pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Importance Sample in Primary Sample Space
Authors: Quan Zheng, Matthias Zwicker
Date: 2018-08-23 00:00:00

Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. In this paper, we propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the rendering algorithm using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real-valued non-volume preserving ('Real NVP') transformations in high dimensional spaces. We use Real NVP to non-linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with many existing rendering techniques by treating them as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.
High frame-rate cardiac ultrasound imaging with deep learning
Authors: Ortal Senouf, Sanketh Vedula, Grigoriy Zurakhov, Alex M. Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, David Blondheim
Date: 2018-08-23 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

Cardiac ultrasound imaging requires a high frame rate in order to capture
rapid motion. This can be achieved by multi-line acquisition (MLA), where
several narrow-focused received lines are obtained from each wide-focused
transmitted line. This shortens the acquisition time at the expense of
introducing block artifacts. In this paper, we propose a data-driven
learning-based approach to improve the MLA image quality. We train an
end-to-end convolutional neural network on pairs of real ultrasound cardiac
data, acquired through MLA and the corresponding single-line acquisition (SLA).
The network achieves a significant improvement in image quality for both $5-$
and $7-$line MLA resulting in a decorrelation measure similar to that of SLA
while having the frame rate of MLA.
High quality ultrasonic multi-line transmission through deep learning
Authors: Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov, Alex M. Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, Diana Gaitini
Date: 2018-08-23 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D
sonography. Several methods have been proposed in the medical ultrasound
literature aiming at accelerating the image acquisition. In this paper, we
consider one such method called \textit{multi-line transmission} (MLT), in
which several evenly separated focused beams are transmitted simultaneously.
While MLT reduces the acquisition time, it comes at the expense of a heavy loss
of contrast due to the interactions between the beams (cross-talk artifact). In
this paper, we introduce a data-driven method to reduce the artifacts arising
in MLT. To this end, we propose to train an end-to-end convolutional neural
network consisting of correction layers followed by a constant apodization
layer. The network is trained on pairs of raw data obtained through MLT and the
corresponding \textit{single-line transmission} (SLT) data. Experimental
evaluation demonstrates significant improvement both in the visual image
quality and in objective measures such as contrast ratio and contrast-to-noise
ratio, while preserving resolution unlike traditional apodization-based
methods. We show that the proposed method is able to generalize well across
different patients and anatomies on real and phantom data.
Transfer Learning for Estimating Causal Effects using Neural Networks
Authors: Sören R. Künzel, Bradly C. Stadie, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, Pieter Abbeel
Date: 2018-08-23 00:00:00
Tasks: Causal Inference, Transfer Learning

We develop new algorithms for estimating heterogeneous treatment effects,
combining recent developments in transfer learning for neural networks with
insights from the causal inference literature. By taking advantage of transfer
learning, we are able to efficiently use different data sources that are
related to the same underlying causal mechanisms. We compare our algorithms
with those in the extant literature using extensive simulation studies based on
large-scale voter persuasion experiments and the MNIST database. Our methods
can perform an order of magnitude better than existing benchmarks while using a
fraction of the data.
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Authors: Carey E. Priebe, Youngser Park, Joshua T. Vogelstein, John M. Conroy, Vince Lyzinski, Minh Tang, Avanti Athreya, Joshua Cape, Eric Bridgeford
Date: 2018-08-23 00:00:00
Tasks: Clustering, Diffusion  MRI, Graph Clustering, Spectral Graph Clustering, Vocal Bursts Valence Prediction

Clustering is concerned with coherently grouping observations without any
explicit concept of true groupings. Spectral graph clustering - clustering the
vertices of a graph based on their spectral embedding - is commonly approached
via K-means (or, more generally, Gaussian mixture model) clustering composed
with either Laplacian or Adjacency spectral embedding (LSE or ASE). Recent
theoretical results provide new understanding of the problem and solutions, and
lead us to a 'Two Truths' LSE vs. ASE spectral graph clustering phenomenon
convincingly illustrated here via a diffusion MRI connectome data set: the
different embedding methods yield different clustering results, with LSE
capturing left hemisphere/right hemisphere affinity structure and ASE capturing
gray matter/white matter core-periphery structure.
Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval
Authors: Niluthpol Chowdhury Mithun, Rameswar Panda, Evangelos E. Papalexakis, Amit K. Roy-Chowdhury
Date: 2018-08-23 00:00:00
Tasks: Cross-Modal Retrieval, Image-text Retrieval, Retrieval, Text Retrieval

