pwc-archive/papers-with-abstracts huggingface dataset:
Color Constancy by Learning to Predict Chromaticity from Luminance
Authors: Ayan Chakrabarti
Date: 2015-06-06 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Color Constancy

Color constancy is the recovery of true surface color from observed color,
and requires estimating the chromaticity of scene illumination to correct for
the bias it induces. In this paper, we show that the per-pixel color statistics
of natural scenes---without any spatial or semantic context---can by themselves
be a powerful cue for color constancy. Specifically, we describe an illuminant
estimation method that is built around a "classifier" for identifying the true
chromaticity of a pixel given its luminance (absolute brightness across color
channels). During inference, each pixel's observed color restricts its true
chromaticity to those values that can be explained by one of a candidate set of
illuminants, and applying the classifier over these values yields a
distribution over the corresponding illuminants. A global estimate for the
scene illuminant is computed through a simple aggregation of these
distributions across all pixels. We begin by simply defining the
luminance-to-chromaticity classifier by computing empirical histograms over
discretized chromaticity and luminance values from a training set of natural
images. These histograms reflect a preference for hues corresponding to smooth
reflectance functions, and for achromatic colors in brighter pixels. Despite
its simplicity, the resulting estimation algorithm outperforms current
state-of-the-art color constancy methods. Next, we propose a method to learn
the luminance-to-chromaticity classifier "end-to-end". Using stochastic
gradient descent, we set chromaticity-luminance likelihoods to minimize errors
in the final scene illuminant estimates on a training set. This leads to
further improvements in accuracy, most significantly in the tail of the error
distribution.
Fast ConvNets Using Group-wise Brain Damage
Authors: Vadim Lebedev, Victor Lempitsky
Date: 2015-06-08 00:00:00
Proceeding: CVPR 2016 6

We revisit the idea of brain damage, i.e. the pruning of the coefficients of
a neural network, and suggest how brain damage can be modified and used to
speedup convolutional layers. The approach uses the fact that many efficient
implementations reduce generalized convolutions to matrix multiplications. The
suggested brain damage process prunes the convolutional kernel tensor in a
group-wise fashion by adding group-sparsity regularization to the standard
training process. After such group-wise pruning, convolutions can be reduced to
multiplications of thinned dense matrices, which leads to speedup. In the
comparison on AlexNet, the method achieves very competitive performance.
Holographic Embeddings of Knowledge Graphs
Authors: Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio
Date: 2015-10-16 00:00:00
Tasks: Knowledge Graphs, Link Prediction, Relational Reasoning

Learning embeddings of entities and relations is an efficient and versatile
method to perform machine learning on relational data such as knowledge graphs.
In this work, we propose holographic embeddings (HolE) to learn compositional
vector space representations of entire knowledge graphs. The proposed method is
related to holographic models of associative memory in that it employs circular
correlation to create compositional representations. By using correlation as
the compositional operator HolE can capture rich interactions but
simultaneously remains efficient to compute, easy to train, and scalable to
very large datasets. In extensive experiments we show that holographic
embeddings are able to outperform state-of-the-art methods for link prediction
in knowledge graphs and relational learning benchmark datasets.
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Authors: Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox
Date: 2015-12-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: Disparity Estimation, Optical Flow Estimation, Scene Flow Estimation

Recent work has shown that optical flow estimation can be formulated as a
supervised learning task and can be successfully solved with convolutional
networks. Training of the so-called FlowNet was enabled by a large
synthetically generated dataset. The present paper extends the concept of
optical flow estimation via convolutional networks to disparity and scene flow
estimation. To this end, we propose three synthetic stereo video datasets with
sufficient realism, variation, and size to successfully train large networks.
Our datasets are the first large-scale datasets to enable training and
evaluating scene flow methods. Besides the datasets, we present a convolutional
network for real-time disparity estimation that provides state-of-the-art
results. By combining a flow and disparity estimation network and training it
jointly, we demonstrate the first scene flow estimation with a convolutional
network.
In-situ multi-scattering tomography
Authors: Vadim Holodovsky, Yoav Y. Schechner, Anat Levin, Aviad Levis, Amit Aides
Date: 2015-12-07 00:00:00
Tasks: Object

