pwc-archive/papers-with-abstracts huggingface dataset:
Towards Collaborative Conceptual Exploration
Authors: Tom Hanika, Jens Zumbrägel
Date: 2017-12-23 00:00:00
Tasks: Attribute

In domains with high knowledge distribution a natural objective is to create
principle foundations for collaborative interactive learning environments. We
present a first mathematical characterization of a collaborative learning
group, a consortium, based on closure systems of attribute sets and the
well-known attribute exploration algorithm from formal concept analysis. To
this end, we introduce (weak) local experts for subdomains of a given knowledge
domain. These entities are able to refute and potentially accept a given
(implicational) query for some closure system that is a restriction of the
whole domain. On this we build up a consortial expert and show first insights
about the ability of such an expert to answer queries. Furthermore, we depict
techniques on how to cope with falsely accepted implications and on combining
counterexamples. Using notions from combinatorial design theory we further
expand those insights as far as providing first results on the decidability
problem if a given consortium is able to explore some target domain.
Applications in conceptual knowledge acquisition as well as in collaborative
interactive ontology learning are at hand.
Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects
Authors: Md Amirul Islam, Mahmoud Kalash, Neil D. B. Bruce
Date: 2018-03-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Salient object detection is a problem that has been considered in detail and
many solutions proposed. In this paper, we argue that work to date has
addressed a problem that is relatively ill-posed. Specifically, there is not
universal agreement about what constitutes a salient object when multiple
observers are queried. This implies that some objects are more likely to be
judged salient than others, and implies a relative rank exists on salient
objects. The solution presented in this paper solves this more general problem
that considers relative rank, and we propose data and metrics suitable to
measuring success in a relative object saliency landscape. A novel deep
learning solution is proposed based on a hierarchical representation of
relative saliency and stage-wise refinement. We also show that the problem of
salient object subitizing can be addressed with the same network, and our
approach exceeds performance of any prior work across all metrics considered
(both traditional and newly proposed).
A high-bias, low-variance introduction to Machine Learning for physicists
Authors: Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G. R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab
Date: 2018-03-23 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Data Visualization, Vocal Bursts Intensity Prediction

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute. (Notebooks are available at https://physics.bu.edu/~pankajm/MLnotebooks.html )
Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks
Authors: Aurelia Bustos, Antonio Pertusa
Date: 2018-03-22 00:00:00
Tasks: Representation Learning, Word Embeddings

Interventional cancer clinical trials are generally too restrictive, and some
patients are often excluded on the basis of comorbidity, past or concomitant
treatments, or the fact that they are over a certain age. The efficacy and
safety of new treatments for patients with these characteristics are,
therefore, not defined. In this work, we built a model to automatically predict
whether short clinical statements were considered inclusion or exclusion
criteria. We used protocols from cancer clinical trials that were available in
public registries from the last 18 years to train word-embeddings, and we
constructed a~dataset of 6M short free-texts labeled as eligible or not
eligible. A text classifier was trained using deep neural networks, with
pre-trained word-embeddings as inputs, to predict whether or not short
free-text statements describing clinical information were considered eligible.
We additionally analyzed the semantic reasoning of the word-embedding
representations obtained and were able to identify equivalent treatments for a
type of tumor analogous with the drugs used to treat other tumors. We show that
representation learning using {deep} neural networks can be successfully
leveraged to extract the medical knowledge from clinical trial protocols for
potentially assisting practitioners when prescribing treatments.
Black-box Testing of First-Order Logic Ontologies Using WordNet
Authors: Javier Álvez, Paqui Lucio, German Rigau
Date: 2017-05-29 00:00:00

Artificial Intelligence aims to provide computer programs with commonsense
knowledge to reason about our world. This paper offers a new practical approach
towards automated commonsense reasoning with first-order logic (FOL)
ontologies. We propose a new black-box testing methodology of FOL SUMO-based
ontologies by exploiting WordNet and its mapping into SUMO. Our proposal
includes a method for the (semi-)automatic creation of a very large benchmark
of competency questions and a procedure for its automated evaluation by using
automated theorem provers (ATPs). Applying different quality criteria, our
testing proposal enables a successful evaluation of a) the competency of
several translations of SUMO into FOL and b) the performance of various
automated ATPs. Finally, we also provide a fine-grained and complete analysis
of the commonsense reasoning competency of current FOL SUMO-based ontologies.
Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation
Authors: Weizhe Liu, Krzysztof Lis, Mathieu Salzmann, Pascal Fua
Date: 2018-03-23 00:00:00
Tasks: Density Estimation