Cross-modal retrieval between visual data and natural language description
remains a long-standing challenge in multimedia. While recent image-text
retrieval methods offer great promise by learning deep representations aligned
across modalities, most of these methods are plagued by the issue of training
with small-scale datasets covering a limited number of images with ground-truth
sentences. Moreover, it is extremely expensive to create a larger dataset by
annotating millions of images with sentences and may lead to a biased model.
Inspired by the recent success of webly supervised learning in deep neural
networks, we capitalize on readily-available web images with noisy annotations
to learn robust image-text joint representation. Specifically, our main idea is
to leverage web images and corresponding tags, along with fully annotated
datasets, in training for learning the visual-semantic joint embedding. We
propose a two-stage approach for the task that can augment a typical supervised
pair-wise ranking loss based formulation with weakly-annotated web images to
learn a more robust visual-semantic embedding. Experiments on two standard
benchmark datasets demonstrate that our method achieves a significant
performance gain in image-text retrieval compared to state-of-the-art
approaches.
Time-Agnostic Prediction: Predicting Predictable Video Frames
Authors: Dinesh Jayaraman, Frederik Ebert, Alexei A. Efros, Sergey Levine
Date: 2018-08-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: Prediction

Prediction is arguably one of the most basic functions of an intelligent
system. In general, the problem of predicting events in the future or between
two waypoints is exceedingly difficult. However, most phenomena naturally pass
through relatively predictable bottlenecks---while we cannot predict the
precise trajectory of a robot arm between being at rest and holding an object
up, we can be certain that it must have picked the object up. To exploit this,
we decouple visual prediction from a rigid notion of time. While conventional
approaches predict frames at regularly spaced temporal intervals, our
time-agnostic predictors (TAP) are not tied to specific times so that they may
instead discover predictable "bottleneck" frames no matter when they occur. We
evaluate our approach for future and intermediate frame prediction across three
robotic manipulation tasks. Our predictions are not only of higher visual
quality, but also correspond to coherent semantic subgoals in temporally
extended tasks.
EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction
Authors: Abhinav Dhall, Amanjot Kaur, Roland Goecke, Tom Gedeon
Date: 2018-08-23 00:00:00
Tasks: Emotion Recognition, multimodal interaction

This paper details the sixth Emotion Recognition in the Wild (EmotiW)
challenge. EmotiW 2018 is a grand challenge in the ACM International Conference
on Multimodal Interaction 2018, Colorado, USA. The challenge aims at providing
a common platform to researchers working in the affective computing community
to benchmark their algorithms on `in the wild' data. This year EmotiW contains
three sub-challenges: a) Audio-video based emotion recognition; b) Student
engagement prediction; and c) Group-level emotion recognition. The databases,
protocols and baselines are discussed in detail.
Topology and Prediction Focused Research on Graph Convolutional Neural Networks
Authors: Matthew Baron
Date: 2018-08-23 00:00:00

Important advances have been made using convolutional neural network (CNN)
approaches to solve complicated problems in areas that rely on grid structured
data such as image processing and object classification. Recently, research on
graph convolutional neural networks (GCNN) has increased dramatically as
researchers try to replicate the success of CNN for graph structured data.
Unfortunately, traditional CNN methods are not readily transferable to GCNN,
given the irregularity and geometric complexity of graphs. The emerging field
of GCNN is further complicated by research papers that differ greatly in their
scope, detail, and level of academic sophistication needed by the reader.
  The present paper provides a review of some basic properties of GCNN. As a
guide to the interested reader, recent examples of GCNN research are then
grouped according to techniques that attempt to uncover the underlying topology
of the graph model and those that seek to generalize traditional CNN methods on
graph data to improve prediction of class membership. Discrete Signal
Processing on Graphs (DSPg) is used as a theoretical framework to better
understand some of the performance gains and limitations of these recent GCNN
approaches. A brief discussion of Topology Adaptive Graph Convolutional
Networks (TAGCN) is presented as an approach motivated by DSPg and future
research directions using this approach are briefly discussed.
Segmentation of Bleeding Regions in Wireless Capsule Endoscopy for Detection of Informative Frames
Authors: Mohsen Hajabdollahi, Reza Esfandiarpoor, Pejman Khadivi, S. M. Reza Soroushmehr, Nader Karimi, Kayvan Najarian, Shadrokh Samavi
Date: 2018-08-23 00:00:00
Tasks: image-classification, Image Classification

Wireless capsule endoscopy (WCE) is an effective mean for diagnosis of
gastrointestinal disorders. Detection of informative scenes in WCE video could
reduce the length of transmitted videos and help the diagnosis procedure. In
this paper, we investigate the problem of simplification of neural networks for
automatic bleeding region detection inside capsule endoscopy device. Suitable
color channels are selected as neural networks inputs, and image classification
is conducted using a multi-layer perceptron (MLP) and a convolutional neural
network (CNN) separately. Both CNN and MLP structures are simplified to reduce
the number of computational operations. Performances of two simplified networks
are evaluated on a WCE bleeding image dataset using the DICE score. Simulation
results show that applying simplification methods on both MLP and CNN
structures reduces the number of computational operations significantly with
AUC greater than 0.97. Although CNN performs better in comparison with
simplified MLP, the simplified MLP segments bleeding regions with a
significantly smaller number of computational operations. Concerning the
importance of having a simple structure or a more accurate model, each of the
designed structures could be selected for inside capsule implementation.
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits
Authors: Fabien C. Y. Benureau, Pierre-Yves Oudeyer
Date: 2018-08-23 00:00:00
Tasks: Diversity, Multi-Armed Bandits

