pwc-archive/papers-with-abstracts huggingface dataset:
Neural Constituency Parsing of Speech Transcripts
Authors: Paria Jamshid Lou, YuFei Wang, Mark Johnson
Date: 2019-04-17 00:00:00
Proceeding: NAACL 2019 6
Tasks: Constituency Parsing

This paper studies the performance of a neural self-attentive parser on transcribed speech. Speech presents parsing challenges that do not appear in written text, such as the lack of punctuation and the presence of speech disfluencies (including filled pauses, repetitions, corrections, etc.). Disfluencies are especially problematic for conventional syntactic parsers, which typically fail to find any EDITED disfluency nodes at all. This motivated the development of special disfluency detection systems, and special mechanisms added to parsers specifically to handle disfluencies. However, we show here that neural parsers can find EDITED disfluency nodes, and the best neural parsers find them with an accuracy surpassing that of specialized disfluency detection systems, thus making these specialized mechanisms unnecessary. This paper also investigates a modified loss function that puts more weight on EDITED nodes. It also describes tree-transformations that simplify the disfluency detection task by providing alternative encodings of disfluencies and syntactic information.
Do Lateral Views Help Automated Chest X-ray Predictions?
Authors: Hadrien Bertrand, Mohammad Hashir, Joseph Paul Cohen
Date: 2019-04-17 00:00:00

Most convolutional neural networks in chest radiology use only the frontal posteroanterior (PA) view to make a prediction. However the lateral view is known to help the diagnosis of certain diseases and conditions. The recently released PadChest dataset contains paired PA and lateral views, allowing us to study for which diseases and conditions the performance of a neural network improves when provided a lateral x-ray view as opposed to a frontal posteroanterior (PA) view. Using a simple DenseNet model, we find that using the lateral view increases the AUC of 8 of the 56 labels in our data and achieves the same performance as the PA view for 21 of the labels. We find that using the PA and lateral views jointly doesn't trivially lead to an increase in performance but suggest further investigation.
One Homonym per Translation
Authors: Bradley Hauer, Grzegorz Kondrak
Date: 2019-04-17 00:00:00
Tasks: Translation

The study of homonymy is vital to resolving fundamental problems in lexical semantics. In this paper, we propose four hypotheses that characterize the unique behavior of homonyms in the context of translations, discourses, collocations, and sense clusters. We present a new annotated homonym resource that allows us to test our hypotheses on existing WSD resources. The results of the experiments provide strong empirical evidence for the hypotheses. This study represents a step towards a computational method for distinguishing between homonymy and polysemy, and constructing a definitive inventory of coarse-grained senses.
Robust Exploration with Tight Bayesian Plausibility Sets
Authors: Reazul H. Russel, Tianyi Gu, Marek Petrik
Date: 2019-04-17 00:00:00
Tasks: Reinforcement Learning

Optimism about the poorly understood states and actions is the main driving
force of exploration for many provably-efficient reinforcement learning
algorithms. We propose optimism in the face of sensible value functions (OFVF)-
a novel data-driven Bayesian algorithm to constructing Plausibility sets for
MDPs to explore robustly minimizing the worst case exploration cost. The method
computes policies with tighter optimistic estimates for exploration by
introducing two new ideas. First, it is based on Bayesian posterior
distributions rather than distribution-free bounds. Second, OFVF does not
construct plausibility sets as simple confidence intervals. Confidence
intervals as plausibility sets are a sufficient but not a necessary condition.
OFVF uses the structure of the value function to optimize the location and
shape of the plausibility set to guarantee upper bounds directly without
necessarily enforcing the requirement for the set to be a confidence interval.
OFVF proceeds in an episodic manner, where the duration of the episode is fixed
and known. Our algorithm is inherently Bayesian and can leverage prior
information. Our theoretical analysis shows the robustness of OFVF, and the
empirical results demonstrate its practical promise.
Towards Open Intent Discovery for Conversational Text
Authors: Nikhita Vedula, Nedim Lipka, Pranav Maneriker, Srinivasan Parthasarathy
Date: 2019-04-17 00:00:00
Tasks: Intent Discovery, Open Intent Discovery

