pwc-archive/papers-with-abstracts huggingface dataset:
Factorising AMR generation through syntax
Authors: Kris Cao, Stephen Clark
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2019 6
Tasks: Abstract Meaning Representation

Generating from Abstract Meaning Representation (AMR) is an underspecified
problem, as many syntactic decisions are not constrained by the semantic graph.
To explicitly account for this underspecification, we break down generating
from AMR into two steps: first generate a syntactic structure, and then
generate the surface form. We show that decomposing the generation process this
way leads to state-of-the-art single model performance generating from AMR
without additional unlabelled data. We also demonstrate that we can generate
meaning-preserving syntactic paraphrases of the same AMR graph, as judged by
humans.
Lightweight Adaptive Mixture of Neural and N-gram Language Models
Authors: Anton Bakhtin, Arthur Szlam, Marc'Aurelio Ranzato, Edouard Grave
Date: 2018-04-20 00:00:00
Tasks: Language Modeling, Language Modelling

It is often the case that the best performing language model is an ensemble
of a neural language model with n-grams. In this work, we propose a method to
improve how these two models are combined. By using a small network which
predicts the mixture weight between the two models, we adapt their relative
importance at each time step. Because the gating network is small, it trains
quickly on small amounts of held out data, and does not add overhead at scoring
time. Our experiments carried out on the One Billion Word benchmark show a
significant improvement over the state of the art ensemble without retraining
of the basic modules.
Facial Attributes: Accuracy and Adversarial Robustness
Authors: Andras Rozsa, Manuel Günther, Ethan M. Rudd, Terrance E. Boult
Date: 2018-01-04 00:00:00
Tasks: Adversarial Robustness, Attribute, General Classification

Facial attributes, emerging soft biometrics, must be automatically and
reliably extracted from images in order to be usable in stand-alone systems.
While recent methods extract facial attributes using deep neural networks
(DNNs) trained on labeled facial attribute data, the robustness of deep
attribute representations has not been evaluated. In this paper, we examine the
representational stability of several approaches that recently advanced the
state of the art on the CelebA benchmark by generating adversarial examples
formed by adding small, non-random perturbations to inputs yielding altered
classifications. We show that our fast flipping attribute (FFA) technique
generates more adversarial examples than traditional algorithms, and that the
adversarial robustness of DNNs varies highly between facial attributes. We also
test the correlation of facial attributes and find that only for related
attributes do the formed adversarial perturbations change the classification of
others. Finally, we introduce the concept of natural adversarial samples, i.e.,
misclassified images where predictions can be corrected via small
perturbations. We demonstrate that natural adversarial samples commonly occur
and show that many of these images remain misclassified even with additional
training epochs, even though their correct classification may require only a
small adjustment to network parameters.
Robust Estimation via Robust Gradient Estimation
Authors: Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar
Date: 2018-02-19 00:00:00
Tasks: regression

We provide a new computationally-efficient class of estimators for risk
minimization. We show that these estimators are robust for general statistical
models: in the classical Huber epsilon-contamination model and in heavy-tailed
settings. Our workhorse is a novel robust variant of gradient descent, and we
provide conditions under which our gradient descent variant provides accurate
estimators in a general convex risk minimization problem. We provide specific
consequences of our theory for linear regression, logistic regression and for
estimation of the canonical parameters in an exponential family. These results
provide some of the first computationally tractable and provably robust
estimators for these canonical statistical models. Finally, we study the
empirical performance of our proposed methods on synthetic and real datasets,
and find that our methods convincingly outperform a variety of baselines.
Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Authors: Kaixiang Mo, Yu Zhang, Qiang Yang, Pascale Fung
Date: 2018-04-20 00:00:00

Dialogue policy transfer enables us to build dialogue policies in a target
domain with little data by leveraging knowledge from a source domain with
plenty of data. Dialogue sentences are usually represented by speech-acts and
domain slots, and the dialogue policy transfer is usually achieved by assigning
a slot mapping matrix based on human heuristics. However, existing dialogue
policy transfer methods cannot transfer across dialogue domains with different
speech-acts, for example, between systems built by different companies. Also,
they depend on either common slots or slot entropy, which are not available
when the source and target slots are totally disjoint and no database is
available to calculate the slot entropy. To solve this problem, we propose a
Policy tRansfer across dOMaIns and SpEech-acts (PROMISE) model, which is able
to transfer dialogue policies across domains with different speech-acts and
disjoint slots. The PROMISE model can learn to align different speech-acts and
slots simultaneously, and it does not require common slots or the calculation
of the slot entropy. Experiments on both real-world dialogue data and
simulations demonstrate that PROMISE model can effectively transfer dialogue
policies across domains with different speech-acts and disjoint slots.
Robust Multilingual Part-of-Speech Tagging via Adversarial Training
Authors: Michihiro Yasunaga, Jungo Kasai, Dragomir Radev
Date: 2017-11-14 00:00:00
Proceeding: NAACL 2018 6
Tasks: Chunking, Dependency Parsing, Named Entity Recognition (NER), Part-Of-Speech Tagging, POS, POS Tagging

