pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Domain Adaptation with Random Walks on Target Labelings
Authors: Twan van Laarhoven, Elena Marchiori
Date: 2017-06-16 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (DA) is used to automatize the task of
labeling data: an unlabeled dataset (target) is annotated using a labeled
dataset (source) from a related domain. We cast domain adaptation as the
problem of finding stable labels for target examples. A new definition of label
stability is proposed, motivated by a generalization error bound for large
margin linear classifiers: a target labeling is stable when, with high
probability, a classifier trained on a random subsample of the target with that
labeling yields the same labeling. We find stable labelings using a random walk
on a directed graph with transition probabilities based on labeling stability.
The majority vote of those labelings visited by the walk yields a stable label
for each target example. The resulting domain adaptation algorithm is
strikingly easy to implement and apply: It does not rely on data
transformations, which are in general computational prohibitive in the presence
of many input features, and does not need to access the source data, which is
advantageous when data sharing is restricted. By acting on the original feature
space, our method is able to take full advantage of deep features from external
pre-trained neural networks, as demonstrated by the results of our experiments.
Thermal to Visible Synthesis of Face Images using Multiple Regions
Authors: Benjamin S. Riggan, Nathaniel J. Short, Shuowen Hu
Date: 2018-03-20 00:00:00
Tasks: Face Recognition, Facial Landmark Detection, Heterogeneous Face Recognition, Image Registration

Synthesis of visible spectrum faces from thermal facial imagery is a
promising approach for heterogeneous face recognition; enabling existing face
recognition software trained on visible imagery to be leveraged, and allowing
human analysts to verify cross-spectrum matches more effectively. We propose a
new synthesis method to enhance the discriminative quality of synthesized
visible face imagery by leveraging both global (e.g., entire face) and local
regions (e.g., eyes, nose, and mouth). Here, each region provides (1) an
independent representation for the corresponding area, and (2) additional
regularization terms, which impact the overall quality of synthesized images.
We analyze the effects of using multiple regions to synthesize a visible face
image from a thermal face. We demonstrate that our approach improves
cross-spectrum verification rates over recently published synthesis approaches.
Moreover, using our synthesized imagery, we report the results on facial
landmark detection-commonly used for image registration-which is a critical
part of the face recognition process.
Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks
Authors: Cem M. Deniz, Siyuan Xiang, Spencer Hallyburton, Arakua Welbeck, James S. Babb, Stephen Honig, Kyunghyun Cho, Gregory Chang
Date: 2017-04-20 00:00:00
Tasks: Management, Segmentation

Magnetic resonance imaging (MRI) has been proposed as a complimentary method
to measure bone quality and assess fracture risk. However, manual segmentation
of MR images of bone is time-consuming, limiting the use of MRI measurements in
the clinical practice. The purpose of this paper is to present an automatic
proximal femur segmentation method that is based on deep convolutional neural
networks (CNNs). This study had institutional review board approval and written
informed consent was obtained from all subjects. A dataset of volumetric
structural MR images of the proximal femur from 86 subject were
manually-segmented by an expert. We performed experiments by training two
different CNN architectures with multiple number of initial feature maps and
layers, and tested their segmentation performance against the gold standard of
manual segmentations using four-fold cross-validation. Automatic segmentation
of the proximal femur achieved a high dice similarity score of 0.94$\pm$0.05
with precision = 0.95$\pm$0.02, and recall = 0.94$\pm$0.08 using a CNN
architecture based on 3D convolution exceeding the performance of 2D CNNs. The
high segmentation accuracy provided by CNNs has the potential to help bring the
use of structural MRI measurements of bone quality into clinical practice for
management of osteoporosis.
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination
Authors: Chang Liu, Xiaolin Wu, Xiao Shu
Date: 2018-03-05 00:00:00
Tasks: Image Enhancement, Image Restoration, Quantization, Tone Mapping

