pwc-archive/papers-with-abstracts huggingface dataset:
Classifying a specific image region using convolutional nets with an ROI mask as input
Authors: Sagi Eppel
Date: 2018-12-01 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object

Convolutional neural nets (CNN) are the leading computer vision method for
classifying images. In some cases, it is desirable to classify only a specific
region of the image that corresponds to a certain object. Hence, assuming that
the region of the object in the image is known in advance and is given as a
binary region of interest (ROI) mask, the goal is to classify the object in
this region using a convolutional neural net. This goal is achieved using a
standard image classification net with the addition of a side branch, which
converts the ROI mask into an attention map. This map is then combined with the
image classification net. This allows the net to focus the attention on the
object region while still extracting contextual cues from the background. This
approach was evaluated using the COCO object dataset and the OpenSurfaces
materials dataset. In both cases, it gave superior results to methods that
completely ignore the background region. In addition, it was found that
combining the attention map at the first layer of the net gave better results
than combining it at higher layers of the net. The advantages of this method
are most apparent in the classification of small regions which demands a great
deal of contextual information from the background.
On Bi-Objective convex-quadratic problems
Authors: Cheikh Toure, Anne Auger, Dimo Brockhoff, Nikolaus Hansen
Date: 2018-12-01 00:00:00
Tasks: Sensitivity

In this paper we analyze theoretical properties of bi-objective
convex-quadratic problems. We give a complete description of their Pareto set
and prove the convexity of their Pareto front. We show that the Pareto set is a
line segment when both Hessian matrices are proportional. We then propose a
novel set of convex-quadratic test problems, describe their theoretical
properties and the algorithm abilities required by those test problems. This
includes in particular testing the sensitivity with respect to separability,
ill-conditioned problems, rotational invariance, and whether the Pareto set is
aligned with the coordinate axis.
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
Authors: Fabian Manhardt, Diego Martin Arroyo, Christian Rupprecht, Benjamin Busam, Tolga Birdal, Nassir Navab, Federico Tombari
Date: 2018-12-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Pose Estimation, valid

3D object detection and pose estimation from a single image are two inherently ambiguous problems. Oftentimes, objects appear similar from different viewpoints due to shape symmetries, occlusion and repetitive textures. This ambiguity in both detection and pose estimation means that an object instance can be perfectly described by several different poses and even classes. In this work we propose to explicitly deal with this uncertainty. For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures. The distribution collapses to a single outcome when the visual appearance uniquely identifies just one valid pose. We show the benefits of our approach which provides not only a better explanation for pose ambiguity, but also a higher accuracy in terms of pose estimation.
Learning Curriculum Policies for Reinforcement Learning
Authors: Sanmit Narvekar, Peter Stone
Date: 2018-12-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Curriculum learning in reinforcement learning is a training methodology that
seeks to speed up learning of a difficult target task, by first training on a
series of simpler tasks and transferring the knowledge acquired to the target
task. Automatically choosing a sequence of such tasks (i.e. a curriculum) is an
open problem that has been the subject of much recent work in this area. In
this paper, we build upon a recent method for curriculum design, which
formulates the curriculum sequencing problem as a Markov Decision Process. We
extend this model to handle multiple transfer learning algorithms, and show for
the first time that a curriculum policy over this MDP can be learned from
experience. We explore various representations that make this possible, and
evaluate our approach by learning curriculum policies for multiple agents in
two different domains. The results show that our method produces curricula that
can train agents to perform on a target task as fast or faster than existing
methods.
HUMBI: A Large Multiview Dataset of Human Body Expressions
Authors: Zhixuan Yu, Jae Shin Yoon, In Kyu Lee, Prashanth Venkatesh, Jaesik Park, Jihun Yu, Hyun Soo Park
Date: 2018-12-01 00:00:00
Proceeding: CVPR 2020 6

This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cameras are used to capture 772 distinctive subjects across gender, ethnicity, age, and physical condition. With the multiview image streams, we reconstruct high fidelity body expressions using 3D mesh models, which allows representing view-specific appearance using their canonical atlas. We demonstrate that HUMBI is highly effective in learning and reconstructing a complete human model and is complementary to the existing datasets of human body expressions with limited views and subjects such as MPII-Gaze, Multi-PIE, Human3.6M, and Panoptic Studio datasets.
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
Authors: Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, Amir Saffari
Date: 2018-12-01 00:00:00
Tasks: Denoising, Knowledge Graphs, Link Prediction

