pwc-archive/papers-with-abstracts huggingface dataset:
Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications
Authors: Benjamin D. Haeffele, Rene Vidal
Date: 2017-08-25 00:00:00
Tasks: Video Segmentation, Video Semantic Segmentation

Recently, convex formulations of low-rank matrix factorization problems have
received considerable attention in machine learning. However, such formulations
often require solving for a matrix of the size of the data matrix, making it
challenging to apply them to large scale datasets. Moreover, in many
applications the data can display structures beyond simply being low-rank,
e.g., images and videos present complex spatio-temporal structures that are
largely ignored by standard low-rank methods. In this paper we study a matrix
factorization technique that is suitable for large datasets and captures
additional structure in the factors by using a particular form of
regularization that includes well-known regularizers such as total variation
and the nuclear norm as particular cases. Although the resulting optimization
problem is non-convex, we show that if the size of the factors is large enough,
under certain conditions, any local minimizer for the factors yields a global
minimizer. A few practical algorithms are also provided to solve the matrix
factorization problem, and bounds on the distance from a given approximate
solution of the optimization problem to the global optimum are derived.
Examples in neural calcium imaging video segmentation and hyperspectral
compressed recovery show the advantages of our approach on high-dimensional
datasets.
Advanced statistical methods for eye movement analysis and modeling: a gentle introduction
Authors: Giuseppe Boccignone
Date: 2015-06-23 00:00:00
Tasks: BIG-bench Machine Learning

In this Chapter we show that by considering eye movements, and in particular,
the resulting sequence of gaze shifts, a stochastic process, a wide variety of
tools become available for analyses and modelling beyond conventional
statistical methods. Such tools encompass random walk analyses and more complex
techniques borrowed from the pattern recognition and machine learning fields.
  After a brief, though critical, probabilistic tour of current computational
models of eye movements and visual attention, we lay down the basis for gaze
shift pattern analysis. To this end, the concepts of Markov Processes, the
Wiener process and related random walks within the Gaussian framework of the
Central Limit Theorem will be introduced. Then, we will deliberately violate
fundamental assumptions of the Central Limit Theorem to elicit a larger
perspective, rooted in statistical physics, for analysing and modelling eye
movements in terms of anomalous, non-Gaussian, random walks and modern foraging
theory.
  Eventually, by resorting to machine learning techniques, we discuss how the
analyses of movement patterns can develop into the inference of hidden patterns
of the mind: inferring the observer's task, assessing cognitive impairments,
classifying expertise.
Identifying Mirror Symmetry Density with Delay in Spiking Neural Networks
Authors: Jonathan K. George, Cesare Soci, Volker J. Sorger
Date: 2017-08-25 00:00:00
Tasks: Attribute

The ability to rapidly identify symmetry and anti-symmetry is an essential
attribute of intelligence. Symmetry perception is a central process in human
vision and may be key to human 3D visualization. While previous work in
understanding neuron symmetry perception has concentrated on the neuron as an
integrator, here we show how the coincidence detecting property of the spiking
neuron can be used to reveal symmetry density in spatial data. We develop a
method for synchronizing symmetry-identifying spiking artificial neural
networks to enable layering and feedback in the network. We show a method for
building a network capable of identifying symmetry density between sets of data
and present a digital logic implementation demonstrating an 8x8
leaky-integrate-and-fire symmetry detector in a field programmable gate array.
Our results show that the efficiencies of spiking neural networks can be
harnessed to rapidly identify symmetry in spatial data with applications in
image processing, 3D computer vision, and robotics.
Accelerated Reconstruction of Perfusion-Weighted MRI Enforcing Jointly Local and Nonlocal Spatio-temporal Constraints
Authors: Cagdas Ulas, Christine Preibisch, Jonathan Sperl, Thomas Pyka, Jayashree Kalpathy-Cramer, Bjoern Menze
Date: 2017-08-25 00:00:00
Tasks: parameter estimation