All existing image enhancement methods, such as HDR tone mapping, cannot
recover A/D quantization losses due to insufficient or excessive lighting,
(underflow and overflow problems). The loss of image details due to A/D
quantization is complete and it cannot be recovered by traditional image
processing methods, but the modern data-driven machine learning approach offers
a much needed cure to the problem. In this work we propose a novel approach to
restore and enhance images acquired in low and uneven lighting. First, the ill
illumination is algorithmically compensated by emulating the effects of
artificial supplementary lighting. Then a DCNN trained using only synthetic
data recovers the missing detail caused by quantization.
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis
Authors: Lijun Ding, Yudong Chen
Date: 2018-03-20 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

In this paper, we introduce a powerful technique based on Leave-one-out analysis to the study of low-rank matrix completion problems. Using this technique, we develop a general approach for obtaining fine-grained, entrywise bounds for iterative stochastic procedures in the presence of probabilistic dependency. We demonstrate the power of this approach in analyzing two of the most important algorithms for matrix completion: (i) the non-convex approach based on Projected Gradient Descent (PGD) for a rank-constrained formulation, also known as the Singular Value Projection algorithm, and (ii) the convex relaxation approach based on nuclear norm minimization (NNM). Using this approach, we establish the first convergence guarantee for the original form of PGD without regularization or sample splitting}, and in particular shows that it converges linearly in the infinity norm. For NNM, we use this approach to study a fictitious iterative procedure that arises in the dual analysis. Our results show that \NNM recovers an $ d $-by-$ d $ rank-$ r $ matrix with $\mathcal{O}(\mu r \log(\mu r) d \log d )$ observed entries. This bound has optimal dependence on the matrix dimension and is independent of the condition number. To the best of our knowledge, this is the first sample complexity result for a tractable matrix completion algorithm that satisfies these two properties simultaneously.
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Authors: Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth
Date: 2018-03-20 00:00:00
Tasks: Drug Design, Gaussian Processes, Meta-Learning, Meta Reinforcement Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Small Data Image Classification

Learning from small data sets is critical in many practical applications
where data collection is time consuming or expensive, e.g., robotics, animal
experiments or drug design. Meta learning is one way to increase the data
efficiency of learning algorithms by generalizing learned concepts from a set
of training tasks to unseen, but related, tasks. Often, this relationship
between tasks is hard coded or relies in some other way on human expertise. In
this paper, we frame meta learning as a hierarchical latent variable model and
infer the relationship between tasks automatically from data. We apply our
framework in a model-based reinforcement learning setting and show that our
meta-learning model effectively generalizes to novel tasks by identifying how
new tasks relate to prior ones from minimal data. This results in up to a 60%
reduction in the average interaction time needed to solve tasks compared to
strong baselines.
Learning Category-Specific Mesh Reconstruction from Image Collections
Authors: Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, Jitendra Malik
Date: 2018-03-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: Prediction

We present a learning framework for recovering the 3D shape, camera, and
texture of an object from a single image. The shape is represented as a
deformable 3D mesh model of an object category where a shape is parameterized
by a learned mean shape and per-instance predicted deformation. Our approach
allows leveraging an annotated image collection for training, where the
deformable model and the 3D prediction mechanism are learned without relying on
ground-truth 3D or multi-view supervision. Our representation enables us to go
beyond existing 3D prediction approaches by incorporating texture inference as
prediction of an image in a canonical appearance space. Additionally, we show
that semantic keypoints can be easily associated with the predicted shapes. We
present qualitative and quantitative results of our approach on CUB and
PASCAL3D datasets and show that we can learn to predict diverse shapes and
textures across objects using only annotated image collections. The project
website can be found at https://akanazawa.github.io/cmr/.
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing
Authors: Zhe Li, Xiaolong Ma, Hongjia Li, Qiyuan An, Aditya Singh Rathore, Qinru Qiu, Wenyao Xu, Yanzhi Wang
Date: 2018-03-20 00:00:00
Tasks: Action Detection, Activity Detection, Object Recognition