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density in the image plane. While useful for this purpose, this image-plane density has no immediate physical meaning because it is subject to perspective distortion. This is a concern in sequences acquired by drones because the viewpoint changes often. This distortion is usually handled implicitly by either learning scale-invariant features or estimating density in patches of different sizes, neither of which accounts for the fact that scale changes must be consistent over the whole scene. In this paper, we explicitly model the scale changes and reason in terms of people per square-meter. We show that feeding the perspective model to the network allows us to enforce global scale consistency and that this model can be obtained on the fly from the drone sensors. In addition, it also enables us to enforce physically-inspired temporal consistency constraints that do not have to be learned. This yields an algorithm that outperforms state-of-the-art methods in inferring crowd density from a moving drone camera especially when perspective effects are strong.
Learning Deep Context-Network Architectures for Image Annotation
Authors: Mingyuan Jiu, Hichem Sahbi
Date: 2018-03-23 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Context plays an important role in visual pattern recognition as it provides
complementary clues for different learning tasks including image classification
and annotation. In the particular scenario of kernel learning, the general
recipe of context-based kernel design consists in learning positive
semi-definite similarity functions that return high values not only when data
share similar content but also similar context. However, in spite of having a
positive impact on performance, the use of context in these kernel design
methods has not been fully explored; indeed, context has been handcrafted
instead of being learned. In this paper, we introduce a novel context-aware
kernel design framework based on deep learning. Our method discriminatively
learns spatial geometric context as the weights of a deep network (DN). The
architecture of this network is fully determined by the solution of an
objective function that mixes content, context and regularization, while the
parameters of this network determine the most relevant (discriminant) parts of
the learned context. We apply this context and kernel learning framework to
image classification using the challenging ImageCLEF Photo Annotation
benchmark; the latter shows that our deep context learning provides highly
effective kernels for image classification as corroborated through extensive
experiments.
Generalization and Expressivity for Deep Nets
Authors: Shao-Bo Lin
Date: 2018-03-10 00:00:00
Tasks: Deep Learning, Learning Theory

Along with the rapid development of deep learning in practice, the
theoretical explanations for its success become urgent. Generalization and
expressivity are two widely used measurements to quantify theoretical behaviors
of deep learning. The expressivity focuses on finding functions expressible by
deep nets but cannot be approximated by shallow nets with the similar number of
neurons. It usually implies the large capacity. The generalization aims at
deriving fast learning rate for deep nets. It usually requires small capacity
to reduce the variance. Different from previous studies on deep learning,
pursuing either expressivity or generalization, we take both factors into
account to explore the theoretical advantages of deep nets. For this purpose,
we construct a deep net with two hidden layers possessing excellent
expressivity in terms of localized and sparse approximation. Then, utilizing
the well known covering number to measure the capacity, we find that deep nets
possess excellent expressive power (measured by localized and sparse
approximation) without enlarging the capacity of shallow nets. As a
consequence, we derive near optimal learning rates for implementing empirical
risk minimization (ERM) on the constructed deep nets. These results
theoretically exhibit the advantage of deep nets from learning theory
viewpoints.
Detecting Adversarial Perturbations with Saliency
Authors: Chiliang Zhang, Zhimou Yang, Zuochang Ye
Date: 2018-03-23 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper we propose a novel method for detecting adversarial examples by
training a binary classifier with both origin data and saliency data. In the
case of image classification model, saliency simply explain how the model make
decisions by identifying significant pixels for prediction. A model shows wrong
classification output always learns wrong features and shows wrong saliency as
well. Our approach shows good performance on detecting adversarial
perturbations. We quantitatively evaluate generalization ability of the
detector, showing that detectors trained with strong adversaries perform well
on weak adversaries.
A Deep Error Correction Network for Compressed Sensing MRI
Authors: Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley
Date: 2018-03-23 00:00:00
Tasks: compressed sensing, Diagnostic, MRI Reconstruction

Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image
sparsity properties to reconstruct MRI from very few Fourier k-space
measurements. The goal is to minimize any structural errors in the
reconstruction that could have a negative impact on its diagnostic quality. To
this end, we propose a deep error correction network (DECN) for CS-MRI. The
DECN model consists of three parts, which we refer to as modules: a guide, or
template, module, an error correction module, and a data fidelity module.
Existing CS-MRI algorithms can serve as the template module for guiding the
reconstruction. Using this template as a guide, the error correction module
learns a convolutional neural network (CNN) to map the k-space data in a way
that adjusts for the reconstruction error of the template image. Our
experimental results show the proposed DECN CS-MRI reconstruction framework can
considerably improve upon existing inversion algorithms by supplementing with
an error-correcting CNN.
MATIC: Learning Around Errors for Efficient Low-Voltage Neural Network Accelerators
Authors: Sung Kim, Patrick Howe, Thierry Moreau, Armin Alaghi, Luis Ceze, Visvesh Sathe
Date: 2017-06-14 00:00:00

As a result of the increasing demand for deep neural network (DNN)-based
services, efforts to develop dedicated hardware accelerators for DNNs are
growing rapidly. However,while accelerators with high performance and
efficiency on convolutional deep neural networks (Conv-DNNs) have been
developed, less progress has been made with regards to fully-connected DNNs
(FC-DNNs). In this paper, we propose MATIC (Memory Adaptive Training with
In-situ Canaries), a methodology that enables aggressive voltage scaling of
accelerator weight memories to improve the energy-efficiency of DNN
accelerators. To enable accurate operation with voltage overscaling, MATIC
combines the characteristics of destructive SRAM reads with the error
resilience of neural networks in a memory-adaptive training process.
Furthermore, PVT-related voltage margins are eliminated using bit-cells from
synaptic weights as in-situ canaries to track runtime environmental variation.
Demonstrated on a low-power DNN accelerator that we fabricate in 65 nm CMOS,
MATIC enables up to 60-80 mV of voltage overscaling (3.3x total energy
reduction versus the nominal voltage), or 18.6x application error reduction.
Deep Structure Inference Network for Facial Action Unit Recognition
Authors: Ciprian A. Corneanu, Meysam Madadi, Sergio Escalera
Date: 2018-03-15 00:00:00
Proceeding: ECCV 2018 9
Tasks: Facial Action Unit Detection

Facial expressions are combinations of basic components called Action Units
(AU). Recognizing AUs is key for developing general facial expression analysis.
In recent years, most efforts in automatic AU recognition have been dedicated
to learning combinations of local features and to exploiting correlations
between Action Units. In this paper, we propose a deep neural architecture that
tackles both problems by combining learned local and global features in its
initial stages and replicating a message passing algorithm between classes
similar to a graphical model inference approach in later stages. We show that
by training the model end-to-end with increased supervision we improve
state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets,
respectively.
Speeding-up Object Detection Training for Robotics with FALKON
Authors: Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale
Date: 2018-03-23 00:00:00
Tasks: object-detection, Object Detection, Region Proposal

Latest deep learning methods for object detection provide remarkable
performance, but have limits when used in robotic applications. One of the most
relevant issues is the long training time, which is due to the large size and
imbalance of the associated training sets, characterized by few positive and a
large number of negative examples (i.e. background). Proposed approaches are
based on end-to-end learning by back-propagation [22] or kernel methods trained
with Hard Negatives Mining on top of deep features [8]. These solutions are
effective, but prohibitively slow for on-line applications. In this paper we
propose a novel pipeline for object detection that overcomes this problem and
provides comparable performance, with a 60x training speedup. Our pipeline
combines (i) the Region Proposal Network and the deep feature extractor from
[22] to efficiently select candidate RoIs and encode them into powerful
representations, with (ii) the FALKON [23] algorithm, a novel kernel-based
method that allows fast training on large scale problems (millions of points).
We address the size and imbalance of training data by exploiting the stochastic
subsampling intrinsic into the method and a novel, fast, bootstrapping
approach. We assess the effectiveness of the approach on a standard Computer
Vision dataset (PASCAL VOC 2007 [5]) and demonstrate its applicability to a
real robotic scenario with the iCubWorld Transformations [18] dataset.
Memory Aware Synapses: Learning what (not) to forget
Authors: Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars
Date: 2017-11-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Lifelong learning, Object Recognition