In this work, we provide a new formulation for Graph Convolutional Neural
Networks (GCNNs) for link prediction on graph data that addresses common
challenges for biomedical knowledge graphs (KGs). We introduce a regularized
attention mechanism to GCNNs that not only improves performance on clean
datasets, but also favorably accommodates noise in KGs, a pervasive issue in
real-world applications. Further, we explore new visualization methods for
interpretable modelling and to illustrate how the learned representation can be
exploited to automate dataset denoising. The results are demonstrated on a
synthetic dataset, the common benchmark dataset FB15k-237, and a large
biomedical knowledge graph derived from a combination of noisy and clean data
sources. Using these improvements, we visualize a learned model's
representation of the disease cystic fibrosis and demonstrate how to
interrogate a neural network to show the potential of PPARG as a candidate
therapeutic target for rheumatoid arthritis.
Cross-Modulation Networks for Few-Shot Learning
Authors: Hugo Prol, Vincent Dumoulin, Luis Herranz
Date: 2018-12-01 00:00:00
Tasks: Few-Shot Learning

A family of recent successful approaches to few-shot learning relies on
learning an embedding space in which predictions are made by computing
similarities between examples. This corresponds to combining information
between support and query examples at a very late stage of the prediction
pipeline. Inspired by this observation, we hypothesize that there may be
benefits to combining the information at various levels of abstraction along
the pipeline. We present an architecture called Cross-Modulation Networks which
allows support and query examples to interact throughout the feature extraction
process via a feature-wise modulation mechanism. We adapt the Matching Networks
architecture to take advantage of these interactions and show encouraging
initial results on miniImageNet in the 5-way, 1-shot setting, where we close
the gap with state-of-the-art.
Learning Speaker Representations with Mutual Information
Authors: Mirco Ravanelli, Yoshua Bengio
Date: 2018-12-01 00:00:00
Tasks: Sentence, Speaker Identification

Learning good representations is of crucial importance in deep learning.
Mutual Information (MI) or similar measures of statistical dependence are
promising tools for learning these representations in an unsupervised way. Even
though the mutual information between two random variables is hard to measure
directly in high dimensional spaces, some recent studies have shown that an
implicit optimization of MI can be achieved with an encoder-discriminator
architecture similar to that of Generative Adversarial Networks (GANs). In this
work, we learn representations that capture speaker identities by maximizing
the mutual information between the encoded representations of chunks of speech
randomly sampled from the same sentence. The proposed encoder relies on the
SincNet architecture and transforms raw speech waveform into a compact feature
vector. The discriminator is fed by either positive samples (of the joint
distribution of encoded chunks) or negative samples (from the product of the
marginals) and is trained to separate them. We report experiments showing that
this approach effectively learns useful speaker representations, leading to
promising results on speaker identification and verification tasks. Our
experiments consider both unsupervised and semi-supervised settings and compare
the performance achieved with different objective functions.
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL
Authors: Chun-Hao Chang, Mingjie Mai, Anna Goldenberg
Date: 2018-12-01 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Current clinical practice to monitor patients' health follows either regular
or heuristic-based lab test (e.g. blood test) scheduling. Such practice not
only gives rise to redundant measurements accruing cost, but may even lead to
unnecessary patient discomfort. From the computational perspective,
heuristic-based test scheduling might lead to reduced accuracy of clinical
forecasting models. Computationally learning an optimal clinical test
scheduling and measurement collection, is likely to lead to both, better
predictive models and patient outcome improvement. We address the scheduling
problem using deep reinforcement learning (RL) to achieve high predictive gain
and low measurement cost, by scheduling fewer, but strategically timed tests.
We first show that in the simulation our policy outperforms heuristic-based
measurement scheduling with higher predictive gain or lower cost measured by
accumulated reward. We then learn a scheduling policy for mortality forecasting
in the real-world clinical dataset (MIMIC3), our learned policy is able to
provide useful clinical insights. To our knowledge, this is the first RL
application on multi-measurement scheduling problem in the clinical setting.
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Authors: Phillip Pope, Soheil Kolouri, Mohammad Rostrami, Charles Martin, Heiko Hoffmann
Date: 2018-12-01 00:00:00
Tasks: Specificity

Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry and material sciences, by reducing the amount of lab experiments required for discovery or synthesis of new molecules. In this paper, we investigate methods based on graph convolutional neural networks (GCNNs) for localizing FGs that contribute to specific chemical properties of interest. In our framework, molecules are modeled as undirected relational graphs with atoms as nodes and bonds as edges. Using this relational graph structure, we trained GCNNs in a supervised way on experimentally-validated molecular training sets to predict specific chemical properties, e.g., toxicity. Upon learning a GCNN, we analyzed its activation patterns to automatically identify FGs using four different explainability methods that we have developed: gradient-based saliency maps, Class Activation Mapping (CAM), gradient-weighted CAM (Grad-CAM), and Excitation Back-Propagation. Although these methods are originally derived for convolutional neural networks (CNNs), we adapt them to develop the corresponding suitable versions for GCNNs. We evaluated the contrastive power of these methods with respect to the specificity of the identified molecular substructures and their relevance for chemical functions. Grad-CAM had the highest contrastive power and generated qualitatively the best FGs. This work paves the way for automatic analysis and design of new molecules.
Towards Gaussian Bayesian Network Fusion
Authors: Irene Córdoba, Concha Bielza, Pedro Larrañaga
Date: 2018-12-01 00:00:00

Data sets are growing in complexity thanks to the increasing facilities we
have nowadays to both generate and store data. This poses many challenges to
machine learning that are leading to the proposal of new methods and paradigms,
in order to be able to deal with what is nowadays referred to as Big Data. In
this paper we propose a method for the aggregation of different Bayesian
network structures that have been learned from separate data sets, as a first
step towards mining data sets that need to be partitioned in an horizontal way,
i.e. with respect to the instances, in order to be processed. Considerations
that should be taken into account when dealing with this situation are
discussed. Scalable learning of Bayesian networks is slowly emerging, and our
method constitutes one of the first insights into Gaussian Bayesian network
aggregation from different sources. Tested on synthetic data it obtains good
results that surpass those from individual learning. Future research will be
focused on expanding the method and testing more diverse data sets.
AnyThreat: An Opportunistic Knowledge Discovery Approach to Insider Threat Detection
Authors: Diana Haidar, Mohamed Medhat Gaber, Yevgeniya Kovalchuk
Date: 2018-12-01 00:00:00
Tasks: Feature Engineering, General Classification

Insider threat detection is getting an increased concern from academia,
industry, and governments due to the growing number of malicious insider
incidents. The existing approaches proposed for detecting insider threats still
have a common shortcoming, which is the high number of false alarms (false
positives). The challenge in these approaches is that it is essential to detect
all anomalous behaviours which belong to a particular threat. To address this
shortcoming, we propose an opportunistic knowledge discovery system, namely
AnyThreat, with the aim to detect any anomalous behaviour in all malicious
insider threats. We design the AnyThreat system with four components. (1) A
feature engineering component, which constructs community data sets from the
activity logs of a group of users having the same role. (2) An oversampling
component, where we propose a novel oversampling technique named Artificial
Minority Oversampling and Trapper REmoval (AMOTRE). AMOTRE first removes the
minority (anomalous) instances that have a high resemblance with normal
(majority) instances to reduce the number of false alarms, then it
synthetically oversamples the minority class by shielding the border of the
majority class. (3) A class decomposition component, which is introduced to
cluster the instances of the majority class into subclasses to weaken the
effect of the majority class without information loss. (4) A classification
component, which applies a classification method on the subclasses to achieve a
better separation between the majority class(es) and the minority class(es).
AnyThreat is evaluated on synthetic data sets generated by Carnegie Mellon
University. It detects approximately 87.5% of malicious insider threats, and
achieves the minimum of false positives=3.36%.
A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task
Authors: Jack Hadfield, Georgia Chalvatzaki, Petros Koutras, Mehdi Khamassi, Costas S. Tzafestas, Petros Maragos
Date: 2018-12-01 00:00:00

In this work we tackle the problem of child engagement estimation while
children freely interact with a robot in their room. We propose a deep-based
multi-view solution that takes advantage of recent developments in human pose
detection. We extract the child's pose from different RGB-D cameras placed
elegantly in the room, fuse the results and feed them to a deep neural network
trained for classifying engagement levels. The deep network contains a
recurrent layer, in order to exploit the rich temporal information contained in
the pose data. The resulting method outperforms a number of baseline
classifiers, and provides a promising tool for better automatic understanding
of a child's attitude, interest and attention while cooperating with a robot.
The goal is to integrate this model in next generation social robots as an
attention monitoring tool during various CRI tasks both for Typically Developed
(TD) children and children affected by autism (ASD).
LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator
Authors: Georgia Chalvatzaki, Petros Koutras, Jack Hadfield, Xanthi S. Papageorgiou, Costas S. Tzafestas, Petros Maragos
Date: 2018-12-01 00:00:00
Tasks: Decoder, Pose Estimation, State Estimation

