pwc-archive/papers-with-abstracts huggingface dataset:
Active Deep Q-learning with Demonstration
Authors: Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama
Date: 2018-12-06 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent research has shown that although Reinforcement Learning (RL) can
benefit from expert demonstration, it usually takes considerable efforts to
obtain enough demonstration. The efforts prevent training decent RL agents with
expert demonstration in practice. In this work, we propose Active Reinforcement
Learning with Demonstration (ARLD), a new framework to streamline RL in terms
of demonstration efforts by allowing the RL agent to query for demonstration
actively during training. Under the framework, we propose Active Deep
Q-Network, a novel query strategy which adapts to the dynamically-changing
distributions during the RL training process by estimating the uncertainty of
recent states. The expert demonstration data within Active DQN are then
utilized by optimizing supervised max-margin loss in addition to temporal
difference loss within usual DQN training. We propose two methods of estimating
the uncertainty based on two state-of-the-art DQN models, namely the divergence
of bootstrapped DQN and the variance of noisy DQN. The empirical results
validate that both methods not only learn faster than other passive expert
demonstration methods with the same amount of demonstration and but also reach
super-expert level of performance across four different tasks.
Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Healthcare Records
Authors: Jeffrey Thompson, Jinxiang Hu, Dinesh Pal Mudaranthakam, David Streeter, Lisa Neums, Michele Park, Devin C. Koestler, Byron Gajewski, Matthew S. Mayo
Date: 2018-12-06 00:00:00

Objective: Electronic health records (EHR) represent a rich resource for
conducting observational studies, supporting clinical trials, and more.
However, much of the relevant information is stored in an unstructured format
that makes it difficult to use. Natural language processing approaches that
attempt to automatically classify the data depend on vectorization algorithms
that impose structure on the text, but these algorithms were not designed for
the unique characteristics of EHR. Here, we propose a new algorithm for
structuring so-called free-text that may help researchers make better use of
EHR. We call this method Relevant Word Order Vectorization (RWOV).
  Materials and Methods: As a proof-of-concept, we attempted to classify the
hormone receptor status of breast cancer patients treated at the University of
Kansas Medical Center during a recent year, from the unstructured text of
pathology reports. Our approach attempts to account for the semi-structured way
that healthcare providers often enter information. We compared this approach to
the ngrams and word2vec methods.
  Results: Our approach resulted in the most consistently high accuracy, as
measured by F1 score and area under the receiver operating characteristic curve
(AUC).
  Discussion: Our results suggest that methods of structuring free text that
take into account its context may show better performance, and that our
approach is promising.
  Conclusion: By using a method that accounts for the fact that healthcare
providers tend to use certain key words repetitively and that the order of
these key words is important, we showed improved performance over methods that
do not.
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
Authors: Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk, Jianming Zhang, Kate Saenko, Vittorio Murino, Stan Sclaroff
Date: 2018-12-06 00:00:00
Tasks: Classification, General Classification, Prediction

We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reasons" for a prediction, defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep convolutional neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable such evidence is for each of the top-k predicted classes, rather than solely trusting the top-1 prediction. We show that Guided Zoom improves the classification accuracy of a deep convolutional neural network model and obtains state-of-the-art results on three fine-grained classification benchmark datasets.
Tube-CNN: Modeling temporal evolution of appearance for object detection in video
Authors: Tuan-Hung Vu, Anton Osokin, Ivan Laptev
Date: 2018-12-06 00:00:00
Tasks: Object, object-detection, Object Detection, Temporal Sequences

Object detection in video is crucial for many applications. Compared to
images, video provides additional cues which can help to disambiguate the
detection problem. Our goal in this paper is to learn discriminative models for
the temporal evolution of object appearance and to use such models for object
detection. To model temporal evolution, we introduce space-time tubes
corresponding to temporal sequences of bounding boxes. We propose two CNN
architectures for generating and classifying tubes, respectively. Our tube
proposal network (TPN) first generates a large number of spatio-temporal tube
proposals maximizing object recall. The Tube-CNN then implements a tube-level
object detector in the video. Our method improves state of the art on two
large-scale datasets for object detection in video: HollywoodHeads and ImageNet
VID. Tube models show particular advantages in difficult dynamic scenes.
Modelling Identity Rules with Neural Networks
Authors: Tillman Weyde, Radha Manisha Kopparti
Date: 2018-12-06 00:00:00
Tasks: General Classification, Inductive Bias