Detecting and identifying user intent from text, both written and spoken,
plays an important role in modelling and understand dialogs. Existing research
for intent discovery model it as a classification task with a predefined set of
known categories. To generailze beyond these preexisting classes, we define a
new task of \textit{open intent discovery}. We investigate how intent can be
generalized to those not seen during training. To this end, we propose a
two-stage approach to this task - predicting whether an utterance contains an
intent, and then tagging the intent in the input utterance. Our model consists
of a bidirectional LSTM with a CRF on top to capture contextual semantics,
subject to some constraints. Self-attention is used to learn long distance
dependencies. Further, we adapt an adversarial training approach to improve
robustness and perforamce across domains. We also present a dataset of 25k
real-life utterances that have been labelled via crowd sourcing. Our
experiments across different domains and real-world datasets show the
effectiveness of our approach, with less than 100 annotated examples needed per
unique domain to recognize diverse intents. The approach outperforms
state-of-the-art baselines by 5-15% F1 score points.
Graph based Dynamic Segmentation of Generic Objects in 3D
Authors: Xiao Lin, Josep R. Casas, Montse Pardàs
Date: 2019-04-17 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation

We propose a novel 3D segmentation method for RBGD stream data to deal with
3D object segmentation task in a generic scenario with frequent object
interactions. It mainly contributes in two aspects, while being generic and not
requiring initialization: firstly, a novel tree structure representation for
the point cloud of the scene is proposed. Then, a dynamic manangement mechanism
for connected component splits and merges exploits the tree structure
representation.
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks
Authors: Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Date: 2019-04-17 00:00:00

Neural Network classifiers have been used successfully in a wide range of
applications. However, their underlying assumption of attack free environment
has been defied by adversarial examples. Researchers tried to develop defenses;
however, existing approaches are still far from providing effective solutions
to this evolving problem. In this paper, we design a generative adversarial net
(GAN) based zero knowledge adversarial training defense, dubbed ZK-GanDef,
which does not consume adversarial examples during training. Therefore,
ZK-GanDef is not only efficient in training but also adaptive to new
adversarial examples. This advantage comes at the cost of small degradation in
test accuracy compared to full knowledge approaches. Our experiments show that
ZK-GanDef enhances test accuracy on adversarial examples by up-to 49.17%
compared to zero knowledge approaches. More importantly, its test accuracy is
close to that of the state-of-the-art full knowledge approaches (maximum
degradation of 8.46%), while taking much less training time.
DeepNovoV2: Better de novo peptide sequencing with deep learning
Authors: Rui Qiao, Ngoc Hieu Tran, Lei Xin, Baozhen Shan, Ming Li, Ali Ghodsi
Date: 2019-04-17 00:00:00
Tasks: Deep Learning, de novo peptide sequencing

Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy. The key step in developing personalized therapeutic cancer vaccines is to identify tumor-specific neoantigens that are on the surface of tumor cells. A promising method for this is through de novo peptide sequencing from mass spectrometry data. In this paper we introduce DeepNovoV2, the state-of-the-art model for peptide sequencing. In DeepNovoV2, a spectrum is directly represented as a set of (m/z, intensity) pairs, therefore it does not suffer from the accuracy-speed/memory trade-off problem. The model combines an order invariant network structure (T-Net) and recurrent neural networks and provides a complete end-to-end training and prediction framework to sequence patterns of peptides. Our experiments on a wide variety of data from different species show that DeepNovoV2 outperforms previous state-of-the-art methods, achieving 13.01-23.95\% higher accuracy at the peptide level.
MorphIC: A 65-nm 738k-Synapse/mm$^2$ Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Authors: Charlotte Frenkel, Jean-Didier Legat, David Bol
Date: 2019-04-17 00:00:00
Tasks: 2k, Quantization

Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of binary weights, which were demonstrated to have a limited accuracy reduction on many applications when quantization-aware training techniques are used. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning appears as a key feature for applications constrained in power and resources during the training phase. However, designing power- and area-efficient spiking neural networks still requires the development of specific techniques in order to leverage on-chip online learning on binary weights without compromising the synapse density. In this work, we demonstrate MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning rule and a hierarchical routing fabric for large-scale chip interconnection. The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF) neurons and more than two million plastic synapses for an active silicon area of 2.86mm$^2$ in 65nm CMOS, achieving a high density of 738k synapses/mm$^2$. MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while having no penalty in the energy-accuracy tradeoff.
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs
Authors: Behzad Ghazanfari, Fatemeh Afghah, Kayvan Najarian, Sajad Mousavi, Jonathan Gryak, James Todd
Date: 2019-04-17 00:00:00
Tasks: Clustering, Specificity

