pwc-archive/papers-with-abstracts huggingface dataset:
Scale-invariant biomarker discovery in urine and plasma metabolite fingerprints
Authors: Helena U. Zacharias, Thorsten Rehberg, Sebastian Mehrl, Daniel Richtmann, Tilo Wettig, Peter J. Oefner, Rainer Spang, Wolfram Gronwald, Michael Altenbuchinger
Date: 2017-03-22 00:00:00
Tasks: regression

Motivation: Metabolomics data is typically scaled to a common reference like
a constant volume of body fluid, a constant creatinine level, or a constant
area under the spectrum. Such normalization of the data, however, may affect
the selection of biomarkers and the biological interpretation of results in
unforeseen ways.
  Results: First, we study how the outcome of hypothesis tests for differential
metabolite concentration is affected by the choice of scale. Furthermore, we
observe this interdependence also for different classification approaches.
Second, to overcome this problem and establish a scale-invariant biomarker
discovery algorithm, we extend linear zero-sum regression to the logistic
regression framework and show in two applications to ${}^1$H NMR-based
metabolomics data how this approach overcomes the scaling problem.
  Availability: Logistic zero-sum regression is available as an R package as
well as a high-performance computing implementation that can be downloaded at
https://github.com/rehbergT/zeroSum
Landmark based localization in urban environment
Authors: Xiaozhi Qu, Bahman Soheilian, Nicolas Paparoditis
Date: 2018-01-01 00:00:00
Proceeding: ISPRS Journal of Photogrammetry and Remote Sensing 2018 1
Tasks: Pose Estimation

A landmark based localization with uncertainty analysis based on cameras and geo-referenced landmarks
is presented in this paper. The system is developed to adapt different camera configurations for six
degree-of-freedom pose estimation. Local bundle adjustment is applied for optimization and the geo-
referenced landmarks are integrated to reduce the drift. In particular, the uncertainty analysis is taken
into account. On the one hand, we estimate the uncertainties of poses to predict the precision of localiza-
tion. On the other hand, uncertainty propagation is considered for matching, tracking and landmark reg-
istering. The proposed method is evaluated on both KITTI benchmark and the data acquired by a mobile
mapping system. In our experiments, decimeter level accuracy can be reached.
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
Authors: Kim Nicoli, Pan Kessel, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Shinichi Nakajima
Date: 2019-03-26 00:00:00

In this comment on "Solving Statistical Mechanics Using Variational
Autoregressive Networks" by Wu et al., we propose a subtle yet powerful
modification of their approach. We show that the inherent sampling error of
their method can be corrected by using neural network-based MCMC or importance
sampling which leads to asymptotically unbiased estimators for physical
quantities. This modification is possible due to a singular property of VANs,
namely that they provide the exact sample probability. With these
modifications, we believe that their method could have a substantially greater
impact on various important fields of physics, including strongly-interacting
field theories and statistical physics.
Adversarially Learned Abnormal Trajectory Classifier
Authors: Pankaj Raj Roy, Guillaume-Alexandre Bilodeau
Date: 2019-03-26 00:00:00
Tasks: Event Detection, Generative Adversarial Network

We address the problem of abnormal event detection from trajectory data. In
this paper, a new adversarial approach is proposed for building a deep neural
network binary classifier, trained in an unsupervised fashion, that can
distinguish normal from abnormal trajectory-based events without the need for
setting manual detection threshold. Inspired by the generative adversarial
network (GAN) framework, our GAN version is a discriminative one in which the
discriminator is trained to distinguish normal and abnormal trajectory
reconstruction errors given by a deep autoencoder. With urban traffic videos
and their associated trajectories, our proposed method gives the best accuracy
for abnormal trajectory detection. In addition, our model can easily be
generalized for abnormal trajectory-based event detection and can still yield
the best behavioural detection results as demonstrated on the CAVIAR dataset.
Optimising the Input Image to Improve Visual Relationship Detection
Authors: Noel Mizzi, Adrian Muscat
Date: 2019-03-26 00:00:00
Tasks: Object, object-detection, Object Detection, Relationship Detection, Visual Relationship Detection

