pwc-archive/papers-with-abstracts huggingface dataset:
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Authors: Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui
Date: 2018-11-21 00:00:00
Tasks: BIG-bench Machine Learning, Image Reconstruction

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.
Recognizing Disguised Faces in the Wild
Authors: Maneet Singh, Richa Singh, Mayank Vatsa, Nalini Ratha, Rama Chellappa
Date: 2018-11-21 00:00:00
Tasks: Disguised Face Verification, Face Recognition

Research in face recognition has seen tremendous growth over the past couple
of decades. Beginning from algorithms capable of performing recognition in
constrained environments, the current face recognition systems achieve very
high accuracies on large-scale unconstrained face datasets. While upcoming
algorithms continue to achieve improved performance, a majority of the face
recognition systems are susceptible to failure under disguise variations, one
of the most challenging covariate of face recognition. Most of the existing
disguise datasets contain images with limited variations, often captured in
controlled settings. This does not simulate a real world scenario, where both
intentional and unintentional unconstrained disguises are encountered by a face
recognition system. In this paper, a novel Disguised Faces in the Wild (DFW)
dataset is proposed which contains over 11000 images of 1000 identities with
different types of disguise accessories. The dataset is collected from the
Internet, resulting in unconstrained face images similar to real world
settings. This is the first-of-a-kind dataset with the availability of
impersonator and genuine obfuscated face images for each subject. The proposed
dataset has been analyzed in terms of three levels of difficulty: (i) easy,
(ii) medium, and (iii) hard in order to showcase the challenging nature of the
problem. It is our view that the research community can greatly benefit from
the DFW dataset in terms of developing algorithms robust to such adversaries.
The proposed dataset was released as part of the First International Workshop
and Competition on Disguised Faces in the Wild at CVPR, 2018. This paper
presents the DFW dataset in detail, including the evaluation protocols,
baseline results, performance analysis of the submissions received as part of
the competition, and three levels of difficulties of the DFW challenge dataset.
Boosting in Image Quality Assessment
Authors: Dogancan Temel, Ghassan AlRegib
Date: 2018-11-21 00:00:00
Tasks: Image Quality Assessment

In this paper, we analyze the effect of boosting in image quality assessment
through multi-method fusion. Existing multi-method studies focus on proposing a
single quality estimator. On the contrary, we investigate the generalizability
of multi-method fusion as a framework. In addition to support vector machines
that are commonly used in the multi-method fusion, we propose using neural
networks in the boosting. To span different types of image quality assessment
algorithms, we use quality estimators based on fidelity, perceptually-extended
fidelity, structural similarity, spectral similarity, color, and learning. In
the experiments, we perform k-fold cross validation using the LIVE, the
multiply distorted LIVE, and the TID 2013 databases and the performance of
image quality assessment algorithms are measured via accuracy-, linearity-, and
ranking-based metrics. Based on the experiments, we show that boosting methods
generally improve the performance of image quality assessment and the level of
improvement depends on the type of the boosting algorithm. Our experimental
results also indicate that boosting the worst performing quality estimator with
two or more additional methods leads to statistically significant performance
enhancements independent of the boosting technique and neural network-based
boosting outperforms support vector machine-based boosting when two or more
methods are fused.
Early Fusion for Goal Directed Robotic Vision
Authors: Aaron Walsman, Yonatan Bisk, Saadia Gabriel, Dipendra Misra, Yoav Artzi, Yejin Choi, Dieter Fox
Date: 2018-11-21 00:00:00
Tasks: Imitation Learning, Retrieval

Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline. Modern vision architectures require the agent to build a summary representation of the entire scene, even if most of the input is irrelevant to the agent's current goal. In this work, we flip this paradigm, by introducing EarlyFusion vision models that condition on a goal to build custom representations for downstream tasks. We show that these goal specific representations can be learned more quickly, are substantially more parameter efficient, and more robust than existing attention mechanisms in our domain. We demonstrate the effectiveness of these methods on a simulated robotic item retrieval problem that is trained in a fully end-to-end manner via imitation learning.
Trajectory PHD and CPHD filters
Authors: Ángel F. García-Fernández, Lennart Svensson
Date: 2018-11-21 00:00:00