Increasing malicious users have sought practices to leverage 3D printing
technology to produce unlawful tools in criminal activities. Current
regulations are inadequate to deal with the rapid growth of 3D printers. It is
of vital importance to enable 3D printers to identify the objects to be
printed, so that the manufacturing procedure of an illegal weapon can be
terminated at the early stage. Deep learning yields significant rises in
performance in the object recognition tasks. However, the lack of large-scale
databases in 3D printing domain stalls the advancement of automatic illegal
weapon recognition.
  This paper presents a new 3D printing image database, namely C3PO, which
compromises two subsets for the different system working scenarios. We extract
images from the numerical control programming code files of 22 3D models, and
then categorize the images into 10 distinct labels. The first set consists of
62,200 images which represent the object projections on the three planes in a
Cartesian coordinate system. And the second sets consists of sequences of total
671,677 images to simulate the cameras' captures of the printed objects.
Importantly, we demonstrate that the weapons can be recognized in either
scenario using deep learning based approaches using our proposed database. % We
also use the trained deep models to build a prototype of object-aware 3D
printer. The quantitative results are promising, and the future exploration of
the database and the crime prevention in 3D printing are demanding tasks.
Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"?
Authors: Lilian Edwards, Michael Veale
Date: 2018-03-20 00:00:00

As concerns about unfairness and discrimination in "black box" machine
learning systems rise, a legal "right to an explanation" has emerged as a
compellingly attractive approach for challenge and redress. We outline recent
debates on the limited provisions in European data protection law, and
introduce and analyze newer explanation rights in French administrative law and
the draft modernized Council of Europe Convention 108. While individual rights
can be useful, in privacy law they have historically unreasonably burdened the
average data subject. "Meaningful information" about algorithmic logics is more
technically possible than commonly thought, but this exacerbates a new
"transparency fallacy"---an illusion of remedy rather than anything
substantively helpful. While rights-based approaches deserve a firm place in
the toolbox, other forms of governance, such as impact assessments, "soft law,"
judicial review, and model repositories deserve more attention, alongside
catalyzing agencies acting for users to control algorithmic system design.
Stacked Neural Networks for end-to-end ciliary motion analysis
Authors: Charles Lu, M. Marx, M. Zahid, C. W. Lo, C. Chennubhotla, S. P. Quinn
Date: 2018-03-20 00:00:00

Cilia are hairlike structures protruding from nearly every cell in the body.
Diseases known as ciliopathies, where cilia function is disrupted, can result
in a wide spectrum of disorders. However, most techniques for assessing ciliary
motion rely on manual identification and tracking of cilia; this process is
laborious and error-prone, and does not scale well. Even where automated
ciliary motion analysis tools exist, their applicability is limited. Here, we
propose an end-to-end computational machine learning pipeline that
automatically identifies regions of cilia from videos, extracts patches of
cilia, and classifies patients as exhibiting normal or abnormal ciliary motion.
In particular, we demonstrate how convolutional LSTM are able to encode complex
features while remaining sensitive enough to differentiate between a variety of
motion patterns. Our framework achieves 90% with only a few hundred training
epochs. We find that the combination of segmentation and classification
networks in a single pipeline yields performance comparable to existing
computational pipelines, while providing the additional benefit of an
end-to-end, fully-automated analysis toolbox for ciliary motion.
An Optimization Approach to Learning Falling Rule Lists
Authors: Chaofan Chen, Cynthia Rudin
Date: 2017-10-06 00:00:00
Tasks: Binary Classification, General Classification