Adversarial training (AT) is a powerful regularization method for neural
networks, aiming to achieve robustness to input perturbations. Yet, the
specific effects of the robustness obtained from AT are still unclear in the
context of natural language processing. In this paper, we propose and analyze a
neural POS tagging model that exploits AT. In our experiments on the Penn
Treebank WSJ corpus and the Universal Dependencies (UD) dataset (27 languages),
we find that AT not only improves the overall tagging accuracy, but also 1)
prevents over-fitting well in low resource languages and 2) boosts tagging
accuracy for rare / unseen words. We also demonstrate that 3) the improved
tagging performance by AT contributes to the downstream task of dependency
parsing, and that 4) AT helps the model to learn cleaner word representations.
5) The proposed AT model is generally effective in different sequence labeling
tasks. These positive results motivate further use of AT for natural language
tasks.
Zero-Shot Learning via Latent Space Encoding
Authors: Yunlong Yu, Zhong Ji, Jichang Guo, Zhongfei, Zhang
Date: 2017-12-26 00:00:00
Tasks: Decoder, Retrieval, Zero-Shot Learning

Zero-Shot Learning (ZSL) is typically achieved by resorting to a class
semantic embedding space to transfer the knowledge from the seen classes to
unseen ones. Capturing the common semantic characteristics between the visual
modality and the class semantic modality (e.g., attributes or word vector) is a
key to the success of ZSL. In this paper, we propose a novel encoder-decoder
approach, namely Latent Space Encoding (LSE), to connect the semantic relations
of different modalities. Instead of requiring a projection function to transfer
information across different modalities like most previous work, LSE per- forms
the interactions of different modalities via a feature aware latent space,
which is learned in an implicit way. Specifically, different modalities are
modeled separately but optimized jointly. For each modality, an encoder-decoder
framework is performed to learn a feature aware latent space via jointly
maximizing the recoverability of the original space from the latent space and
the predictability of the latent space from the original space. To relate
different modalities together, their features referring to the same concept are
enforced to share the same latent codings. In this way, the common semantic
characteristics of different modalities are generalized with the latent
representations. Another property of the proposed approach is that it is easily
extended to more modalities. Extensive experimental results on four benchmark
datasets (AwA, CUB, aPY, and ImageNet) clearly demonstrate the superiority of
the proposed approach on several ZSL tasks, including traditional ZSL,
generalized ZSL, and zero-shot retrieval (ZSR).
Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Authors: Shen Li, Christian Häger, Nil Garcia, Henk Wymeersch
Date: 2018-04-20 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning is used to compute achievable information rates (AIRs) for a
simplified fiber channel. The approach jointly optimizes the input distribution
(constellation shaping) and the auxiliary channel distribution to compute AIRs
without explicit channel knowledge in an end-to-end fashion.
Unsupervised learning of the brain connectivity dynamic using residual D-net
Authors: Youngjoo Seo, Manuel Morante, Yannis Kopsinis, Sergios Theodoridis
Date: 2018-04-20 00:00:00
Tasks: Deep Learning, General Classification

In this paper, we propose a novel unsupervised learning method to learn the
brain dynamics using a deep learning architecture named residual D-net. As it
is often the case in medical research, in contrast to typical deep learning
tasks, the size of the resting-state functional Magnetic Resonance Image
(rs-fMRI) datasets for training is limited. Thus, the available data should be
very efficiently used to learn the complex patterns underneath the brain
connectivity dynamics. To address this issue, we use residual connections to
alleviate the training complexity through recurrent multi-scale representation.
We conduct two classification tasks to differentiate early and late stage Mild
Cognitive Impairment (MCI) from Normal healthy Control (NC) subjects. The
experiments verify that our proposed residual D-net indeed learns the brain
connectivity dynamics, leading to significantly higher classification accuracy
compared to previously published techniques.
Modelling customer online behaviours with neural networks: applications to conversion prediction and advertising retargeting
Authors: Yanwei Cui, Rogatien Tobossi, Olivia Vigouroux
Date: 2018-04-20 00:00:00
Tasks: Marketing