This paper presents the probability hypothesis density filter (PHD) and the cardinality PHD (CPHD) filter for sets of trajectories, which are referred to as the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters. Contrary to the PHD/CPHD filters, the TPHD/TCPHD filters are able to produce trajectory estimates from first principles. The TPHD filter is derived by recursively obtaining the best Poisson multitrajectory density approximation to the posterior density over the alive trajectories by minimising the Kullback-Leibler divergence. The TCPHD is derived in the same way but propagating an independent identically distributed (IID) cluster multitrajectory density approximation. We also propose the Gaussian mixture implementations of the TPHD and TCPHD recursions, the Gaussian mixture TPHD (GMTPHD) and the Gaussian mixture TCPHD (GMTCPHD), and the L-scan computationally efficient implementations, which only update the density of the trajectory states of the last L time steps.
Learning cross-lingual phonological and orthagraphic adaptations: a case study in improving neural machine translation between low-resource languages
Authors: Saurav Jha, Akhilesh Sudhakar, Anil Kumar Singh
Date: 2018-11-21 00:00:00
Tasks: Machine Translation, NMT, Translation

Out-of-vocabulary (OOV) words can pose serious challenges for machine translation (MT) tasks, and in particular, for low-resource language (LRL) pairs, i.e., language pairs for which few or no parallel corpora exist. Our work adapts variants of seq2seq models to perform transduction of such words from Hindi to Bhojpuri (an LRL instance), learning from a set of cognate pairs built from a bilingual dictionary of Hindi--Bhojpuri words. We demonstrate that our models can be effectively used for language pairs that have limited parallel corpora; our models work at the character level to grasp phonetic and orthographic similarities across multiple types of word adaptations, whether synchronic or diachronic, loan words or cognates. We describe the training aspects of several character level NMT systems that we adapted to this task and characterize their typical errors. Our method improves BLEU score by 6.3 on the Hindi-to-Bhojpuri translation task. Further, we show that such transductions can generalize well to other languages by applying it successfully to Hindi -- Bangla cognate pairs. Our work can be seen as an important step in the process of: (i) resolving the OOV words problem arising in MT tasks, (ii) creating effective parallel corpora for resource-constrained languages, and (iii) leveraging the enhanced semantic knowledge captured by word-level embeddings to perform character-level tasks.
Learning Motion in Feature Space: Locally-Consistent Deformable Convolution Networks for Fine-Grained Action Detection
Authors: Khoi-Nguyen C. Mac, Dhiraj Joshi, Raymond A. Yeh, JinJun Xiong, Rogerio S. Feris, Minh N. Do
Date: 2018-11-21 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Detection, Fine-Grained Action Detection, Optical Flow Estimation

Fine-grained action detection is an important task with numerous applications in robotics and human-computer interaction. Existing methods typically utilize a two-stage approach including extraction of local spatio-temporal features followed by temporal modeling to capture long-term dependencies. While most recent papers have focused on the latter (long-temporal modeling), here, we focus on producing features capable of modeling fine-grained motion more efficiently. We propose a novel locally-consistent deformable convolution, which utilizes the change in receptive fields and enforces a local coherency constraint to capture motion information effectively. Our model jointly learns spatio-temporal features (instead of using independent spatial and temporal streams). The temporal component is learned from the feature space instead of pixel space, e.g. optical flow. The produced features can be flexibly used in conjunction with other long-temporal modeling networks, e.g. ST-CNN, DilatedTCN, and ED-TCN. Overall, our proposed approach robustly outperforms the original long-temporal models on two fine-grained action datasets: 50 Salads and GTEA, achieving F1 scores of 80.22% and 75.39% respectively.
Adversarial Classifier for Imbalanced Problems
Authors: Ehsan Montahaei, Mahsa Ghorbani, Mahdieh Soleymani Baghshah, Hamid R. Rabiee
Date: 2018-11-21 00:00:00
Tasks: Classification, General Classification, Graph Representation Learning, imbalanced classification, Link Prediction, Representation Learning

Adversarial approach has been widely used for data generation in the last few
years. However, this approach has not been extensively utilized for classifier
training. In this paper, we propose an adversarial framework for classifier
training that can also handle imbalanced data. Indeed, a network is trained via
an adversarial approach to give weights to samples of the majority class such
that the obtained classification problem becomes more challenging for the
discriminator and thus boosts its classification capability. In addition to the
general imbalanced classification problems, the proposed method can also be
used for problems such as graph representation learning in which it is desired
to discriminate similar nodes from dissimilar nodes. Experimental results on
imbalanced data classification and on the tasks like graph link prediction show
the superiority of the proposed method compared to the state-of-the-art
methods.
Distinguishing correlation from causation using genome-wide association studies
Authors: Luke J. O'Connor, Alkes L. Price
Date: 2018-11-21 00:00:00