Visual Relationship Detection is defined as, given an image composed of a
subject and an object, the correct relation is predicted. To improve the visual
part of this difficult problem, ten preprocessing methods were tested to
determine whether the widely used Union method yields the optimal results.
Therefore, focusing solely on predicate prediction, no object detection and
linguistic knowledge were used to prevent them from affecting the comparison
results. Once fine-tuned, the Visual Geometry Group models were evaluated using
Recall@1, per-predicate recall, activation maximisations, class activation
maps, and error analysis. From this research it was found that using
preprocessing methods such as the Union-Without-Background-and-with-Binary-mask
(Union-WB-and-B) method yields significantly better results than the widely
used Union method since, as designed, it enables the Convolutional Neural
Network to also identify the subject and object in the convolutional layers
instead of solely in the fully-connected layers.
nuScenes: A multimodal dataset for autonomous driving
Authors: Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom
Date: 2019-03-26 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Object Detection, Autonomous Driving, Autonomous Vehicles, object-detection, Object Detection

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online.
Deep Learning and Word Embeddings for Tweet Classification for Crisis Response
Authors: Reem ALRashdi, Simon O'Keefe
Date: 2019-03-26 00:00:00
Tasks: Classification, General Classification, Word Embeddings

Tradition tweet classification models for crisis response focus on
convolutional layers and domain-specific word embeddings. In this paper, we
study the application of different neural networks with general-purpose and
domain-specific word embeddings to investigate their ability to improve the
performance of tweet classification models. We evaluate four tweet
classification models on CrisisNLP dataset and obtain comparable results which
indicates that general-purpose word embedding such as GloVe can be used instead
of domain-specific word embedding especially with Bi-LSTM where results
reported the highest performance of 62.04% F1 score.
Domain Independent SVM for Transfer Learning in Brain Decoding
Authors: Shuo Zhou, Wenwen Li, Christopher R. Cox, Haiping Lu
Date: 2019-03-26 00:00:00
Tasks: Brain Decoding, Transfer Learning

Brain imaging data are important in brain sciences yet expensive to obtain,
with big volume (i.e., large p) but small sample size (i.e., small n). To
tackle this problem, transfer learning is a promising direction that leverages
source data to improve performance on related, target data. Most transfer
learning methods focus on minimizing data distribution mismatch. However, a big
challenge in brain imaging is the large domain discrepancies in cognitive
experiment designs and subject-specific structures and functions. A recent
transfer learning approach minimizes domain dependence to learn common features
across domains, via the Hilbert-Schmidt Independence Criterion (HSIC). Inspired
by this method, we propose a new Domain Independent Support Vector Machine
(DI-SVM) for transfer learning in brain condition decoding. Specifically,
DI-SVM simultaneously minimizes the SVM empirical risk and the dependence on
domain information via a simplified HSIC. We use public data to construct 13
transfer learning tasks in brain decoding, including three interesting
multi-source transfer tasks. Experiments show that DI-SVM's superior
performance over eight competing methods on these tasks, particularly an
improvement of more than 24% on multi-source transfer tasks.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games
Authors: Devdhar Patel, Hananel Hazan, Daniel J. Saunders, Hava Siegelmann, Robert Kozma
Date: 2019-03-26 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, image-classification, Image Classification, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (RL) demonstrates excellent performance on tasks that can be solved by trained policy. It plays a dominant role among cutting-edge machine learning approaches using multi-layer Neural networks (NNs). At the same time, Deep RL suffers from high sensitivity to noisy, incomplete, and misleading input data. Following biological intuition, we involve Spiking Neural Networks (SNNs) to address some deficiencies of deep RL solutions. Previous studies in image classification domain demonstrated that standard NNs (with ReLU nonlinearity) trained using supervised learning can be converted to SNNs with negligible deterioration in performance. In this paper, we extend those conversion results to the domain of Q-Learning NNs trained using RL. We provide a proof of principle of the conversion of standard NN to SNN. In addition, we show that the SNN has improved robustness to occlusion in the input image. Finally, we introduce results with converting full-scale Deep Q-network to SNN, paving the way for future research to robust Deep RL applications.
Learning Accurate, Comfortable and Human-like Driving
Authors: Simon Hecker, Dengxin Dai, Luc van Gool
Date: 2019-03-26 00:00:00
Tasks: Autonomous Vehicles, Navigate

Autonomous vehicles are more likely to be accepted if they drive accurately,
comfortably, but also similar to how human drivers would. This is especially
true when autonomous and human-driven vehicles need to share the same road. The
main research focus thus far, however, is still on improving driving accuracy
only. This paper formalizes the three concerns with the aim of accurate,
comfortable and human-like driving. Three contributions are made in this paper.
First, numerical map data from HERE Technologies are employed for more accurate
driving; a set of map features which are believed to be relevant to driving are
engineered to navigate better. Second, the learning procedure is improved from
a pointwise prediction to a sequence-based prediction and passengers' comfort
measures are embedded into the learning algorithm. Finally, we take advantage
of the advances in adversary learning to learn human-like driving;
specifically, the standard L1 or L2 loss is augmented by an adversary loss
which is based on a discriminator trained to distinguish between human driving
and machine driving. Our model is trained and evaluated on the Drive360
dataset, which features 60 hours and 3000 km of real-world driving data.
Extensive experiments show that our driving model is more accurate, more
comfortable and behaves more like a human driver than previous methods. The
resources of this work will be released on the project page.
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation
Authors: Marco Ancona, Cengiz Öztireli, Markus Gross
Date: 2019-03-26 00:00:00

