal with independent noise from quadratic measurements. Inspired by
the dark fringes shown in the measured images of the array detector, a novel
phase retrieval approach is proposed and demonstrated both theoretically and
experimentally to recognize the dark fringes and bypass the invalid fringes. A
more accurate relative phase ratio between arbitrary two pixels is achieved by
calculating the multiplicative ratios (or the sum of phase difference) on the
path between them. Then the object phase image can be reconstructed precisely.
Our approach is a good choice for retrieving high-quality phase images from
noisy signals and has many potential applications in the fields such as X-ray
crystallography, diffractive imaging, and so on.
Multilingual Word Embeddings using Multigraphs
Authors: Radu Soricut, Nan Ding
Date: 2016-12-14 00:00:00
Tasks: Machine Translation, Multilingual Word Embeddings, Semantic Similarity, Semantic Textual Similarity, Translation, Word Embeddings

We present a family of neural-network--inspired models for computing
continuous word representations, specifically designed to exploit both
monolingual and multilingual text. This framework allows us to perform
unsupervised training of embeddings that exhibit higher accuracy on syntactic
and semantic compositionality, as well as multilingual semantic similarity,
compared to previous models trained in an unsupervised fashion. We also show
that such multilingual embeddings, optimized for semantic similarity, can
improve the performance of statistical machine translation with respect to how
it handles words not present in the parallel data.
VAST : The Virtual Acoustic Space Traveler Dataset
Authors: Clément Gaultier, Saurabh Kataria, Antoine Deleforge
Date: 2016-12-14 00:00:00
Tasks: Sound Source Localization

This paper introduces a new paradigm for sound source lo-calization referred
to as virtual acoustic space traveling (VAST) and presents a first dataset
designed for this purpose. Existing sound source localization methods are
either based on an approximate physical model (physics-driven) or on a
specific-purpose calibration set (data-driven). With VAST, the idea is to learn
a mapping from audio features to desired audio properties using a massive
dataset of simulated room impulse responses. This virtual dataset is designed
to be maximally representative of the potential audio scenes that the
considered system may be evolving in, while remaining reasonably compact. We
show that virtually-learned mappings on this dataset generalize to real data,
overcoming some intrinsic limitations of traditional binaural sound
localization methods based on time differences of arrival.
Unsupervised Clustering of Commercial Domains for Adaptive Machine Translation
Authors: Mauro Cettolo, Mara Chinea Rios, Roldano Cattoni
Date: 2016-12-14 00:00:00
Tasks: Clustering, Machine Translation, Translation

In this paper, we report on domain clustering in the ambit of an adaptive MT
architecture. A standard bottom-up hierarchical clustering algorithm has been
instantiated with five different distances, which have been compared, on an MT
benchmark built on 40 commercial domains, in terms of dendrograms, intrinsic
and extrinsic evaluations. The main outcome is that the most expensive distance
is also the only one able to allow the MT engine to guarantee good performance
even with few, but highly populated clusters of domains.
Tensor Decomposition for Signal Processing and Machine Learning
Authors: Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, Evangelos E. Papalexakis, Christos Faloutsos
Date: 2016-07-06 00:00:00
Tasks: BIG-bench Machine Learning, Collaborative Filtering, Tensor Decomposition