In this work, we present a novel framework for on-line human gait stability
prediction of the elderly users of an intelligent robotic rollator using Long
Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range
Finder (LRF) data from non-wearable sensors. A Deep Learning (DL) based
approach is used for the upper body pose estimation. The detected pose is used
for estimating the body Center of Mass (CoM) using Unscented Kalman Filter
(UKF). An Augmented Gait State Estimation framework exploits the LRF data to
estimate the legs' positions and the respective gait phase. These estimates are
the inputs of an encoder-decoder sequence to sequence model which predicts the
gait stability state as Safe or Fall Risk walking. It is validated with data
from real patients, by exploring different network architectures,
hyperparameter settings and by comparing the proposed method with other
baselines. The presented LSTM-based human gait stability predictor is shown to
provide robust predictions of the human stability state, and thus has the
potential to be integrated into a general user-adaptive control architecture as
a fall-risk alarm.
On Compressing U-net Using Knowledge Distillation
Authors: Karttikeya Mangalam, Mathieu Salzamann
Date: 2018-12-01 00:00:00
Tasks: Knowledge Distillation

We study the use of knowledge distillation to compress the U-net
architecture. We show that, while standard distillation is not sufficient to
reliably train a compressed U-net, introducing other regularization methods,
such as batch normalization and class re-weighting, in knowledge distillation
significantly improves the training process. This allows us to compress a U-net
by over 1000x, i.e., to 0.1% of its original number of parameters, at a
negligible decrease in performance.
Building robust classifiers through generation of confident out of distribution examples
Authors: Kumar Sricharan, Ashok Srivastava
Date: 2018-12-01 00:00:00

Deep learning models are known to be overconfident in their predictions on
out of distribution inputs. There have been several pieces of work to address
this issue, including a number of approaches for building Bayesian neural
networks, as well as closely related work on detection of out of distribution
samples. Recently, there has been work on building classifiers that are robust
to out of distribution samples by adding a regularization term that maximizes
the entropy of the classifier output on out of distribution data. To
approximate out of distribution samples (which are not known apriori), a GAN
was used for generation of samples at the edges of the training distribution.
In this paper, we introduce an alternative GAN based approach for building a
robust classifier, where the idea is to use the GAN to explicitly generate out
of distribution samples that the classifier is confident on (low entropy), and
have the classifier maximize the entropy for these samples. We showcase the
effectiveness of our approach relative to state-of-the-art on hand-written
characters as well as on a variety of natural image datasets.
Improving robustness of classifiers by training against live traffic
Authors: Kumar Sricharan, Kumar Kallurupalli, Ashok Srivastava
Date: 2018-12-01 00:00:00

Deep learning models are known to be overconfident in their predictions on
out of distribution inputs. This is a challenge when a model is trained on a
particular input dataset, but receives out of sample data when deployed in
practice. Recently, there has been work on building classifiers that are robust
to out of distribution samples by adding a regularization term that maximizes
the entropy of the classifier output on out of distribution data. However,
given the challenge that it is not always possible to obtain out of
distribution samples, the authors suggest a GAN based alternative that is
independent of specific knowledge of out of distribution samples. From this
existing work, we also know that having access to the true out of sample
distribution for regularization works significantly better than using samples
from the GAN. In this paper, we make the following observation: in practice,
the out of distribution samples are contained in the traffic that hits a
deployed classifier. However, the traffic will also contain a unknown
proportion of in-distribution samples. If the entropy over of all of the
traffic data were to be naively maximized, this will hurt the classifier
performance on in-distribution data. To effectively leverage this traffic data,
we propose an adaptive regularization technique (based on the maximum
predictive probability score of a sample) which penalizes out of distribution
samples more heavily than in distribution samples in the incoming traffic. This
ensures that the overall performance of the classifier does not degrade on
in-distribution data, while detection of out-of-distribution samples is
significantly improved by leveraging the unlabeled traffic data. We show the
effectiveness of our method via experiments on natural image datasets.
Learning to Caption Images through a Lifetime by Asking Questions
Authors: Kevin Shen, Amlan Kar, Sanja Fidler
Date: 2018-12-01 00:00:00
Tasks: Active Learning, Image Captioning

In order to bring artificial agents into our lives, we will need to go beyond
supervised learning on closed datasets to having the ability to continuously
expand knowledge. Inspired by a student learning in a classroom, we present an
agent that can continuously learn by posing natural language questions to
humans. Our agent is composed of three interacting modules, one that performs
captioning, another that generates questions and a decision maker that learns
when to ask questions by implicitly reasoning about the uncertainty of the
agent and expertise of the teacher. As compared to current active learning
methods which query images for full captions, our agent is able to ask pointed
questions to improve the generated captions. The agent trains on the improved
captions, expanding its knowledge. We show that our approach achieves better
performance using less human supervision than the baselines on the challenging
MSCOCO dataset.