Genome-wide association studies (GWAS) have emerged as a rich source of
genetic clues into disease biology, and they have revealed strong genetic
correlations among many diseases and traits. Some of these genetic correlations
may reflect causal relationships. We developed a method to quantify causal
relationships between genetically correlated traits using GWAS summary
association statistics. In particular, our method quantifies what part of the
genetic component of trait 1 is also causal for trait 2 using mixed fourth
moments $E(\alpha_1^2\alpha_1\alpha_2)$ and $E(\alpha_2^2\alpha_1\alpha_2)$ of
the bivariate effect size distribution. If trait 1 is causal for trait 2, then
SNPs affecting trait 1 (large $\alpha_1^2$) will have correlated effects on
trait 2 (large $\alpha_1\alpha_2$), but not vice versa. We validated this
approach in extensive simulations. Across 52 traits (average $N=331$k), we
identified 30 putative genetically causal relationships, many novel, including
an effect of LDL cholesterol on decreased bone mineral density. More broadly,
we demonstrate that it is possible to distinguish between genetic correlation
and causation using genetic association data.
Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards
Authors: Mhafuzul Islam, Mahsrur Chowdhury, Hongda Li, Hongxin Hu
Date: 2018-09-27 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Navigate, object-detection, Object Detection, Semantic Segmentation, Transfer Learning

Vision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems of the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adversarial inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicle by unexpected roadway hazards, such as debris and roadblocks. In this study, we first introduce a roadway hazardous environment (both intentional and unintentional roadway hazards) that can compromise the DNN-based navigational system of an autonomous vehicle, and produces an incorrect steering wheel angle, which can cause crashes resulting in fatality and injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazardous environment, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system including hazardous object detection and semantic segmentation improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared to the traditional DNN-based autonomous vehicle driving system.
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks
Authors: Mahsa Ghorbani, Mahdieh Soleymani Baghshah, Hamid R. Rabiee
Date: 2018-11-21 00:00:00
Tasks: General Classification, Graph Embedding, Link Prediction, Network Embedding, Node Classification

Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attributes (if available) besides node relations and learn node embeddings for unsupervised and semi-supervised tasks on graphs. On the other hand, multi-layer graph analysis has been received attention recently. However, the existing methods for multi-layer graph embedding cannot incorporate all available information (like node attributes). Moreover, most of them consider either type of nodes or type of edges, and they do not treat within and between layer edges differently. In this paper, we propose a method called MGCN that utilizes the GCN for multi-layer graphs. MGCN embeds nodes of multi-layer graphs using both within and between layers relations and nodes attributes. We evaluate our method on the semi-supervised node classification task. Experimental results demonstrate the superiority of the proposed method to other multi-layer and single-layer competitors and also show the positive effect of using cross-layer edges.
Artificial Intelligence-Defined 5G Radio Access Networks
Authors: Miao Yao, Munawwar Sohul, Vuk Marojevic, Jeffrey H. Reed
Date: 2018-11-21 00:00:00

Massive multiple-input multiple-output antenna systems, millimeter wave
communications, and ultra-dense networks have been widely perceived as the
three key enablers that facilitate the development and deployment of 5G
systems. This article discusses the intelligent agent in 5G base station which
combines sensing, learning, understanding and optimizing to facilitate these
enablers. We present a flexible, rapidly deployable, and cross-layer artificial
intelligence (AI)-based framework to enable the imminent and future demands on
5G and beyond infrastructure. We present example AI-enabled 5G use cases that
accommodate important 5G-specific capabilities and discuss the value of AI for
enabling beyond 5G network evolution.
Learning Quadratic Games on Networks
Authors: Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland
Date: 2018-11-21 00:00:00
Proceeding: ICML 2020 1

Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations. Such strategic interactions are often modeled as games played on networks, where an individual's payoff depends not only on her action but also on that of her neighbors. The current literature has largely focused on analyzing the characteristics of network games in the scenario where the structure of the network, which is represented by a graph, is known beforehand. It is often the case, however, that the actions of the players are readily observable while the underlying interaction network remains hidden. In this paper, we propose two novel frameworks for learning, from the observations on individual actions, network games with linear-quadratic payoffs, and in particular, the structure of the interaction network. Our frameworks are based on the Nash equilibrium of such games and involve solving a joint optimization problem for the graph structure and the individual marginal benefits. Both synthetic and real-world experiments demonstrate the effectiveness of the proposed frameworks, which have theoretical as well as practical implications for understanding strategic interactions in a network environment.
Portmanteau test for the asymmetric power GARCH model when the power is unknown
Authors: Yacouba Boubacar Maïnassara, Othman Kadmiri, Bruno Saussereau
Date: 2018-11-21 00:00:00