In this paper, we apply neural networks into digital marketing world for the
purpose of better targeting the potential customers. To do so, we model the
customer online behaviours using dedicated neural network architectures.
Starting from user searched keywords in a search engine to the landing page and
different following pages, until the user left the site, we model the whole
visited journey with a Recurrent Neural Network (RNN), together with
Convolution Neural Networks (CNN) that can take into account of the semantic
meaning of user searched keywords and different visited page names. With such
model, we use Monte Carlo simulation to estimate the conversion rates of each
potential customer in the future visiting. We believe our concept and the
preliminary promising results in this paper enable the use of largely available
customer online behaviours data for advanced digital marketing analysis.
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
Authors: Yu-Wei Chao, Sudheendra Vijayanarasimhan, Bryan Seybold, David A. Ross, Jia Deng, Rahul Sukthankar
Date: 2018-04-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Classification, Action Localization, General Classification, object-detection, Object Detection, Temporal Action Localization

We propose TAL-Net, an improved approach to temporal action localization in
video that is inspired by the Faster R-CNN object detection framework. TAL-Net
addresses three key shortcomings of existing approaches: (1) we improve
receptive field alignment using a multi-scale architecture that can accommodate
extreme variation in action durations; (2) we better exploit the temporal
context of actions for both proposal generation and action classification by
appropriately extending receptive fields; and (3) we explicitly consider
multi-stream feature fusion and demonstrate that fusing motion late is
important. We achieve state-of-the-art performance for both action proposal and
localization on THUMOS'14 detection benchmark and competitive performance on
ActivityNet challenge.
An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics
Authors: Andreas Steyven, Emma Hart, Ben Paechter
Date: 2018-04-20 00:00:00

A robotic swarm that is required to operate for long periods in a potentially
unknown environment can use both evolution and individual learning methods in
order to adapt. However, the role played by the environment in influencing the
effectiveness of each type of learning is not well understood. In this paper,
we address this question by analysing the performance of a swarm in a range of
simulated, dynamic environments where a distributed evolutionary algorithm for
evolving a controller is augmented with a number of different individual
learning mechanisms. The learning mechanisms themselves are defined by
parameters which can be either fixed or inherited. We conduct experiments in a
range of dynamic environments whose characteristics are varied so as to present
different opportunities for learning. Results enable us to map environmental
characteristics to the most effective learning algorithm.
Super-resolution Ultrasound Localization Microscopy through Deep Learning
Authors: Ruud J. G. van Sloun, Oren Solomon, Matthew Bruce, Zin Z. Khaing, Hessel Wijkstra, Yonina C. Eldar, Massimo Mischi
Date: 2018-04-20 00:00:00
Tasks: Deep Learning, GPU, Super-Resolution

Ultrasound localization microscopy has enabled super-resolution vascular
imaging through precise localization of individual ultrasound contrast agents
(microbubbles) across numerous imaging frames. However, analysis of
high-density regions with significant overlaps among the microbubble point
spread responses yields high localization errors, constraining the technique to
low-concentration conditions. As such, long acquisition times are required to
sufficiently cover the vascular bed. In this work, we present a fast and
precise method for obtaining super-resolution vascular images from high-density
contrast-enhanced ultrasound imaging data. This method, which we term Deep
Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning
strategies and employs a convolutional neural network to perform localization
microscopy in dense scenarios. This end-to-end fully convolutional neural
network architecture is trained effectively using on-line synthesized data,
enabling robust inference in-vivo under a wide variety of imaging conditions.
We show that deep learning attains super-resolution with challenging
contrast-agent densities, both in-silico as well as in-vivo. Deep-ULM is
suitable for real-time applications, resolving about 70 high-resolution patches
(128x128 pixels) per second on a standard PC. Exploiting GPU computation, this
number increases to 1250 patches per second.
Acquisition of Phrase Correspondences using Natural Deduction Proofs
Authors: Hitomi Yanaka, Koji Mineshima, Pascual Martinez-Gomez, Daisuke Bekki
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2018 6
Tasks: Natural Language Inference, RTE, Sentence

How to identify, extract, and use phrasal knowledge is a crucial problem for
the task of Recognizing Textual Entailment (RTE). To solve this problem, we
propose a method for detecting paraphrases via natural deduction proofs of
semantic relations between sentence pairs. Our solution relies on a graph
reformulation of partial variable unifications and an algorithm that induces
subgraph alignments between meaning representations. Experiments show that our
method can automatically detect various paraphrases that are absent from
existing paraphrase databases. In addition, the detection of paraphrases using
proof information improves the accuracy of RTE tasks.
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm
Authors: Emma Hart, Andreas S. W. Steyven, Ben Paechter
Date: 2018-04-20 00:00:00
Tasks: Diversity