To recover the three dimensional (3D) volumetric distribution of matter in an
object, images of the object are captured from multiple directions and
locations. Using these images tomographic computations extract the
distribution. In highly scattering media and constrained, natural irradiance,
tomography must explicitly account for off-axis scattering. Furthermore, the
tomographic model and recovery must function when imaging is done in-situ, as
occurs in medical imaging and ground-based atmospheric sensing. We formulate
tomography that handles arbitrary orders of scattering, using a monte-carlo
model. Moreover, the model is highly parallelizable in our formulation. This
enables large scale rendering and recovery of volumetric scenes having a large
number of variables. We solve stability and conditioning problems that stem
from radiative transfer (RT) modeling in-situ.
Digital Genesis: Computers, Evolution and Artificial Life
Authors: Tim Taylor, Alan Dorin, Kevin Korb
Date: 2015-12-07 00:00:00
Tasks: Artificial Life

The application of evolution in the digital realm, with the goal of creating
artificial intelligence and artificial life, has a history as long as that of
the digital computer itself. We illustrate the intertwined history of these
ideas, starting with the early theoretical work of John von Neumann and the
pioneering experimental work of Nils Aall Barricelli. We argue that
evolutionary thinking and artificial life will continue to play an integral
role in the future development of the digital world.
Time-causal and time-recursive spatio-temporal receptive fields
Authors: Tony Lindeberg
Date: 2015-04-10 00:00:00

We present an improved model and theory for time-causal and time-recursive
spatio-temporal receptive fields, based on a combination of Gaussian receptive
fields over the spatial domain and first-order integrators or equivalently
truncated exponential filters coupled in cascade over the temporal domain.
  Compared to previous spatio-temporal scale-space formulations in terms of
non-enhancement of local extrema or scale invariance, these receptive fields
are based on different scale-space axiomatics over time by ensuring
non-creation of new local extrema or zero-crossings with increasing temporal
scale. Specifically, extensions are presented about (i) parameterizing the
intermediate temporal scale levels, (ii) analysing the resulting temporal
dynamics, (iii) transferring the theory to a discrete implementation, (iv)
computing scale-normalized spatio-temporal derivative expressions for
spatio-temporal feature detection and (v) computational modelling of receptive
fields in the lateral geniculate nucleus (LGN) and the primary visual cortex
(V1) in biological vision.
  We show that by distributing the intermediate temporal scale levels according
to a logarithmic distribution, we obtain much faster temporal response
properties (shorter temporal delays) compared to a uniform distribution.
Specifically, these kernels converge very rapidly to a limit kernel possessing
true self-similar scale-invariant properties over temporal scales, thereby
allowing for true scale invariance over variations in the temporal scale,
although the underlying temporal scale-space representation is based on a
discretized temporal scale parameter.
  We show how scale-normalized temporal derivatives can be defined for these
time-causal scale-space kernels and how the composed theory can be used for
computing basic types of scale-normalized spatio-temporal derivative
expressions in a computationally efficient manner.
Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method
Authors: Teng Qiu, YongJie Li
Date: 2015-12-07 00:00:00
Tasks: Clustering, Density Estimation

Most density-based clustering methods largely rely on how well the underlying
density is estimated. However, density estimation itself is also a challenging
problem, especially the determination of the kernel bandwidth. A large
bandwidth could lead to the over-smoothed density estimation in which the
number of density peaks could be less than the true clusters, while a small
bandwidth could lead to the under-smoothed density estimation in which spurious
density peaks, or called the "ripple noise", would be generated in the
estimated density. In this paper, we propose a density-based hierarchical
clustering method, called the Deep Nearest Neighbor Descent (D-NND), which
could learn the underlying density structure layer by layer and capture the
cluster structure at the same time. The over-smoothed density estimation could
be largely avoided and the negative effect of the under-estimated cases could
be also largely reduced. Overall, D-NND presents not only the strong capability
of discovering the underlying cluster structure but also the remarkable
reliability due to its insensitivity to parameters.
From rules to runs: A dynamic epistemic take on imperfect information games
Authors: Kai Li, Yanjing Wang
Date: 2015-12-07 00:00:00