Tensors or {\em multi-way arrays} are functions of three or more indices
$(i,j,k,\cdots)$ -- similar to matrices (two-way arrays), which are functions
of two indices $(r,c)$ for (row,column). Tensors have a rich history,
stretching over almost a century, and touching upon numerous disciplines; but
they have only recently become ubiquitous in signal and data analytics at the
confluence of signal processing, statistics, data mining and machine learning.
This overview article aims to provide a good starting point for researchers and
practitioners interested in learning about and working with tensors. As such,
it focuses on fundamentals and motivation (using various application examples),
aiming to strike an appropriate balance of breadth {\em and depth} that will
enable someone having taken first graduate courses in matrix algebra and
probability to get started doing research and/or developing tensor algorithms
and software. Some background in applied optimization is useful but not
strictly required. The material covered includes tensor rank and rank
decomposition; basic tensor factorization models and their relationships and
properties (including fairly good coverage of identifiability); broad coverage
of algorithms ranging from alternating optimization to stochastic gradient;
statistical performance analysis; and applications ranging from source
separation to collaborative filtering, mixture and topic modeling,
classification, and multilinear subspace learning.
Recurrent Deep Stacking Networks for Speech Recognition
Authors: Peidong Wang, Zhongqiu Wang, DeLiang Wang
Date: 2016-12-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper presented our work on applying Recurrent Deep Stacking Networks (RDSNs) to Robust Automatic Speech Recognition (ASR) tasks. In the paper, we also proposed a more efficient yet comparable substitute to RDSN, Bi- Pass Stacking Network (BPSN). The main idea of these two models is to add phoneme-level information into acoustic models, transforming an acoustic model to the combination of an acoustic model and a phoneme-level N-gram model. Experiments showed that RDSN and BPsn can substantially improve the performances over conventional DNNs.
Stable Memory Allocation in the Hippocampus: Fundamental Limits and Neural Realization
Authors: Wenlong Mou, Zhi Wang, Li-Wei Wang
Date: 2016-12-14 00:00:00
Tasks: Hippocampus, Memorization

It is believed that hippocampus functions as a memory allocator in brain, the
mechanism of which remains unrevealed. In Valiant's neuroidal model, the
hippocampus was described as a randomly connected graph, the computation on
which maps input to a set of activated neuroids with stable size. Valiant
proposed three requirements for the hippocampal circuit to become a stable
memory allocator (SMA): stability, continuity and orthogonality. The
functionality of SMA in hippocampus is essential in further computation within
cortex, according to Valiant's model.
  In this paper, we put these requirements for memorization functions into
rigorous mathematical formulation and introduce the concept of capacity, based
on the probability of erroneous allocation. We prove fundamental limits for the
capacity and error probability of SMA, in both data-independent and
data-dependent settings. We also establish an example of stable memory
allocator that can be implemented via neuroidal circuits. Both theoretical
bounds and simulation results show that the neural SMA functions well.
UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision
Authors: Yi Zhang, Weichao Qiu, Qi Chen, Xiaolin Hu, Alan Yuille
Date: 2016-12-14 00:00:00
Tasks: Image Generation

A reliable stereo algorithm is critical for many robotics applications. But
textureless and specular regions can easily cause failure by making feature
matching difficult. Understanding whether an algorithm is robust to these
hazardous regions is important. Although many stereo benchmarks have been
developed to evaluate performance, it is hard to quantify the effect of
hazardous regions in real images because the location and severity of these
regions are unknown. In this paper, we develop a synthetic image generation
tool enabling to control hazardous factors, such as making objects more
specular or transparent, to produce hazardous regions at different degrees. The
densely controlled sampling strategy in virtual worlds enables to effectively
stress test stereo algorithms by varying the types and degrees of the hazard.
We generate a large synthetic image dataset with automatically computed
hazardous regions and analyze algorithms on these regions. The observations
from synthetic images are further validated by annotating hazardous regions in
real-world datasets Middlebury and KITTI (which gives a sparse sampling of the
hazards). Our synthetic image generation tool is based on a game engine Unreal
Engine 4 and will be open-source along with the virtual scenes in our
experiments. Many publicly available realistic game contents can be used by our
tool to provide an enormous resource for development and evaluation of
algorithms.
M-Power Regularized Least Squares Regression
Authors: Julien Audiffren, Hachem Kadri
Date: 2013-10-09 00:00:00
Tasks: regression