In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Relation Based Pattern (RBP) extensions to neural network structures that solve this problem and answer, as well as raise, questions about integrating structures for inductive bias into neural networks. Examples of abstract patterns are the sequence patterns ABA and ABB where A or B can be any object. These were introduced by Marcus et al (1999) who also found that 7 month old infants recognise these patterns in sequences that use an unfamiliar vocabulary while simple recurrent neural networks do not.This result has been contested in the literature but it is confirmed by our experiments. We also show that the inability to generalise extends to different, previously untested, settings. We propose a new approach to modify standard neural network architectures, called Relation Based Patterns (RBP) with different variants for classification and prediction. Our experiments show that neural networks with the appropriate RBP structure achieve perfect classification and prediction performance on synthetic data, including mixed concrete and abstract patterns. RBP also improves neural network performance in experiments with real-world sequence prediction tasks. We discuss these finding in terms of challenges for neural network models and identify consequences from this result in terms of developing inductive biases for neural network learning.
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation
Authors: Alexandre Rame, Emilien Garreau, Hedi Ben-Younes, Charles Ollion
Date: 2018-12-06 00:00:00
Tasks: Domain Adaptation, object-detection, Object Detection, Transfer Learning

Object detectors tend to perform poorly in new or open domains, and require
exhaustive yet costly annotations from fully labeled datasets. We aim at
benefiting from several datasets with different categories but without
additional labelling, not only to increase the number of categories detected,
but also to take advantage from transfer learning and to enhance domain
independence.
  Our dataset merging procedure starts with training several initial Faster
R-CNN on the different datasets while considering the complementary datasets'
images for domain adaptation. Similarly to self-training methods, the
predictions of these initial detectors mitigate the missing annotations on the
complementary datasets. The final OMNIA Faster R-CNN is trained with all
categories on the union of the datasets enriched by predictions. The joint
training handles unsafe targets with a new classification loss called SoftSig
in a softly supervised way.
  Experimental results show that in the case of fashion detection for images in
the wild, merging Modanet with COCO increases the final performance from 45.5%
to 57.4% in mAP. Applying our soft distillation to the task of detection with
domain shift between GTA and Cityscapes enables to beat the state-of-the-art by
5.3 points. Our methodology could unlock object detection for real-world
applications without immense datasets.
Disjoint Label Space Transfer Learning with Common Factorised Space
Authors: Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales
Date: 2018-12-06 00:00:00
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

In this paper, a unified approach is presented to transfer learning that
addresses several source and target domain label-space and annotation
assumptions with a single model. It is particularly effective in handling a
challenging case, where source and target label-spaces are disjoint, and
outperforms alternatives in both unsupervised and semi-supervised settings. The
key ingredient is a common representation termed Common Factorised Space. It is
shared between source and target domains, and trained with an unsupervised
factorisation loss and a graph-based loss. With a wide range of experiments, we
demonstrate the flexibility, relevance and efficacy of our method, both in the
challenging cases with disjoint label spaces, and in the more conventional
cases such as unsupervised domain adaptation, where the source and target
domains share the same label-sets.
Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis
Authors: Hao-Ting Wang, Jonathan Smallwood, Janaina Mourao-Miranda, Cedric Huchuan Xia, Theodore D. Satterthwaite, Danielle S. Bassett, Danilo Bzdok
Date: 2018-12-06 00:00:00

Since the beginning of the 21st century, the size, breadth, and granularity
of data in biology and medicine has grown rapidly. In the example of
neuroscience, studies with thousands of subjects are becoming more common,
which provide extensive phenotyping on the behavioral, neural, and genomic
level with hundreds of variables. The complexity of such big data repositories
offer new opportunities and pose new challenges to investigate brain,
cognition, and disease. Canonical correlation analysis (CCA) is a prototypical
family of methods for wrestling with and harvesting insight from such rich
datasets. This doubly-multivariate tool can simultaneously consider two
variable sets from different modalities to uncover essential hidden
associations. Our primer discusses the rationale, promises, and pitfalls of CCA
in biomedicine.
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
Authors: Jogendra Nath Kundu, Maharshi Gor, Phani Krishna Uppala, R. Venkatesh Babu
Date: 2018-12-06 00:00:00
Tasks: Action Recognition, Fine-grained Action Recognition, Representation Learning, Temporal Action Localization