Perfusion-weighted magnetic resonance imaging (MRI) is an imaging technique
that allows one to measure tissue perfusion in an organ of interest through the
injection of an intravascular paramagnetic contrast agent (CA). Due to a
preference for high temporal and spatial resolution in many applications, this
modality could significantly benefit from accelerated data acquisitions. In
this paper, we specifically address the problem of reconstructing perfusion MR
image series from a subset of k-space data. Our proposed approach is motivated
by the observation that temporal variations (dynamics) in perfusion imaging
often exhibit correlation across different spatial scales. Hence, we propose a
model that jointly penalizes the voxel-wise deviations in temporal gradient
images obtained based on a baseline, and the patch-wise dissimilarities between
the spatio-temporal neighborhoods of entire image sequence. We validate our
method on dynamic susceptibility contrast (DSC)-MRI and dynamic
contrast-enhanced (DCE)-MRI brain perfusion datasets acquired from 10 tumor
patients in total. We provide extensive analysis of reconstruction performance
and perfusion parameter estimation in comparison to state-of-the-art
reconstruction methods. Experimental results on clinical datasets demonstrate
that our reconstruction model can potentially achieve up to 8-fold acceleration
by enabling accurate estimation of perfusion parameters while preserving
spatial image details and reconstructing the complete perfusion time-intensity
curves (TICs).
Modular Learning Component Attacks: Today's Reality, Tomorrow's Challenge
Authors: Xinyang Zhang, Yujie Ji, Ting Wang
Date: 2017-08-25 00:00:00

Many of today's machine learning (ML) systems are not built from scratch, but
are compositions of an array of {\em modular learning components} (MLCs). The
increasing use of MLCs significantly simplifies the ML system development
cycles. However, as most MLCs are contributed and maintained by third parties,
their lack of standardization and regulation entails profound security
implications.
  In this paper, for the first time, we demonstrate that potentially harmful
MLCs pose immense threats to the security of ML systems. We present a broad
class of {\em logic-bomb} attacks in which maliciously crafted MLCs trigger
host systems to malfunction in a predictable manner. By empirically studying
two state-of-the-art ML systems in the healthcare domain, we explore the
feasibility of such attacks. For example, we show that, without prior knowledge
about the host ML system, by modifying only 3.3{\textperthousand} of the MLC's
parameters, each with distortion below $10^{-3}$, the adversary is able to
force the misdiagnosis of target victims' skin cancers with 100\% success rate.
We provide analytical justification for the success of such attacks, which
points to the fundamental characteristics of today's ML models: high
dimensionality, non-linearity, and non-convexity. The issue thus seems
fundamental to many ML systems. We further discuss potential countermeasures to
mitigate MLC-based attacks and their potential technical challenges.
Achieving Budget-optimality with Adaptive Schemes in Crowdsourcing
Authors: Ashish Khetan, Sewoong Oh
Date: 2016-02-10 00:00:00
Proceeding: NeurIPS 2016 12

Crowdsourcing platforms provide marketplaces where task requesters can pay to
get labels on their data. Such markets have emerged recently as popular venues
for collecting annotations that are crucial in training machine learning models
in various applications. However, as jobs are tedious and payments are low,
errors are common in such crowdsourced labels. A common strategy to overcome
such noise in the answers is to add redundancy by getting multiple answers for
each task and aggregating them using some methods such as majority voting. For
such a system, there is a fundamental question of interest: how can we maximize
the accuracy given a fixed budget on how many responses we can collect on the
crowdsourcing system. We characterize this fundamental trade-off between the
budget (how many answers the requester can collect in total) and the accuracy
in the estimated labels. In particular, we ask whether adaptive task assignment
schemes lead to a more efficient trade-off between the accuracy and the budget.
  Adaptive schemes, where tasks are assigned adaptively based on the data
collected thus far, are widely used in practical crowdsourcing systems to
efficiently use a given fixed budget. However, existing theoretical analyses of
crowdsourcing systems suggest that the gain of adaptive task assignments is
minimal. To bridge this gap, we investigate this question under a strictly more
general probabilistic model, which has been recently introduced to model
practical crowdsourced annotations. Under this generalized Dawid-Skene model,
we characterize the fundamental trade-off between budget and accuracy. We
introduce a novel adaptive scheme that matches this fundamental limit. We
further quantify the fundamental gap between adaptive and non-adaptive schemes,
by comparing the trade-off with the one for non-adaptive schemes. Our analyses
confirm that the gap is significant.
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Authors: Lantao Yu, Wei-Nan Zhang, Jun Wang, Yong Yu
Date: 2016-09-18 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Text Generation