A falling rule list is a probabilistic decision list for binary
classification, consisting of a series of if-then rules with antecedents in the
if clauses and probabilities of the desired outcome ("1") in the then clauses.
Just as in a regular decision list, the order of rules in a falling rule list
is important -- each example is classified by the first rule whose antecedent
it satisfies. Unlike a regular decision list, a falling rule list requires the
probabilities of the desired outcome ("1") to be monotonically decreasing down
the list. We propose an optimization approach to learning falling rule lists
and "softly" falling rule lists, along with Monte-Carlo search algorithms that
use bounds on the optimal solution to prune the search space.
Bucket Renormalization for Approximate Inference
Authors: Sungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin
Date: 2018-03-14 00:00:00
Proceeding: ICML 2018 7

Probabilistic graphical models are a key tool in machine learning
applications. Computing the partition function, i.e., normalizing constant, is
a fundamental task of statistical inference but it is generally computationally
intractable, leading to extensive study of approximation methods. Iterative
variational methods are a popular and successful family of approaches. However,
even state of the art variational methods can return poor results or fail to
converge on difficult instances. In this paper, we instead consider computing
the partition function via sequential summation over variables. We develop
robust approximate algorithms by combining ideas from mini-bucket elimination
with tensor network and renormalization group methods from statistical physics.
The resulting "convergence-free" methods show good empirical performance on
both synthetic and real-world benchmark models, even for difficult instances.
Multi-shot ASP solving with clingo
Authors: Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Torsten Schaub
Date: 2017-05-27 00:00:00

We introduce a new flexible paradigm of grounding and solving in Answer Set
Programming (ASP), which we refer to as multi-shot ASP solving, and present its
implementation in the ASP system clingo.
  Multi-shot ASP solving features grounding and solving processes that deal
with continuously changing logic programs. In doing so, they remain operative
and accommodate changes in a seamless way. For instance, such processes allow
for advanced forms of search, as in optimization or theory solving, or
interaction with an environment, as in robotics or query-answering. Common to
them is that the problem specification evolves during the reasoning process,
either because data or constraints are added, deleted, or replaced. This
evolutionary aspect adds another dimension to ASP since it brings about state
changing operations. We address this issue by providing an operational
semantics that characterizes grounding and solving processes in multi-shot ASP
solving. This characterization provides a semantic account of grounder and
solver states along with the operations manipulating them.
  The operative nature of multi-shot solving avoids redundancies in relaunching
grounder and solver programs and benefits from the solver's learning
capacities. clingo accomplishes this by complementing ASP's declarative input
language with control capacities. On the declarative side, a new directive
allows for structuring logic programs into named and parameterizable
subprograms. The grounding and integration of these subprograms into the
solving process is completely modular and fully controllable from the
procedural side. To this end, clingo offers a new application programming
interface that is conveniently accessible via scripting languages.
Copula Index for Detecting Dependence and Monotonicity between Stochastic Signals
Authors: Kiran Karra, Lamine Mili
Date: 2017-03-20 00:00:00

This paper introduces a nonparametric copula-based index for detecting the
strength and monotonicity structure of linear and nonlinear statistical
dependence between pairs of random variables or stochastic signals. Our index,
termed Copula Index for Detecting Dependence and Monotonicity (CIM), satisfies
several desirable properties of measures of association, including Renyi's
properties, the data processing inequality (DPI), and consequently
self-equitability. Synthetic data simulations reveal that the statistical power
of CIM compares favorably to other state-of-the-art measures of association
that are proven to satisfy the DPI. Simulation results with real-world data
reveal the CIM's unique ability to detect the monotonicity structure among
stochastic signals to find interesting dependencies in large datasets.
Additionally, simulations show that the CIM shows favorable performance to
estimators of mutual information when discovering Markov network structure.
Fusion of stereo and still monocular depth estimates in a self-supervised learning context
Authors: Diogo Martins, Kevin van Hecke, Guido de Croon
Date: 2018-03-20 00:00:00
Tasks: Autonomous Navigation, Depth Estimation, Self-Supervised Learning