An unsupervised human action modeling framework can provide useful
pose-sequence representation, which can be utilized in a variety of pose
analysis applications. In this work we propose a novel temporal pose-sequence
modeling framework, which can embed the dynamics of 3D human-skeleton joints to
a continuous latent space in an efficient manner. In contrast to end-to-end
framework explored by previous works, we disentangle the task of individual
pose representation learning from the task of learning actions as a trajectory
in pose embedding space. In order to realize a continuous pose embedding
manifold with improved reconstructions, we propose an unsupervised, manifold
learning procedure named Encoder GAN, (or EnGAN). Further, we use the pose
embeddings generated by EnGAN to model human actions using a bidirectional RNN
auto-encoder architecture, PoseRNN. We introduce first-order gradient loss to
explicitly enforce temporal regularity in the predicted motion sequence. A
hierarchical feature fusion technique is also investigated for simultaneous
modeling of local skeleton joints along with global pose variations. We
demonstrate state-of-the-art transfer-ability of the learned representation
against other supervisedly and unsupervisedly learned motion embeddings for the
task of fine-grained action recognition on SBU interaction dataset. Further, we
show the qualitative strengths of the proposed framework by visualizing
skeleton pose reconstructions and interpolations in pose-embedding space, and
low dimensional principal component projections of the reconstructed pose
trajectories.
BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN
Authors: Jogendra Nath Kundu, Maharshi Gor, R. Venkatesh Babu
Date: 2018-12-06 00:00:00
Tasks: Human motion prediction, motion prediction, Prediction

Human motion prediction model has applications in various fields of computer
vision. Without taking into account the inherent stochasticity in the
prediction of future pose dynamics, such methods often converges to a
deterministic undesired mean of multiple probable outcomes. Devoid of this, we
propose a novel probabilistic generative approach called Bidirectional Human
motion prediction GAN, or BiHMP-GAN. To be able to generate multiple probable
human-pose sequences, conditioned on a given starting sequence, we introduce a
random extrinsic factor r, drawn from a predefined prior distribution.
Furthermore, to enforce a direct content loss on the predicted motion sequence
and also to avoid mode-collapse, a novel bidirectional framework is
incorporated by modifying the usual discriminator architecture. The
discriminator is trained also to regress this extrinsic factor r, which is used
alongside with the intrinsic factor (encoded starting pose sequence) to
generate a particular pose sequence. To further regularize the training, we
introduce a novel recursive prediction strategy. In spite of being in a
probabilistic framework, the enhanced discriminator architecture allows
predictions of an intermediate part of pose sequence to be used as a
conditioning for prediction of the latter part of the sequence. The
bidirectional setup also provides a new direction to evaluate the prediction
quality against a given test sequence. For a fair assessment of BiHMP-GAN, we
report performance of the generated motion sequence using (i) a critic model
trained to discriminate between real and fake motion sequence, and (ii) an
action classifier trained on real human motion dynamics. Outcomes of both
qualitative and quantitative evaluations, on the probabilistic generations of
the model, demonstrate the superiority of BiHMP-GAN over previously available
methods.
Prior Networks for Detection of Adversarial Attacks
Authors: Andrey Malinin, Mark Gales
Date: 2018-12-06 00:00:00
Tasks: Adversarial Attack, Adversarial Attack Detection

Adversarial examples are considered a serious issue for safety critical
applications of AI, such as finance, autonomous vehicle control and medicinal
applications. Though significant work has resulted in increased robustness of
systems to these attacks, systems are still vulnerable to well-crafted attacks.
To address this problem, several adversarial attack detection methods have been
proposed. However, a system can still be vulnerable to adversarial samples that
are designed to specifically evade these detection methods. One recent
detection scheme that has shown good performance is based on uncertainty
estimates derived from Monte-Carlo dropout ensembles. Prior Networks, a new
method of estimating predictive uncertainty, has been shown to outperform
Monte-Carlo dropout on a range of tasks. One of the advantages of this approach
is that the behaviour of a Prior Network can be explicitly tuned to, for
example, predict high uncertainty in regions where there are no training data
samples. In this work, Prior Networks are applied to adversarial attack
detection using measures of uncertainty in a similar fashion to Monte-Carlo
Dropout. Detection based on measures of uncertainty derived from DNNs and
Monte-Carlo dropout ensembles are used as a baseline. Prior Networks are shown
to significantly out-perform these baseline approaches over a range of
adversarial attacks in both detection of whitebox and blackbox configurations.
Even when the adversarial attacks are constructed with full knowledge of the
detection mechanism, it is shown to be highly challenging to successfully
generate an adversarial sample.
Enhancing the efficiency of quantum annealing via reinforcement: A path-integral Monte Carlo simulation of the quantum reinforcement algorithm
Authors: A. Ramezanpour
Date: 2018-12-06 00:00:00