As a new way of training generative models, Generative Adversarial Nets (GAN)
that uses a discriminative model to guide the training of the generative model
has enjoyed considerable success in generating real-valued data. However, it
has limitations when the goal is for generating sequences of discrete tokens. A
major reason lies in that the discrete outputs from the generative model make
it difficult to pass the gradient update from the discriminative model to the
generative model. Also, the discriminative model can only assess a complete
sequence, while for a partially generated sequence, it is non-trivial to
balance its current score and the future one once the entire sequence has been
generated. In this paper, we propose a sequence generation framework, called
SeqGAN, to solve the problems. Modeling the data generator as a stochastic
policy in reinforcement learning (RL), SeqGAN bypasses the generator
differentiation problem by directly performing gradient policy update. The RL
reward signal comes from the GAN discriminator judged on a complete sequence,
and is passed back to the intermediate state-action steps using Monte Carlo
search. Extensive experiments on synthetic data and real-world tasks
demonstrate significant improvements over strong baselines.
Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales
Authors: Hendrik J. Weideman, Zachary M. Jablons, Jason Holmberg, Kiirsten Flynn, John Calambokidis, Reny B. Tyson, Jason B. Allen, Randall S. Wells, Krista Hupman, Kim Urian, Charles V. Stewart
Date: 2017-08-25 00:00:00
Tasks: Image Retrieval

We address the problem of identifying individual cetaceans from images
showing the trailing edge of their fins. Given the trailing edge from an
unknown individual, we produce a ranking of known individuals from a database.
The nicks and notches along the trailing edge define an individual's unique
signature. We define a representation based on integral curvature that is
robust to changes in viewpoint and pose, and captures the pattern of nicks and
notches in a local neighborhood at multiple scales. We explore two ranking
methods that use this representation. The first uses a dynamic programming
time-warping algorithm to align two representations, and interprets the
alignment cost as a measure of similarity. This algorithm also exploits learned
spatial weights to downweight matches from regions of unstable curvature. The
second interprets the representation as a feature descriptor. Feature keypoints
are defined at the local extrema of the representation. Descriptors for the set
of known individuals are stored in a tree structure, which allows us to perform
queries given the descriptors from an unknown trailing edge. We evaluate the
top-k accuracy on two real-world datasets to demonstrate the effectiveness of
the curvature representation, achieving top-1 accuracy scores of approximately
95% and 80% for bottlenose dolphins and humpback whales, respectively.
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
Authors: Marc Tanti, Albert Gatt, Kenneth P. Camilleri
Date: 2017-08-07 00:00:00
Proceeding: WS 2017 9
Tasks: Image Captioning

In neural image captioning systems, a recurrent neural network (RNN) is
typically viewed as the primary `generation' component. This view suggests that
the image features should be `injected' into the RNN. This is in fact the
dominant view in the literature. Alternatively, the RNN can instead be viewed
as only encoding the previously generated words. This view suggests that the
RNN should only be used to encode linguistic features and that only the final
representation should be `merged' with the image features at a later stage.
This paper compares these two architectures. We find that, in general, late
merging outperforms injection, suggesting that RNNs are better viewed as
encoders, rather than generators.
Subspace Approximation for Approximate Nearest Neighbor Search in NLP
Authors: Jing Wang
Date: 2017-08-25 00:00:00
Tasks: Machine Translation, Question Answering, Retrieval