It is now widely accepted that, to model the dynamics of daily financial
returns, volatility models have to incorporate the so-called leverage effect.
We derive the asymptotic behaviour of the squared residuals autocovariances for
the class of asymmetric power GARCH model when the power is unknown and is
jointly estimated with the model's parameters. We then deduce a portmanteau
adequacy test based on the autocovariances of the squared residuals. These
asymptotic results are illustrated by Monte Carlo experiments. An application
to real financial data is also proposed.
Regularizing by the Variance of the Activations' Sample-Variances
Authors: Etai Littwin, Lior Wolf
Date: 2018-11-21 00:00:00
Proceeding: NeurIPS 2018 12

Normalization techniques play an important role in supporting efficient and
often more effective training of deep neural networks. While conventional
methods explicitly normalize the activations, we suggest to add a loss term
instead. This new loss term encourages the variance of the activations to be
stable and not vary from one random mini-batch to the next. As we prove, this
encourages the activations to be distributed around a few distinct modes. We
also show that if the inputs are from a mixture of two Gaussians, the new loss
would either join the two together, or separate between them optimally in the
LDA sense, depending on the prior probabilities. Finally, we are able to link
the new regularization term to the batchnorm method, which provides it with a
regularization perspective. Our experiments demonstrate an improvement in
accuracy over the batchnorm technique for both CNNs and fully connected
networks.
Dynamic-Net: Tuning the Objective Without Re-training for Synthesis Tasks
Authors: Alon Shoshan, Roey Mechrez, Lihi Zelnik-Manor
Date: 2018-11-21 00:00:00
Proceeding: ICCV 2019 10

One of the key ingredients for successful optimization of modern CNNs is identifying a suitable objective. To date, the objective is fixed a-priori at training time, and any variation to it requires re-training a new network. In this paper we present a first attempt at alleviating the need for re-training. Rather than fixing the network at training time, we train a "Dynamic-Net" that can be modified at inference time. Our approach considers an "objective-space" as the space of all linear combinations of two objectives, and the Dynamic-Net is emulating the traversing of this objective-space at test-time, without any further training. We show that this upgrades pre-trained networks by providing an out-of-learning extension, while maintaining the performance quality. The solution we propose is fast and allows a user to interactively modify the network, in real-time, in order to obtain the result he/she desires. We show the benefits of such an approach via several different applications.
Using AI to Design Stone Jewelry
Authors: Khyatti Gupta, Sonam Damani, Kedhar Nath Narahari
Date: 2018-11-21 00:00:00
Tasks: BIG-bench Machine Learning, Cultural Vocal Bursts Intensity Prediction

Jewelry has been an integral part of human culture since ages. One of the
most popular styles of jewelry is created by putting together precious and
semi-precious stones in diverse patterns. While technology is finding its way
in the production process of such jewelry, designing it remains a
time-consuming and involved task. In this paper, we propose a unique approach
using optimization methods coupled with machine learning techniques to generate
novel stone jewelry designs at scale. Our evaluation shows that designs
generated by our approach are highly likeable and visually appealing.
The Best of Both Worlds: Lexical Resources To Improve Low-Resource Part-of-Speech Tagging
Authors: Barbara Plank, Sigrid Klerke, Zeljko Agic
Date: 2018-11-21 00:00:00
Tasks: Cross-Lingual POS Tagging, Part-Of-Speech Tagging, POS, POS Tagging

In natural language processing, the deep learning revolution has shifted the
focus from conventional hand-crafted symbolic representations to dense inputs,
which are adequate representations learned automatically from corpora. However,
particularly when working with low-resource languages, small amounts of
symbolic lexical resources such as user-generated lexicons are often available
even when gold-standard corpora are not. Such additional linguistic information
is though often neglected, and recent neural approaches to cross-lingual
tagging typically rely only on word and subword embeddings. While these
representations are effective, our recent work has shown clear benefits of
combining the best of both worlds: integrating conventional lexical information
improves neural cross-lingual part-of-speech (PoS) tagging. However, little is
known on how complementary such additional information is, and to what extent
improvements depend on the coverage and quality of these external resources.
This paper seeks to fill this gap by providing the first thorough analysis on
the contributions of lexical resources for cross-lingual PoS tagging in neural
times.