The problem of explaining the behavior of deep neural networks has recently gained a lot of attention. While several attribution methods have been proposed, most come without strong theoretical foundations, which raises questions about their reliability. On the other hand, the literature on cooperative game theory suggests Shapley values as a unique way of assigning relevance scores such that certain desirable properties are satisfied. Unfortunately, the exact evaluation of Shapley values is prohibitively expensive, exponential in the number of input features. In this work, by leveraging recent results on uncertainty propagation, we propose a novel, polynomial-time approximation of Shapley values in deep neural networks. We show that our method produces significantly better approximations of Shapley values than existing state-of-the-art attribution methods.
A Tight Runtime Analysis for the cGA on Jump Functions---EDAs Can Cross Fitness Valleys at No Extra Cost
Authors: Benjamin Doerr
Date: 2019-03-26 00:00:00
Tasks: Evolutionary Algorithms, valid

We prove that the compact genetic algorithm (cGA) with hypothetical
population size $\mu = \Omega(\sqrt n \log n) \cap \text{poly}(n)$ with high
probability finds the optimum of any $n$-dimensional jump function with jump
size $k < \frac 1 {20} \ln n$ in $O(\mu \sqrt n)$ iterations. Since it is known
that the cGA with high probability needs at least $\Omega(\mu \sqrt n + n \log
n)$ iterations to optimize the unimodal OneMax function, our result shows that
the cGA in contrast to most classic evolutionary algorithms here is able to
cross moderate-sized valleys of low fitness at no extra cost.
  Our runtime guarantee improves over the recent upper bound $O(\mu n^{1.5}
\log n)$ valid for $\mu = \Omega(n^{3.5+\varepsilon})$ of Hasen\"ohrl and
Sutton (GECCO 2018). For the best choice of the hypothetical population size,
this result gives a runtime guarantee of $O(n^{5+\varepsilon})$, whereas ours
gives $O(n \log n)$.
  We also provide a simple general method based on parallel runs that, under
mild conditions, (i)~overcomes the need to specify a suitable population size,
but gives a performance close to the one stemming from the best-possible
population size, and (ii)~transforms EDAs with high-probability performance
guarantees into EDAs with similar bounds on the expected runtime.
DetNAS: Backbone Search for Object Detection
Authors: Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, Jian Sun
Date: 2019-03-26 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: General Classification, image-classification, Image Classification, Neural Architecture Search, Object, object-detection, Object Detection

Object detectors are usually equipped with backbone networks designed for image classification. It might be sub-optimal because of the gap between the tasks of image classification and object detection. In this work, we present DetNAS to use Neural Architecture Search (NAS) for the design of better backbones for object detection. It is non-trivial because detection training typically needs ImageNet pre-training while NAS systems require accuracies on the target detection task as supervisory signals. Based on the technique of one-shot supernet, which contains all possible networks in the search space, we propose a framework for backbone search on object detection. We train the supernet under the typical detector training schedule: ImageNet pre-training and detection fine-tuning. Then, the architecture search is performed on the trained supernet, using the detection task as the guidance. This framework makes NAS on backbones very efficient. In experiments, we show the effectiveness of DetNAS on various detectors, for instance, one-stage RetinaNet and the two-stage FPN. We empirically find that networks searched on object detection shows consistent superiority compared to those searched on ImageNet classification. The resulting architecture achieves superior performance than hand-crafted networks on COCO with much less FLOPs complexity.
Sparse Learning for Variable Selection with Structures and Nonlinearities
Authors: Magda Gregorova
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning, Sparse Learning, Variable Selection

In this thesis we discuss machine learning methods performing automated
variable selection for learning sparse predictive models. There are multiple
reasons for promoting sparsity in the predictive models. By relying on a
limited set of input variables the models naturally counteract the overfitting
problem ubiquitous in learning from finite sets of training points. Sparse
models are cheaper to use for predictions, they usually require lower
computational resources and by relying on smaller sets of inputs can possibly
reduce costs for data collection and storage. Sparse models can also contribute
to better understanding of the investigated phenomenons as they are easier to
interpret than full models.
On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms
Authors: Dogan Corus, Pietro S. Oliveto
Date: 2019-03-26 00:00:00