Most natural language processing tasks can be formulated as the approximated
nearest neighbor search problem, such as word analogy, document similarity,
machine translation. Take the question-answering task as an example, given a
question as the query, the goal is to search its nearest neighbor in the
training dataset as the answer. However, existing methods for approximate
nearest neighbor search problem may not perform well owing to the following
practical challenges: 1) there are noise in the data; 2) the large scale
dataset yields a huge retrieval space and high search time complexity.
  In order to solve these problems, we propose a novel approximate nearest
neighbor search framework which i) projects the data to a subspace based
spectral analysis which eliminates the influence of noise; ii) partitions the
training dataset to different groups in order to reduce the search space.
Specifically, the retrieval space is reduced from $O(n)$ to $O(\log n)$ (where
$n$ is the number of data points in the training dataset). We prove that the
retrieved nearest neighbor in the projected subspace is the same as the one in
the original feature space. We demonstrate the outstanding performance of our
framework on real-world natural language processing tasks.
Evaluation of Deep Learning on an Abstract Image Classification Dataset
Authors: Sebastian Stabinger, Antonio Rodriguez-Sanchez
Date: 2017-08-25 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification

Convolutional Neural Networks have become state of the art methods for image
classification over the last couple of years. By now they perform better than
human subjects on many of the image classification datasets. Most of these
datasets are based on the notion of concrete classes (i.e. images are
classified by the type of object in the image). In this paper we present a
novel image classification dataset, using abstract classes, which should be
easy to solve for humans, but variations of it are challenging for CNNs. The
classification performance of popular CNN architectures is evaluated on this
dataset and variations of the dataset that might be interesting for further
research are identified.
Non-FPT lower bounds for structural restrictions of decision DNNF
Authors: Andrea Calì, Florent Capelli, Igor Razgon
Date: 2017-08-25 00:00:00

We give a non-FPT lower bound on the size of structured decision DNNF and
OBDD with decomposable AND-nodes representing CNF-formulas of bounded incidence
treewidth. Both models are known to be of FPT size for CNFs of bounded primal
treewidth. To the best of our knowledge this is the first parameterized
separation of primal treewidth and incidence treewidth for knowledge
compilation models.
Areas of Attention for Image Captioning
Authors: Marco Pedersoli, Thomas Lucas, Cordelia Schmid, Jakob Verbeek
Date: 2016-12-03 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Captioning, Language Modeling, Language Modelling

We propose "Areas of Attention", a novel attention-based model for automatic
image captioning. Our approach models the dependencies between image regions,
caption words, and the state of an RNN language model, using three pairwise
interactions. In contrast to previous attention-based approaches that associate
image regions only to the RNN state, our method allows a direct association
between caption words and image regions. During training these associations are
inferred from image-level captions, akin to weakly-supervised object detector
training. These associations help to improve captioning by localizing the
corresponding regions during testing. We also propose and compare different
ways of generating attention areas: CNN activation grids, object proposals, and
spatial transformers nets applied in a convolutional fashion. Spatial
transformers give the best results. They allow for image specific attention
areas, and can be trained jointly with the rest of the network. Our attention
mechanism and spatial transformer attention areas together yield
state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic
structure in the generated captions.
Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation
Authors: Ioannis Tsamardinos, Elissavet Greasidou, Michalis Tsagris, Giorgos Borboudakis
Date: 2017-08-23 00:00:00

Cross-Validation (CV), and out-of-sample performance-estimation protocols in
general, are often employed both for (a) selecting the optimal combination of
algorithms and values of hyper-parameters (called a configuration) for
producing the final predictive model, and (b) estimating the predictive
performance of the final model. However, the cross-validated performance of the
best configuration is optimistically biased. We present an efficient bootstrap
method that corrects for the bias, called Bootstrap Bias Corrected CV (BBC-CV).
BBC-CV's main idea is to bootstrap the whole process of selecting the
best-performing configuration on the out-of-sample predictions of each
configuration, without additional training of models. In comparison to the
alternatives, namely the nested cross-validation and a method by Tibshirani and
Tibshirani, BBC-CV is computationally more efficient, has smaller variance and
bias, and is applicable to any metric of performance (accuracy, AUC,
concordance index, mean squared error). Subsequently, we employ again the idea
of bootstrapping the out-of-sample predictions to speed up the CV process.
Specifically, using a bootstrap-based hypothesis test we stop training of
models on new folds of statistically-significantly inferior configurations. We
name the method Bootstrap Corrected with Early Dropping CV (BCED-CV) that is
both efficient and provides accurate performance estimates.
Deep Structure for end-to-end inverse rendering
Authors: Shima Kamyab, Ali Ghodsi, S. Zohreh Azimifar
Date: 2017-08-25 00:00:00
Tasks: Inverse Rendering