We consider a scenario where an agent has multiple available strategies to
explore an unknown environment. For each new interaction with the environment,
the agent must select which exploration strategy to use. We provide a new
strategy-agnostic method that treat the situation as a Multi-Armed Bandits
problem where the reward signal is the diversity of effects that each strategy
produces. We test the method empirically on a simulated planar robotic arm, and
establish that the method is both able discriminate between strategies of
dissimilar quality, even when the differences are tenuous, and that the
resulting performance is competitive with the best fixed mixture of strategies.
Revisiting the Importance of Encoding Logic Rules in Sentiment Classification
Authors: Kalpesh Krishna, Preethi Jyothi, Mohit Iyyer
Date: 2018-08-23 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

We analyze the performance of different sentiment classification models on
syntactically complex inputs like A-but-B sentences. The first contribution of
this analysis addresses reproducible research: to meaningfully compare
different models, their accuracies must be averaged over far more random seeds
than what has traditionally been reported. With proper averaging in place, we
notice that the distillation model described in arXiv:1603.06318v4 [cs.LG],
which incorporates explicit logic rules for sentiment classification, is
ineffective. In contrast, using contextualized ELMo embeddings
(arXiv:1802.05365v2 [cs.CL]) instead of logic rules yields significantly better
performance. Additionally, we provide analysis and visualizations that
demonstrate ELMo's ability to implicitly learn logic rules. Finally, a
crowdsourced analysis reveals how ELMo outperforms baseline models even on
sentences with ambiguous sentiment labels.
Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs
Authors: Dimitri Kartsaklis, Mohammad Taher Pilehvar, Nigel Collier
Date: 2018-08-23 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, Sentence, Word Embeddings

This paper addresses the problem of mapping natural language text to
knowledge base entities. The mapping process is approached as a composition of
a phrase or a sentence into a point in a multi-dimensional entity space
obtained from a knowledge graph. The compositional model is an LSTM equipped
with a dynamic disambiguation mechanism on the input word embeddings (a
Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base
space is prepared by collecting random walks from a graph enhanced with textual
features, which act as a set of semantic bridges between text and knowledge
base entities. The ideas of this work are demonstrated on large-scale
text-to-entity mapping and entity classification tasks, with state of the art
results.
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Authors: Meysam Sadeghi, Erik G. Larsson
Date: 2018-08-23 00:00:00
Tasks: Classification, Deep Learning, General Classification

Deep learning (DL), despite its enormous success in many computer vision and
language processing applications, is exceedingly vulnerable to adversarial
attacks. We consider the use of DL for radio signal (modulation) classification
tasks, and present practical methods for the crafting of white-box and
universal black-box adversarial attacks in that application. We show that these
attacks can considerably reduce the classification performance, with extremely
small perturbations of the input. In particular, these attacks are
significantly more powerful than classical jamming attacks, which raises
significant security and robustness concerns in the use of DL-based algorithms
for the wireless physical layer.
Predicting Action Tubes
Authors: Gurkirt Singh, Suman Saha, Fabio Cuzzolin
Date: 2018-08-23 00:00:00
Tasks: Action Classification, Action Detection, Autonomous Driving

In this work, we present a method to predict an entire `action tube' (a set
of temporally linked bounding boxes) in a trimmed video just by observing a
smaller subset of it. Predicting where an action is going to take place in the
near future is essential to many computer vision based applications such as
autonomous driving or surgical robotics. Importantly, it has to be done in
real-time and in an online fashion. We propose a Tube Prediction network
(TPnet) which jointly predicts the past, present and future bounding boxes
along with their action classification scores. At test time TPnet is used in a
(temporal) sliding window setting, and its predictions are put into a tube
estimation framework to construct/predict the video long action tubes not only
for the observed part of the video but also for the unobserved part.
Additionally, the proposed action tube predictor helps in completing action
tubes for unobserved segments of the video. We quantitatively demonstrate the
latter ability, and the fact that TPnet improves state-of-the-art detection
performance, on one of the standard action detection benchmarks - J-HMDB-21
dataset.
Discriminative out-of-distribution detection for semantic segmentation
Authors: Petra Bevandić, Ivan Krešo, Marin Oršić, Siniša Šegvić
Date: 2018-08-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: Out-of-Distribution Detection, Semantic Segmentation