We study how autonomous robots can learn by themselves to improve their depth
estimation capability. In particular, we investigate a self-supervised learning
setup in which stereo vision depth estimates serve as targets for a
convolutional neural network (CNN) that transforms a single still image to a
dense depth map. After training, the stereo and mono estimates are fused with a
novel fusion method that preserves high confidence stereo estimates, while
leveraging the CNN estimates in the low-confidence regions. The main
contribution of the article is that it is shown that the fused estimates lead
to a higher performance than the stereo vision estimates alone. Experiments are
performed on the KITTI dataset, and on board of a Parrot SLAMDunk, showing that
even rather limited CNNs can help provide stereo vision equipped robots with
more reliable depth maps for autonomous navigation.
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
Authors: Yishai Shimoni, Chen Yanover, Ehud Karavani, Yaara Goldschmnidt
Date: 2018-02-14 00:00:00
Tasks: Benchmarking, Causal Inference

Causal inference analysis is the estimation of the effects of actions on
outcomes. In the context of healthcare data this means estimating the outcome
of counter-factual treatments (i.e. including treatments that were not
observed) on a patient's outcome. Compared to classic machine learning methods,
evaluation and validation of causal inference analysis is more challenging
because ground truth data of counter-factual outcome can never be obtained in
any real-world scenario. Here, we present a comprehensive framework for
benchmarking algorithms that estimate causal effect. The framework includes
unlabeled data for prediction, labeled data for validation, and code for
automatic evaluation of algorithm predictions using both established and novel
metrics. The data is based on real-world covariates, and the treatment
assignments and outcomes are based on simulations, which provides the basis for
validation. In this framework we address two questions: one of scaling, and the
other of data-censoring. The framework is available as open source code at
https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework
Mysteries of Visual Experience
Authors: Jerome Feldman
Date: 2016-04-28 00:00:00

Science is a crowning glory of the human spirit and its applications remain our best hope for social progress. But there are limitations to current science and perhaps to any science. The general mind-body problem is known to be intractable and currently mysterious. This is one of many deep problems that are universally agreed to be beyond the current purview of Science, including quantum phenomena, etc. But all of these famous unsolved problems are either remote from everyday experience (entanglement, dark matter) or are hard to even define sharply (phenomenology, consciousness, etc.). An updated summary of this work has been published as: Feldman, J. (2022). Computation, perception, and mind. Behavioral and Brain Sciences, 45, E48. doi:10.1017/S0140525X21001886 A more readable, open access, version is: https://escholarship.org/uc/item/6cs78450
Dynamic Variational Autoencoders for Visual Process Modeling
Authors: Alexander Sagel, Hao Shen
Date: 2018-03-20 00:00:00

This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences. We propose a joint learning framework, combining a vector autoregressive model and Variational Autoencoders. This results in an architecture that allows Variational Autoencoders to simultaneously learn a non-linear observation as well as a linear state model from sequences of frames. We validate our approach on artificial sequences and dynamic textures.
Actor and Action Video Segmentation from a Sentence
Authors: Kirill Gavrilyuk, Amir Ghodrati, Zhenyang Li, Cees G. M. Snoek
Date: 2018-03-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Segmentation, Decoder, Referring Expression Segmentation, Segmentation, Sentence, Video Segmentation, Video Semantic Segmentation

This paper strives for pixel-level segmentation of actors and their actions
in video content. Different from existing works, which all learn to segment
from a fixed vocabulary of actor and action pairs, we infer the segmentation
from a natural language input sentence. This allows to distinguish between
fine-grained actors in the same super-category, identify actor and action
instances, and segment pairs that are outside of the actor and action
vocabulary. We propose a fully-convolutional model for pixel-level actor and
action segmentation using an encoder-decoder architecture optimized for video.
To show the potential of actor and action video segmentation from a sentence,
we extend two popular actor and action datasets with more than 7,500 natural
language descriptions. Experiments demonstrate the quality of the
sentence-guided segmentations, the generalization ability of our model, and its
advantage for traditional actor and action segmentation compared to the
state-of-the-art.