In the literature of game theory, the information sets of extensive form
games have different interpretations, which may lead to confusions and
paradoxical cases. We argue that the problem lies in the mix-up of two
interpretations of the extensive form game structures: game rules or game runs
which do not always coincide. In this paper, we try to separate and connect
these two views by proposing a dynamic epistemic framework in which we can
compute the runs step by step from the game rules plus the given assumptions of
the players. We propose a modal logic to describe players' knowledge and its
change during the plays, and provide a complete axiomatization. We also show
that, under certain conditions, the mix-up of the rules and the runs is not
harmful due to the structural similarity of the two.
On The Continuous Steering of the Scale of Tight Wavelet Frames
Authors: Zsuzsanna Püspöki, John Paul Ward, Daniel Sage, Michael Unser
Date: 2015-12-07 00:00:00

In analogy with steerable wavelets, we present a general construction of
adaptable tight wavelet frames, with an emphasis on scaling operations. In
particular, the derived wavelets can be "dilated" by a procedure comparable to
the operation of steering steerable wavelets. The fundamental aspects of the
construction are the same: an admissible collection of Fourier multipliers is
used to extend a tight wavelet frame, and the "scale" of the wavelets is
adapted by scaling the multipliers. As an application, the proposed wavelets
can be used to improve the frequency localization. Importantly, the localized
frequency bands specified by this construction can be scaled efficiently using
matrix multiplication.
Piecewise Linear Activation Functions For More Efficient Deep Networks
Authors: Cheng-Yang Fu, Alexander C. Berg
Date: 2015-11-11 00:00:00

This submission has been withdrawn by arXiv administrators because it is
intentionally incomplete, which is in violation of our policies.
A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation
Authors: Vlad Olaru, Mihai Florea, Cristian Sminchisescu
Date: 2015-09-20 00:00:00
Tasks: GPU, Image Segmentation, Segmentation, Semantic Segmentation

This paper presents a framework that supports the implementation of parallel
solutions for the widespread parametric maximum flow computational routines
used in image segmentation algorithms. The framework is based on supergraphs, a
special construction combining several image graphs into a larger one, and
works on various architectures (multi-core or GPU), either locally or remotely
in a cluster of computing nodes. The framework can also be used for performance
evaluation of parallel implementations of maximum flow algorithms. We present
the case study of a state-of-the-art image segmentation algorithm based on
graph cuts, Constrained Parametric Min-Cut (CPMC), that uses the parallel
framework to solve parametric maximum flow problems, based on a GPU
implementation of the well-known push-relabel algorithm. Our results indicate
that real-time implementations based on the proposed techniques are possible.
Fast Online EM for Big Topic Modeling
Authors: Jia Zeng, Zhi-Qiang Liu, Xiao-Qin Cao
Date: 2012-10-08 00:00:00
Tasks: Scheduling

The expectation-maximization (EM) algorithm can compute the
maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the
mixture models or latent variable models such as latent Dirichlet allocation
(LDA), which has been one of the most popular probabilistic topic modeling
methods in the past decade. However, batch EM has high time and space
complexities to learn big LDA models from big data streams. In this paper, we
present a fast online EM (FOEM) algorithm that infers the topic distribution
from the previously unseen documents incrementally with constant memory
requirements. Within the stochastic approximation framework, we show that FOEM
can converge to the local stationary point of the LDA's likelihood function. By
dynamic scheduling for the fast speed and parameter streaming for the low
memory usage, FOEM is more efficient for some lifelong topic modeling tasks
than the state-of-the-art online LDA algorithms to handle both big data and big
models (aka, big topic modeling) on just a PC.
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
Authors: Bei Chen, Ning Chen, Jun Zhu, Jiaming Song, Bo Zhang
Date: 2015-12-07 00:00:00
Tasks: Bayesian Inference, Data Augmentation, Link Prediction