The standard quantum annealing algorithm tries to approach the ground state
of a classical system by slowly decreasing the hopping rates of a quantum
random walk in the configuration space of the problem, where the on-site
energies are provided by the classical energy function. In a quantum
reinforcement algorithm, the annealing works instead by increasing gradually
the strength of the on-site energies according to the probability of finding
the walker on each site of the configuration space. Here, by using the
path-integral Monte Carlo simulations of the quantum algorithms, we show that
annealing via reinforcement can significantly enhance the success probability
of the quantum walker. More precisely, we implement a local version of the
quantum reinforcement algorithm, where the system wave function is replaced by
an approximate wave function using the local expectation values of the system.
We use this algorithm to find solutions to a prototypical constraint
satisfaction problem (XORSAT) close to the satisfiability to unsatisfiability
phase transition. The study is limited to small problem sizes (a few hundreds
of variables), nevertheless, the numerical results suggest that quantum
reinforcement may provide a useful strategy to deal with other computationally
hard problems and larger problem sizes even as a classical optimization
algorithm.
Singular Values for ReLU Layers
Authors: Sören Dittmer, Emily J. King, Peter Maass
Date: 2018-12-06 00:00:00

Despite their prevalence in neural networks we still lack a thorough theoretical characterization of ReLU layers. This paper aims to further our understanding of ReLU layers by studying how the activation function ReLU interacts with the linear component of the layer and what role this interaction plays in the success of the neural network in achieving its intended task. To this end, we introduce two new tools: ReLU singular values of operators and the Gaussian mean width of operators. By presenting on the one hand theoretical justifications, results, and interpretations of these two concepts and on the other hand numerical experiments and results of the ReLU singular values and the Gaussian mean width being applied to trained neural networks, we hope to give a comprehensive, singular-value-centric view of ReLU layers. We find that ReLU singular values and the Gaussian mean width do not only enable theoretical insights, but also provide one with metrics which seem promising for practical applications. In particular, these measures can be used to distinguish correctly and incorrectly classified data as it traverses the network. We conclude by introducing two tools based on our findings: double-layers and harmonic pruning.
A two-stage hybrid model by using artificial neural networks as feature construction algorithms
Authors: Yan Wang, Xuelei Sherry Ni, Brian Stone
Date: 2018-12-06 00:00:00

We propose a two-stage hybrid approach with neural networks as the new
feature construction algorithms for bankcard response classifications. The
hybrid model uses a very simple neural network structure as the new feature
construction tool in the first stage, then the newly created features are used
as the additional input variables in logistic regression in the second stage.
The model is compared with the traditional one-stage model in credit customer
response classification. It is observed that the proposed two-stage model
outperforms the one-stage model in terms of accuracy, the area under ROC curve,
and KS statistic. By creating new features with the neural network technique,
the underlying nonlinear relationships between variables are identified.
Furthermore, by using a very simple neural network structure, the model could
overcome the drawbacks of neural networks in terms of its long training time,
complex topology, and limited interpretability.
Computer Vision for Autonomous Vehicles
Authors: Rohit Gandikota
Date: 2018-12-06 00:00:00
Tasks: Autonomous Vehicles

In this work, we try to implement Image Processing techniques in the area of
autonomous vehicles, both indoor and outdoor. The challenges for both are
different and the ways to tackle them vary too. We also showed deep learning
makes things easier and precise. We also made base models for all the problems
we tackle while building an autonomous car for Indian Institute of Space
science and Technology.
Segmentation-driven 6D Object Pose Estimation
Authors: Yinlin Hu, Joachim Hugonot, Pascal Fua, Mathieu Salzmann
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Object, Pose Estimation, Pose Prediction, Segmentation

The most recent trend in estimating the 6D pose of rigid objects has been to
train deep networks to either directly regress the pose from the image or to
predict the 2D locations of 3D keypoints, from which the pose can be obtained
using a PnP algorithm. In both cases, the object is treated as a global entity,
and a single pose estimate is computed. As a consequence, the resulting
techniques can be vulnerable to large occlusions.
  In this paper, we introduce a segmentation-driven 6D pose estimation
framework where each visible part of the objects contributes a local pose
prediction in the form of 2D keypoint locations. We then use a predicted
measure of confidence to combine these pose candidates into a robust set of
3D-to-2D correspondences, from which a reliable pose estimate can be obtained.
We outperform the state-of-the-art on the challenging Occluded-LINEMOD and
YCB-Video datasets, which is evidence that our approach deals well with
multiple poorly-textured objects occluding each other. Furthermore, it relies
on a simple enough architecture to achieve real-time performance.