Humans can learn in a continuous manner. Old rarely utilized knowledge can be
overwritten by new incoming information while important, frequently used
knowledge is prevented from being erased. In artificial learning systems,
lifelong learning so far has focused mainly on accumulating knowledge over
tasks and overcoming catastrophic forgetting. In this paper, we argue that,
given the limited model capacity and the unlimited new information to be
learned, knowledge has to be preserved or erased selectively. Inspired by
neuroplasticity, we propose a novel approach for lifelong learning, coined
Memory Aware Synapses (MAS). It computes the importance of the parameters of a
neural network in an unsupervised and online manner. Given a new sample which
is fed to the network, MAS accumulates an importance measure for each parameter
of the network, based on how sensitive the predicted output function is to a
change in this parameter. When learning a new task, changes to important
parameters can then be penalized, effectively preventing important knowledge
related to previous tasks from being overwritten. Further, we show an
interesting connection between a local version of our method and Hebb's
rule,which is a model for the learning process in the brain. We test our method
on a sequence of object recognition tasks and on the challenging problem of
learning an embedding for predicting $<$subject, predicate, object$>$ triplets.
We show state-of-the-art performance and, for the first time, the ability to
adapt the importance of the parameters based on unlabeled data towards what the
network needs (not) to forget, which may vary depending on test conditions.
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
Authors: Magdalini Paschali, Sailesh Conjeti, Fernando Navarro, Nassir Navab
Date: 2018-03-23 00:00:00
Tasks: Brain Segmentation, General Classification, Lesion Classification, Skin Lesion Classification

In this paper, for the first time, we propose an evaluation method for deep
learning models that assesses the performance of a model not only in an unseen
test scenario, but also in extreme cases of noise, outliers and ambiguous input
data. To this end, we utilize adversarial examples, images that fool machine
learning models, while looking imperceptibly different from original data, as a
measure to evaluate the robustness of a variety of medical imaging models.
Through extensive experiments on skin lesion classification and whole brain
segmentation with state-of-the-art networks such as Inception and UNet, we show
that models that achieve comparable performance regarding generalizability may
have significant variations in their perception of the underlying data
manifold, leading to an extensive performance gap in their robustness.
Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World
Authors: Matteo Fabbri, Fabio Lanzi, Simone Calderara, Andrea Palazzi, Roberto Vezzani, Rita Cucchiara
Date: 2018-03-22 00:00:00
Proceeding: ECCV 2018 9

Multi-People Tracking in an open-world setting requires a special effort in
precise detection. Moreover, temporal continuity in the detection phase gains
more importance when scene cluttering introduces the challenging problems of
occluded targets. For the purpose, we propose a deep network architecture that
jointly extracts people body parts and associates them across short temporal
spans. Our model explicitly deals with occluded body parts, by hallucinating
plausible solutions of not visible joints. We propose a new end-to-end
architecture composed by four branches (visible heatmaps, occluded heatmaps,
part affinity fields and temporal affinity fields) fed by a time linker feature
extractor. To overcome the lack of surveillance data with tracking, body part
and occlusion annotations we created the vastest Computer Graphics dataset for
people tracking in urban scenarios by exploiting a photorealistic videogame. It
is up to now the vastest dataset (about 500.000 frames, almost 10 million body
poses) of human body parts for people tracking in urban scenarios. Our
architecture trained on virtual data exhibits good generalization capabilities
also on public real tracking benchmarks, when image resolution and sharpness
are high enough, producing reliable tracklets useful for further batch data
association or re-id modules.
CSfM: Community-based Structure from Motion
Authors: Hainan Cui, Shuhan Shen, Xiang Gao, Zhanyi Hu
Date: 2018-03-23 00:00:00
Tasks: Computational Efficiency

Structure-from-Motion approaches could be broadly divided into two classes:
incremental and global. While incremental manner is robust to outliers, it
suffers from error accumulation and heavy computation load. The global manner
has the advantage of simultaneously estimating all camera poses, but it is
usually sensitive to epipolar geometry outliers. In this paper, we propose an
adaptive community-based SfM (CSfM) method which takes both robustness and
efficiency into consideration. First, the epipolar geometry graph is
partitioned into separate communities. Then, the reconstruction problem is
solved for each community in parallel. Finally, the reconstruction results are
merged by a novel global similarity averaging method, which solves three convex
$L1$ optimization problems. Experimental results show that our method performs
better than many of the state-of-the-art global SfM approaches in terms of
computational efficiency, while achieves similar or better reconstruction
accuracy and robustness than many of the state-of-the-art incremental SfM
approaches.