It is generally accepted that populations are useful for the global
exploration of multi-modal optimisation problems. Indeed, several theoretical
results are available showing such advantages over single-trajectory search
heuristics. In this paper we provide evidence that evolving populations via
crossover and mutation may also benefit the optimisation time for hillclimbing
unimodal functions. In particular, we prove bounds on the expected runtime of
the standard ($\mu$+1)~GA for OneMax that are lower than its unary black box
complexity and decrease in the leading constant with the population size up to
$\mu=O(\sqrt{\log n})$. Our analysis suggests that the optimal mutation
strategy is to flip two bits most of the time. To achieve the results we
provide two interesting contributions to the theory of randomised search
heuristics: 1) A novel application of drift analysis which compares absorption
times of different Markov chains without defining an explicit potential
function. 2) The inversion of fundamental matrices to calculate the absorption
times of the Markov chains. The latter strategy was previously proposed in the
literature but to the best of our knowledge this is the first time is has been
used to show non-trivial bounds on expected runtimes.
Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network
Authors: Esra Ataer-Cansizoglu, Michael Jones, Ziming Zhang, Alan Sullivan
Date: 2019-03-26 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification, Super-Resolution

Face super-resolution methods usually aim at producing visually appealing
results rather than preserving distinctive features for further face
identification. In this work, we propose a deep learning method for face
verification on very low-resolution face images that involves
identity-preserving face super-resolution. Our framework includes a
super-resolution network and a feature extraction network. We train a VGG-based
deep face recognition network (Parkhi et al. 2015) to be used as feature
extractor. Our super-resolution network is trained to minimize the feature
distance between the high resolution ground truth image and the super-resolved
image, where features are extracted using our pre-trained feature extraction
network. We carry out experiments on FRGC, Multi-PIE, LFW-a, and MegaFace
datasets to evaluate our method in controlled and uncontrolled settings. The
results show that the presented method outperforms conventional
super-resolution methods in low-resolution face verification.
Simple Applications of BERT for Ad Hoc Document Retrieval
Authors: Wei Yang, Haotian Zhang, Jimmy Lin
Date: 2019-03-26 00:00:00
Tasks: Ad-Hoc Information Retrieval, Question Answering, Retrieval, Sentence

Following recent successes in applying BERT to question answering, we explore
simple applications to ad hoc document retrieval. This required confronting the
challenge posed by documents that are typically longer than the length of input
BERT was designed to handle. We address this issue by applying inference on
sentences individually, and then aggregating sentence scores to produce
document scores. Experiments on TREC microblog and newswire test collections
show that our approach is simple yet effective, as we report the highest
average precision on these datasets by neural approaches that we are aware of.
Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering
Authors: Liyang Han, Thomas Morstyn, Constance Crozier, Malcolm McCulloch
Date: 2019-03-26 00:00:00
Tasks: Clustering

Among the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number of participants. To address this issue, this paper proposes the application of K-means clustering to the energy profiles following the grand coalition optimization. The cooperative model is run with the "clustered players" to compute their payoff allocations, which are then further distributed among the prosumers within each cluster. Case studies show that the proposed method can significantly improve the scalability of the cooperative scheme while maintaining a high level of financial incentives for the prosumers.
Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation
Authors: Shin Nishio, Yulu Pan, Takahiko Satoh, Hideharu Amano, Rodney Van Meter
Date: 2019-03-26 00:00:00

NISQ (Noisy, Intermediate-Scale Quantum) computing requires error mitigation
to achieve meaningful computation. Our compilation tool development focuses on
the fact that the error rates of individual qubits are not equal, with a goal
of maximizing the success probability of real-world subroutines such as an
adder circuit. We begin by establishing a metric for choosing among possible
paths and circuit alternatives for executing gates between variables placed far
apart within the processor, and test our approach on two IBM 20-qubit systems
named Tokyo and Poughkeepsie. We find that a single-number metric describing
the fidelity of individual gates is a useful but imperfect guide. Our compiler
uses this subsystem and maps complete circuits onto the machine using a beam
search-based heuristic that will scale as processor and program sizes grow. To
evaluate the whole compilation process, we compiled and executed adder
circuits, then calculated the KL-divergence (a measure of the distance between
two probability distributions). For a circuit within the capabilities of the
hardware, our compilation increases estimated success probability and reduces
KL-divergence relative to an error-oblivious placement.