The high rate of false alarms in intensive care units (ICUs) is one of the
top challenges of using medical technology in hospitals. These false alarms are
often caused by patients' movements, detachment of monitoring sensors, or
different sources of noise and interference that impact the collected signals
from different monitoring devices. In this paper, we propose a novel set of
high-level features based on unsupervised feature learning technique in order
to effectively capture the characteristics of different arrhythmia in
electrocardiogram (ECG) signal and differentiate them from irregularity in
signals due to different sources of signal disturbances. This unsupervised
feature learning technique, first extracts a set of low-level features from all
existing heart cycles of a patient, and then clusters these segments for each
individual patient to provide a set of prominent high-level features. The
objective of the clustering phase is to enable the classification method to
differentiate between the high-level features extracted from normal and
abnormal cycles (i.e., either due to arrhythmia or different sources of
distortions in signal) in order to put more attention to the features extracted
from abnormal portion of the signal that contribute to the alarm. The
performance of this method is evaluated using the 2015 PhysioNet/Computing in
Cardiology Challenge dataset for reducing false arrhythmia alarms in the ICUs.
As confirmed by the experimental results, the proposed method offers a
considerable performance in terms of accuracy, sensitivity and specificity of
alarm detection only using a few high-level features that are extracted from
one single lead ECG signal.
Learning Interpretable Disentangled Representations using Adversarial VAEs
Authors: Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni
Date: 2019-04-17 00:00:00
Tasks: Clustering, Disentanglement, General Classification

Learning Interpretable representation in medical applications is becoming
essential for adopting data-driven models into clinical practice. It has been
recently shown that learning a disentangled feature representation is important
for a more compact and explainable representation of the data. In this paper,
we introduce a novel adversarial variational autoencoder with a total
correlation constraint to enforce independence on the latent representation
while preserving the reconstruction fidelity. Our proposed method is validated
on a publicly available dataset showing that the learned disentangled
representation is not only interpretable, but also superior to the
state-of-the-art methods. We report a relative improvement of 81.50% in terms
of disentanglement, 11.60% in clustering, and 2% in supervised classification
with a few amounts of labeled data.
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
Authors: Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar, Chinmay Hegde
Date: 2019-04-17 00:00:00
Proceeding: ICCV 2019 10

Deep neural networks have been shown to exhibit an intriguing vulnerability to adversarial input images corrupted with imperceptible perturbations. However, the majority of adversarial attacks assume global, fine-grained control over the image pixel space. In this paper, we consider a different setting: what happens if the adversary could only alter specific attributes of the input image? These would generate inputs that might be perceptibly different, but still natural-looking and enough to fool a classifier. We propose a novel approach to generate such `semantic' adversarial examples by optimizing a particular adversarial loss over the range-space of a parametric conditional generative model. We demonstrate implementations of our attacks on binary classifiers trained on face images, and show that such natural-looking semantic adversarial examples exist. We evaluate the effectiveness of our attack on synthetic and real data, and present detailed comparisons with existing attack methods. We supplement our empirical results with theoretical bounds that demonstrate the existence of such parametric adversarial examples.
Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
Authors: Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, In So Kweon
Date: 2019-04-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Metric Learning, One-Shot Learning, Translation

In daily life, graphic symbols, such as traffic signs and brand logos, are
ubiquitously utilized around us due to its intuitive expression beyond language
boundary. We tackle an open-set graphic symbol recognition problem by one-shot
classification with prototypical images as a single training example for each
novel class. We take an approach to learn a generalizable embedding space for
novel tasks. We propose a new approach called variational prototyping-encoder
(VPE) that learns the image translation task from real-world input images to
their corresponding prototypical images as a meta-task. As a result, VPE learns
image similarity as well as prototypical concepts which differs from widely
used metric learning based approaches. Our experiments with diverse datasets
demonstrate that the proposed VPE performs favorably against competing metric
learning based one-shot methods. Also, our qualitative analyses show that our
meta-task induces an effective embedding space suitable for unseen data
representation.
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning
Authors: Yaoyao Liu, Bernt Schiele, Qianru Sun
Date: 2019-04-17 00:00:00
Proceeding: ECCV 2020 8
Tasks: Few-Shot Learning, Meta-Learning

Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. "Epoch-wise" means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. "Empirical" means that the hyperparameters, e.g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent vs. epoch-independent, in the paradigm of meta-learning. We conduct extensive experiments for five-class few-shot tasks on three challenging benchmarks: miniImageNet, tieredImageNet, and FC100, and achieve top performance using the epoch-dependent transductive hyperprior learner, which captures the richest information. Our ablation study shows that both "epoch-wise ensemble" and "empirical" encourage high efficiency and robustness in the model performance.
A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts
Authors: Negin Musavi
Date: 2019-04-17 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the
National Aerospace (NAS) is a complex problem which is required to be addressed
by simulation studies before allowing the routine access of UAS into the NAS.
This thesis focuses on providing 2D and 3D simulation frameworks using a game
theoretical methodology to evaluate integration concepts in scenarios where
manned and unmanned air vehicles co-exist. The fundamental gap in the
literature is that the models of interaction between manned and unmanned
vehicles are insufficient: a) they assume that pilot behavior is known a priori
and b) they disregard decision making processes. The contribution of this work
is to propose a modeling framework, in which, human pilot reactions are modeled
using reinforcement learning and a game theoretical concept called level-k
reasoning to fill this gap. The level-k reasoning concept is based on the
assumption that humans have various levels of decision making. Reinforcement
learning is a mathematical learning method that is rooted in human learning. In
this work, a classical and an approximate reinforcement learning (Neural Fitted
Q Iteration) methods are used to model time-extended decisions of pilots with
2D and 3D maneuvers. An analysis of UAS integration is conducted using example
scenarios in the presence of manned aircraft and fully autonomous UAS equipped
with sense and avoid algorithms.
Image Resizing by Reconstruction from Deep Features
Authors: Moab Arar, Dov Danon, Daniel Cohen-Or, Ariel Shamir
Date: 2019-04-17 00:00:00

Traditional image resizing methods usually work in pixel space and use various saliency measures. The challenge is to adjust the image shape while trying to preserve important content. In this paper we perform image resizing in feature space where the deep layers of a neural network contain rich important semantic information. We directly adjust the image feature maps, extracted from a pre-trained classification network, and reconstruct the resized image using a neural-network based optimization. This novel approach leverages the hierarchical encoding of the network, and in particular, the high-level discriminative power of its deeper layers, that recognizes semantic objects and regions and allows maintaining their aspect ratio. Our use of reconstruction from deep features diminishes the artifacts introduced by image-space resizing operators. We evaluate our method on benchmarks, compare to alternative approaches, and demonstrate its strength on challenging images.
Off-Policy Policy Gradient with State Distribution Correction
Authors: Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
Date: 2019-04-17 00:00:00

We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approaches have generally ignored the mismatch between the distribution of states visited under the behavior policy used to collect data, and what would be the distribution of states under the learned policy. Here we build on recent progress for estimating the ratio of the state distributions under behavior and evaluation policies for policy evaluation, and present an off-policy policy gradient optimization technique that can account for this mismatch in distributions. We present an illustrative example of why this is important and a theoretical convergence guarantee for our approach. Empirically, we compare our method in simulations to several strong baselines which do not correct for this mismatch, significantly improving in the quality of the policy discovered.
Towards Evolutionary Theorem Proving for Isabelle/HOL
Authors: Yutaka Nagashima
Date: 2019-04-17 00:00:00
Tasks: Automated Theorem Proving

Mechanized theorem proving is becoming the basis of reliable systems
programming and rigorous mathematics. Despite decades of progress in proof
automation, writing mechanized proofs still requires engineers' expertise and
remains labor intensive. Recently, researchers have extracted heuristics of
interactive proof development from existing large proof corpora using
supervised learning. However, such existing proof corpora present only one way
of proving conjectures, while there are often multiple equivalently effective
ways to prove one conjecture. In this abstract, we identify challenges in
discovering heuristics for automatic proof search and propose our novel
approach to improve heuristics of automatic proof search in Isabelle/HOL using
evolutionary computation.
DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation
Authors: Zhenlin Xu, Marc Niethammer
Date: 2019-04-17 00:00:00
Tasks: Data Augmentation, Image Registration, Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Registration

Deep convolutional neural networks (CNNs) are state-of-the-art for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. Motivated by classical approaches for joint segmentation and registration we therefore propose a deep learning framework that jointly learns networks for image registration and image segmentation. In contrast to previous work on deep unsupervised image registration, which showed the benefit of weak supervision via image segmentations, our approach can use existing segmentations when available and computes them via the segmentation network otherwise, thereby providing the same registration benefit. Conversely, segmentation network training benefits from the registration, which essentially provides a realistic form of data augmentation. Experiments on knee and brain 3D magnetic resonance (MR) images show that our approach achieves large simultaneous improvements of segmentation and registration accuracy (over independently trained networks) and allows training high-quality models with very limited training data. Specifically, in a one-shot-scenario (with only one manually labeled image) our approach increases Dice scores (%) over an unsupervised registration network by 2.7 and 1.8 on the knee and brain images respectively.