Regularization is used to find a solution that both fits the data and is
sufficiently smooth, and thereby is very effective for designing and refining
learning algorithms. But the influence of its exponent remains poorly
understood. In particular, it is unclear how the exponent of the reproducing
kernel Hilbert space~(RKHS) regularization term affects the accuracy and the
efficiency of kernel-based learning algorithms. Here we consider regularized
least squares regression (RLSR) with an RKHS regularization raised to the power
of m, where m is a variable real exponent. We design an efficient algorithm for
solving the associated minimization problem, we provide a theoretical analysis
of its stability, and we compare its advantage with respect to computational
complexity, speed of convergence and prediction accuracy to the classical
kernel ridge regression algorithm where the regularization exponent m is fixed
at 2. Our results show that the m-power RLSR problem can be solved efficiently,
and support the suggestion that one can use a regularization term that grows
significantly slower than the standard quadratic growth in the RKHS norm.
Neural Emoji Recommendation in Dialogue Systems
Authors: Ruobing Xie, Zhiyuan Liu, Rui Yan, Maosong Sun
Date: 2016-12-14 00:00:00
Tasks: General Classification

Emoji is an essential component in dialogues which has been broadly utilized
on almost all social platforms. It could express more delicate feelings beyond
plain texts and thus smooth the communications between users, making dialogue
systems more anthropomorphic and vivid. In this paper, we focus on
automatically recommending appropriate emojis given the contextual information
in multi-turn dialogue systems, where the challenges locate in understanding
the whole conversations. More specifically, we propose the hierarchical long
short-term memory model (H-LSTM) to construct dialogue representations,
followed by a softmax classifier for emoji classification. We evaluate our
models on the task of emoji classification in a real-world dataset, with some
further explorations on parameter sensitivity and case study. Experimental
results demonstrate that our method achieves the best performances on all
evaluation metrics. It indicates that our method could well capture the
contextual information and emotion flow in dialogues, which is significant for
emoji recommendation.
The Mehler-Fock Transform and some Applications in Texture Analysis and Color Processing
Authors: Reiner Lenz
Date: 2016-12-14 00:00:00
Tasks: Relation, Texture Classification

Many stochastic processes are defined on special geometrical objects like
spheres and cones. We describe how tools from harmonic analysis, i.e. Fourier
analysis on groups, can be used to investigate probability density functions
(pdfs) on groups and homogeneous spaces. We consider the special case of the
Lorentz group SU(1,1) and the unit disk with its hyperbolic geometry, but the
procedure can be generalized to a much wider class of Lie-groups. We mainly
concentrate on the Mehler-Fock transform which is the radial part of the
Fourier transform on the disk. Some of the characteristic features of this
transform are the relation to group-convolutions, the isometry between signal
and transform space, the relation to the Laplace-Beltrami operator and the
relation to group representation theory. We will give an overview over these
properties and their applications in signal processing. We will illustrate the
theory with two examples from low-level vision and color image processing.
Scalable Group Level Probabilistic Sparse Factor Analysis
Authors: Jesper L. Hinrich, Søren F. V. Nielsen, Nicolai A. B. Riis, Casper T. Eriksen, Jacob Frøsig, Marco D. F. Kristensen, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten Mørup
Date: 2016-12-14 00:00:00
Tasks: Experimental Design

Many data-driven approaches exist to extract neural representations of
functional magnetic resonance imaging (fMRI) data, but most of them lack a
proper probabilistic formulation. We propose a group level scalable
probabilistic sparse factor analysis (psFA) allowing spatially sparse maps,
component pruning using automatic relevance determination (ARD) and subject
specific heteroscedastic spatial noise modeling. For task-based and resting
state fMRI, we show that the sparsity constraint gives rise to components
similar to those obtained by group independent component analysis. The noise
modeling shows that noise is reduced in areas typically associated with
activation by the experimental design. The psFA model identifies sparse
components and the probabilistic setting provides a natural way to handle
parameter uncertainties. The variational Bayesian framework easily extends to
more complex noise models than the presently considered.
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
Authors: Jürgen Leitner, Adam W. Tow, Jake E. Dean, Niko Suenderhauf, Joseph W. Durham, Matthew Cooper, Markus Eich, Christopher Lehnert, Ruben Mangels, Christopher Mccool, Peter Kujala, Lachlan Nicholson, Trung Pham, James Sergeant, Liao Wu, Fangyi Zhang, Ben Upcroft, Peter Corke
Date: 2016-09-17 00:00:00