We present a discriminative nonparametric latent feature relational model
(LFRM) for link prediction to automatically infer the dimensionality of latent
features. Under the generic RegBayes (regularized Bayesian inference)
framework, we handily incorporate the prediction loss with probabilistic
inference of a Bayesian model; set distinct regularization parameters for
different types of links to handle the imbalance issue in real networks; and
unify the analysis of both the smooth logistic log-loss and the piecewise
linear hinge loss. For the nonconjugate posterior inference, we present a
simple Gibbs sampler via data augmentation, without making restricting
assumptions as done in variational methods. We further develop an approximate
sampler using stochastic gradient Langevin dynamics to handle large networks
with hundreds of thousands of entities and millions of links, orders of
magnitude larger than what existing LFRM models can process. Extensive studies
on various real networks show promising performance.
Scalable domain adaptation of convolutional neural networks
Authors: Adrian Popescu, Etienne Gadeski, Hervé Le Borgne
Date: 2015-12-07 00:00:00
Tasks: Domain Adaptation, Reranking

Convolutional neural networks (CNNs) tend to become a standard approach to
solve a wide array of computer vision problems. Besides important theoretical
and practical advances in their design, their success is built on the existence
of manually labeled visual resources, such as ImageNet. The creation of such
datasets is cumbersome and here we focus on alternatives to manual labeling. We
hypothesize that new resources are of uttermost importance in domains which are
not or weakly covered by ImageNet, such as tourism photographs. We first
collect noisy Flickr images for tourist points of interest and apply automatic
or weakly-supervised reranking techniques to reduce noise. Then, we learn
domain adapted models with a standard CNN architecture and compare them to a
generic model obtained from ImageNet. Experimental validation is conducted with
publicly available datasets, including Oxford5k, INRIA Holidays and Div150Cred.
Results show that low-cost domain adaptation improves results compared to the
use of generic models but also compared to strong non-CNN baselines such as
triangulation embedding.
Jointly Modeling Topics and Intents with Global Order Structure
Authors: Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang
Date: 2015-12-07 00:00:00

Modeling document structure is of great importance for discourse analysis and
related applications. The goal of this research is to capture the document
intent structure by modeling documents as a mixture of topic words and
rhetorical words. While the topics are relatively unchanged through one
document, the rhetorical functions of sentences usually change following
certain orders in discourse. We propose GMM-LDA, a topic modeling based
Bayesian unsupervised model, to analyze the document intent structure
cooperated with order information. Our model is flexible that has the ability
to combine the annotations and do supervised learning. Additionally, entropic
regularization can be introduced to model the significant divergence between
topics and intents. We perform experiments in both unsupervised and supervised
settings, results show the superiority of our model over several
state-of-the-art baselines.
A Novel Approach to Distributed Multi-Class SVM
Authors: Aruna Govada, Shree Ranjani, Aditi Viswanathan, S. K. Sahay
Date: 2015-12-07 00:00:00

With data sizes constantly expanding, and with classical machine learning
algorithms that analyze such data requiring larger and larger amounts of
computation time and storage space, the need to distribute computation and
memory requirements among several computers has become apparent. Although
substantial work has been done in developing distributed binary SVM algorithms
and multi-class SVM algorithms individually, the field of multi-class
distributed SVMs remains largely unexplored. This research proposes a novel
algorithm that implements the Support Vector Machine over a multi-class dataset
and is efficient in a distributed environment (here, Hadoop). The idea is to
divide the dataset into half recursively and thus compute the optimal Support
Vector Machine for this half during the training phase, much like a divide and
conquer approach. While testing, this structure has been effectively exploited
to significantly reduce the prediction time. Our algorithm has shown better
computation time during the prediction phase than the traditional sequential
SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of
the dataset grows. This approach also classifies the data with higher accuracy
than the traditional multi-class algorithms.
Hyperspectral Chemical Plume Detection Algorithms Based On Multidimensional Iterative Filtering Decomposition
Authors: Antonio Cicone, Jingfang Liu, Haomin Zhou
Date: 2015-12-07 00:00:00