Most classification and segmentation datasets assume a closed-world scenario
in which predictions are expressed as distribution over a predetermined set of
visual classes. However, such assumption implies unavoidable and often
unnoticeable failures in presence of out-of-distribution (OOD) input. These
failures are bound to happen in most real-life applications since current
visual ontologies are far from being comprehensive. We propose to address this
issue by discriminative detection of OOD pixels in input data. Different from
recent approaches, we avoid to bring any decisions by only observing the
training dataset of the primary model trained to solve the desired computer
vision task. Instead, we train a dedicated OOD model which discriminates the
primary training set from a much larger "background" dataset which approximates
the variety of the visual world. We perform our experiments on high resolution
natural images in a dense prediction setup. We use several road driving
datasets as our training distribution, while we approximate the background
distribution with the ILSVRC dataset. We evaluate our approach on WildDash
test, which is currently the only public test dataset that includes
out-of-distribution images. The obtained results show that the proposed
approach succeeds to identify out-of-distribution pixels while outperforming
previous work by a wide margin.
End-to-End Neural Entity Linking
Authors: Nikolaos Kolitsas, Octavian-Eugen Ganea, Thomas Hofmann
Date: 2018-08-23 00:00:00
Proceeding: CONLL 2018 10
Tasks: Entity Disambiguation, Entity Embeddings, Entity Linking, NER

Entity Linking (EL) is an essential task for semantic text understanding and
information extraction. Popular methods separately address the Mention
Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging
their mutual dependency. We here propose the first neural end-to-end EL system
that jointly discovers and links entities in a text document. The main idea is
to consider all possible spans as potential mentions and learn contextual
similarity scores over their entity candidates that are useful for both MD and
ED decisions. Key components are context-aware mention embeddings, entity
embeddings and a probabilistic mention - entity map, without demanding other
engineered features. Empirically, we show that our end-to-end method
significantly outperforms popular systems on the Gerbil platform when enough
training data is available. Conversely, if testing datasets follow different
annotation conventions compared to the training set (e.g. queries/ tweets vs
news documents), our ED model coupled with a traditional NER system offers the
best or second best EL accuracy.
A Directionally Selective Neural Network with Separated ON and OFF Pathways for Translational Motion Perception in a Visually Cluttered Environment
Authors: Qinbing Fu, Nicola Bellotto, Shigang Yue
Date: 2018-08-23 00:00:00

With respect to biological findings underlying fly's physiology in the past
decade, we present a directionally selective neural network, with a
feed-forward structure and entirely low-level visual processing, so as to
implement direction selective neurons in the fly's visual system, which are
mainly sensitive to wide-field translational movements in four cardinal
directions. In this research, we highlight the functionality of ON and OFF
pathways, separating motion information for parallel computation corresponding
to light-on and light-off selectivity. Through this modeling study, we
demonstrate several achievements compared with former bio-plausible
translational motion detectors, like the elementary motion detectors. First, we
thoroughly mimic the fly's preliminary motion-detecting pathways with newly
revealed fly's physiology. Second, we improve the speed response to moving
dark/light features via the design of ensembles of same polarity cells in the
dual-pathways. Moreover, we alleviate the impact of irrelevant motion in a
visually cluttered environment like the shifting of background and windblown
vegetation, via the modeling of spatiotemporal dynamics. We systematically
tested the DSNN against stimuli ranging from synthetic and real-world scenes,
to notably a visual modality of a ground micro robot. The results demonstrated
that the DSNN outperforms former bio-plausible translational motion detectors.
Importantly, we verified its computational simplicity and effectiveness
benefiting the building of neuromorphic vision sensor for robots.
Role of Intonation in Scoring Spoken English
Authors: Amber Nigam, Arpan Saxena, Ishan Sodhi
Date: 2018-08-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Sentence, speech-recognition, Speech Recognition, Speech-to-Text

In this paper, we have introduced and evaluated intonation based feature for
scoring the English speech of nonnative English speakers in Indian context. For
this, we created an automated spoken English scoring engine to learn from the
manual evaluation of spoken English. This involved using an existing Automatic
Speech Recognition (ASR) engine to convert the speech to text. Thereafter,
macro features like accuracy, fluency and prosodic features were used to build
a scoring model. In the process, we introduced SimIntonation, short for
similarity between spoken intonation pattern and "ideal" i.e. training
intonation pattern. Our results show that it is a highly predictive feature
under controlled environment. We also categorized interword pauses into 4
distinct types for a granular evaluation of pauses and their impact on speech
evaluation. Moreover, we took steps to moderate test difficulty through its
evaluation across parameters like difficult word count, average sentence
readability and lexical density. Our results show that macro features like
accuracy and intonation, and micro features like pause-topography are strongly
predictive. The scoring of spoken English is not within the purview of this
paper.