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA
Challenges are an established and important way to drive scientific progress.
They make research comparable on a well-defined benchmark with equal test
conditions for all participants. However, such challenge events occur only
occasionally, are limited to a small number of contestants, and the test
conditions are very difficult to replicate after the main event. We present a
new physical benchmark challenge for robotic picking: the ACRV Picking
Benchmark (APB). Designed to be reproducible, it consists of a set of 42 common
objects, a widely available shelf, and exact guidelines for object arrangement
using stencils. A well-defined evaluation protocol enables the comparison of
\emph{complete} robotic systems -- including perception and manipulation --
instead of sub-systems only. Our paper also describes and reports results
achieved by an open baseline system based on a Baxter robot.
Grammatical Constraints on Intra-sentential Code-Switching: From Theories to Working Models
Authors: Gayatri Bhat, Monojit Choudhury, Kalika Bali
Date: 2016-12-14 00:00:00

We make one of the first attempts to build working models for
intra-sentential code-switching based on the Equivalence-Constraint (Poplack
1980) and Matrix-Language (Myers-Scotton 1993) theories. We conduct a detailed
theoretical analysis, and a small-scale empirical study of the two models for
Hindi-English CS. Our analyses show that the models are neither sound nor
complete. Taking insights from the errors made by the models, we propose a new
model that combines features of both the theories.
A bag-of-paths framework for network data analysis
Authors: Kevin Françoisse, Ilkka Kivimäki, Amin Mantrach, Fabrice Rossi, Marco Saerens
Date: 2013-02-27 00:00:00

This work develops a generic framework, called the bag-of-paths (BoP), for
link and network data analysis. The central idea is to assign a probability
distribution on the set of all paths in a network. More precisely, a
Gibbs-Boltzmann distribution is defined over a bag of paths in a network, that
is, on a representation that considers all paths independently. We show that,
under this distribution, the probability of drawing a path connecting two nodes
can easily be computed in closed form by simple matrix inversion. This
probability captures a notion of relatedness between nodes of the graph: two
nodes are considered as highly related when they are connected by many,
preferably low-cost, paths. As an application, two families of distances
between nodes are derived from the BoP probabilities. Interestingly, the second
distance family interpolates between the shortest path distance and the
resistance distance. In addition, it extends the Bellman-Ford formula for
computing the shortest path distance in order to integrate sub-optimal paths by
simply replacing the minimum operator by the soft minimum operator.
Experimental results on semi-supervised classification show that both of the
new distance families are competitive with other state-of-the-art approaches.
In addition to the distance measures studied in this paper, the bag-of-paths
framework enables straightforward computation of many other relevant network
measures.
Permutation-equivariant neural networks applied to dynamics prediction
Authors: Nicholas Guttenberg, Nathaniel Virgo, Olaf Witkowski, Hidetoshi Aoki, Ryota Kanai
Date: 2016-12-14 00:00:00
Tasks: Prediction, Translation

The introduction of convolutional layers greatly advanced the performance of
neural networks on image tasks due to innately capturing a way of encoding and
learning translation-invariant operations, matching one of the underlying
symmetries of the image domain. In comparison, there are a number of problems
in which there are a number of different inputs which are all 'of the same
type' --- multiple particles, multiple agents, multiple stock prices, etc. The
corresponding symmetry to this is permutation symmetry, in that the algorithm
should not depend on the specific ordering of the input data. We discuss a
permutation-invariant neural network layer in analogy to convolutional layers,
and show the ability of this architecture to learn to predict the motion of a
variable number of interacting hard discs in 2D. In the same way that
convolutional layers can generalize to different image sizes, the permutation
layer we describe generalizes to different numbers of objects.