Chemicals released in the air can be extremely dangerous for human beings and
the environment. Hyperspectral images can be used to identify chemical plumes,
however the task can be extremely challenging. Assuming we know a priori that
some chemical plume, with a known frequency spectrum, has been photographed
using a hyperspectral sensor, we can use standard techniques like the so called
matched filter or adaptive cosine estimator, plus a properly chosen threshold
value, to identify the position of the chemical plume. However, due to noise
and sensors fault, the accurate identification of chemical pixels is not easy
even in this apparently simple situation. In this paper we present a
post-processing tool that, in a completely adaptive and data driven fashion,
allows to improve the performance of any classification methods in identifying
the boundaries of a plume. This is done using the Multidimensional Iterative
Filtering (MIF) algorithm (arXiv:1411.6051, arXiv:1507.07173), which is a
non-stationary signal decomposition method like the pioneering Empirical Mode
Decomposition (EMD) method. Moreover, based on the MIF technique, we propose
also a pre-processing method that allows to decorrelate and mean-center a
hyperspectral dataset. The Cosine Similarity measure, which often fails in
practice, appears to become a successful and outperforming classifier when
equipped with such pre-processing method. We show some examples of the proposed
methods when applied to real life problems.
Light-field Microscopy with a Consumer Light-field Camera
Authors: Lois Mignard-Debise, Ivo Ihrke
Date: 2015-05-04 00:00:00
Tasks: Single Particle Analysis

We explore the use of inexpensive consumer light- field camera technology for
the purpose of light-field mi- croscopy. Our experiments are based on the Lytro
(first gen- eration) camera. Unfortunately, the optical systems of the Lytro
and those of microscopes are not compatible, lead- ing to a loss of light-field
information due to angular and spatial vignetting when directly recording
microscopic pic- tures. We therefore consider an adaptation of the Lytro op-
tical system. We demonstrate that using the Lytro directly as an oc- ular
replacement, leads to unacceptable spatial vignetting. However, we also found a
setting that allows the use of the Lytro camera in a virtual imaging mode which
prevents the information loss to a large extent. We analyze the new vir- tual
imaging mode and use it in two different setups for im- plementing light-field
microscopy using a Lytro camera. As a practical result, we show that the camera
can be used for low magnification work, as e.g. common in quality control,
surface characterization, etc. We achieve a maximum spa- tial resolution of
about 6.25{\mu}m, albeit at a limited SNR for the side views.
Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection
Authors: Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana
Date: 2015-12-07 00:00:00
Tasks: Functional Connectivity

In neuroimaging data analysis, Gaussian graphical models are often used to
model statistical dependencies across spatially remote brain regions known as
functional connectivity. Typically, data is collected across a cohort of
subjects and the scientific objectives consist of estimating population and
subject-specific graphical models. A third objective that is often overlooked
involves quantifying inter-subject variability and thus identifying regions or
sub-networks that demonstrate heterogeneity across subjects. Such information
is fundamental in order to thoroughly understand the human connectome. We
propose Mixed Neighborhood Selection in order to simultaneously address the
three aforementioned objectives. By recasting covariance selection as a
neighborhood selection problem we are able to efficiently learn the topology of
each node. We introduce an additional mixed effect component to neighborhood
selection in order to simultaneously estimate a graphical model for the
population of subjects as well as for each individual subject. The proposed
method is validated empirically through a series of simulations and applied to
resting state data for healthy subjects taken from the ABIDE consortium.