Inverse rendering in a 3D format denoted to recovering the 3D properties of a
scene given 2D input image(s) and is typically done using 3D Morphable Model
(3DMM) based methods from single view images. These models formulate each face
as a weighted combination of some basis vectors extracted from the training
data. In this paper a deep framework is proposed in which the coefficients and
basis vectors are computed by training an autoencoder network and a
Convolutional Neural Network (CNN) simultaneously. The idea is to find a common
cause which can be mapped to both the 3D structure and corresponding 2D image
using deep networks. The empirical results verify the power of deep framework
in finding accurate 3D shapes of human faces from their corresponding 2D images
on synthetic datasets of human faces.
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
Authors: Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen
Date: 2017-02-10 00:00:00
Tasks: Quantization

This paper presents incremental network quantization (INQ), a novel method,
targeting to efficiently convert any pre-trained full-precision convolutional
neural network (CNN) model into a low-precision version whose weights are
constrained to be either powers of two or zero. Unlike existing methods which
are struggled in noticeable accuracy loss, our INQ has the potential to resolve
this issue, as benefiting from two innovations. On one hand, we introduce three
interdependent operations, namely weight partition, group-wise quantization and
re-training. A well-proven measure is employed to divide the weights in each
layer of a pre-trained CNN model into two disjoint groups. The weights in the
first group are responsible to form a low-precision base, thus they are
quantized by a variable-length encoding method. The weights in the other group
are responsible to compensate for the accuracy loss from the quantization, thus
they are the ones to be re-trained. On the other hand, these three operations
are repeated on the latest re-trained group in an iterative manner until all
the weights are converted into low-precision ones, acting as an incremental
network quantization and accuracy enhancement procedure. Extensive experiments
on the ImageNet classification task using almost all known deep CNN
architectures including AlexNet, VGG-16, GoogleNet and ResNets well testify the
efficacy of the proposed method. Specifically, at 5-bit quantization, our
models have improved accuracy than the 32-bit floating-point references. Taking
ResNet-18 as an example, we further show that our quantized models with 4-bit,
3-bit and 2-bit ternary weights have improved or very similar accuracy against
its 32-bit floating-point baseline. Besides, impressive results with the
combination of network pruning and INQ are also reported. The code is available
at https://github.com/Zhouaojun/Incremental-Network-Quantization.
Linear Differential Constraints for Photo-polarimetric Height Estimation
Authors: Silvia Tozza, William A. P. Smith, Dizhong Zhu, Ravi Ramamoorthi, Edwin R. Hancock
Date: 2017-08-25 00:00:00
Proceeding: ICCV 2017 10

In this paper we present a differential approach to photo-polarimetric shape
estimation. We propose several alternative differential constraints based on
polarisation and photometric shading information and show how to express them
in a unified partial differential system. Our method uses the image ratios
technique to combine shading and polarisation information in order to directly
reconstruct surface height, without first computing surface normal vectors.
Moreover, we are able to remove the non-linearities so that the problem reduces
to solving a linear differential problem. We also introduce a new method for
estimating a polarisation image from multichannel data and, finally, we show it
is possible to estimate the illumination directions in a two source setup,
extending the method into an uncalibrated scenario. From a numerical point of
view, we use a least-squares formulation of the discrete version of the
problem. To the best of our knowledge, this is the first work to consider a
unified differential approach to solve photo-polarimetric shape estimation
directly for height. Numerical results on synthetic and real-world data confirm
the effectiveness of our proposed method.