The presence of functional diversity within a group has been demonstrated to
lead to greater robustness, higher performance and increased problem-solving
ability in a broad range of studies that includes insect groups, human groups
and swarm robotics. Evolving group diversity however has proved challenging
within Evolutionary Robotics, requiring reproductive isolation and careful
attention to population size and selection mechanisms. To tackle this issue, we
introduce a novel, decentralised, variant of the MAP-Elites illumination
algorithm which is hybridised with a well-known distributed evolutionary
algorithm (mEDEA). The algorithm simultaneously evolves multiple diverse
behaviours for multiple robots, with respect to a simple token-gathering task.
Each robot in the swarm maintains a local archive defined by two pre-specified
functional traits which is shared with robots it come into contact with. We
investigate four different strategies for sharing, exploiting and combining
local archives and compare results to mEDEA. Experimental results show that in
contrast to previous claims, it is possible to evolve a functionally diverse
swarm without geographical isolation, and that the new method outperforms mEDEA
in terms of the diversity, coverage and precision of the evolved swarm.
Occluded Person Re-identification
Authors: Jiaxuan Zhuo, Zeyu Chen, Jian-Huang Lai, Guangcong Wang
Date: 2018-04-09 00:00:00
Tasks: Occluded Person Re-Identification, Person Re-Identification

Person re-identification (re-id) suffers from a serious occlusion problem
when applied to crowded public places. In this paper, we propose to retrieve a
full-body person image by using a person image with occlusions. This differs
significantly from the conventional person re-id problem where it is assumed
that person images are detected without any occlusion. We thus call this new
problem the occluded person re-identitification. To address this new problem,
we propose a novel Attention Framework of Person Body (AFPB) based on deep
learning, consisting of 1) an Occlusion Simulator (OS) which automatically
generates artificial occlusions for full-body person images, and 2) multi-task
losses that force the neural network not only to discriminate a person's
identity but also to determine whether a sample is from the occluded data
distribution or the full-body data distribution. Experiments on a new occluded
person re-id dataset and three existing benchmarks modified to include
full-body person images and occluded person images show the superiority of the
proposed method.
Long-term Tracking in the Wild: A Benchmark
Authors: Jack Valmadre, Luca Bertinetto, João F. Henriques, Ran Tao, Andrea Vedaldi, Arnold Smeulders, Philip Torr, Efstratios Gavves
Date: 2018-03-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, Object Tracking

We introduce the OxUvA dataset and benchmark for evaluating single-object
tracking algorithms. Benchmarks have enabled great strides in the field of
object tracking by defining standardized evaluations on large sets of diverse
videos. However, these works have focused exclusively on sequences that are
just tens of seconds in length and in which the target is always visible.
Consequently, most researchers have designed methods tailored to this
"short-term" scenario, which is poorly representative of practitioners' needs.
Aiming to address this disparity, we compile a long-term, large-scale tracking
dataset of sequences with average length greater than two minutes and with
frequent target object disappearance. The OxUvA dataset is much larger than the
object tracking datasets of recent years: it comprises 366 sequences spanning
14 hours of video. We assess the performance of several algorithms, considering
both the ability to locate the target and to determine whether it is present or
absent. Our goal is to offer the community a large and diverse benchmark to
enable the design and evaluation of tracking methods ready to be used "in the
wild". The project website is http://oxuva.net
Revisiting Small Batch Training for Deep Neural Networks
Authors: Dominic Masters, Carlo Luschi
Date: 2018-04-20 00:00:00

Modern deep neural network training is typically based on mini-batch
stochastic gradient optimization. While the use of large mini-batches increases
the available computational parallelism, small batch training has been shown to
provide improved generalization performance and allows a significantly smaller
memory footprint, which might also be exploited to improve machine throughput.
  In this paper, we review common assumptions on learning rate scaling and
training duration, as a basis for an experimental comparison of test
performance for different mini-batch sizes. We adopt a learning rate that
corresponds to a constant average weight update per gradient calculation (i.e.,
per unit cost of computation), and point out that this results in a variance of
the weight updates that increases linearly with the mini-batch size $m$.
  The collected experimental results for the CIFAR-10, CIFAR-100 and ImageNet
datasets show that increasing the mini-batch size progressively reduces the
range of learning rates that provide stable convergence and acceptable test
performance. On the other hand, small mini-batch sizes provide more up-to-date
gradient calculations, which yields more stable and reliable training. The best
performance has been consistently obtained for mini-batch sizes between $m = 2$
and $m = 32$, which contrasts with recent work advocating the use of mini-batch
sizes in the thousands.
ClaimRank: Detecting Check-Worthy Claims in Arabic and English
Authors: Israa Jaradat, Pepa Gencheva, Alberto Barron-Cedeno, Lluis Marquez, Preslav Nakov
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Fact Checking

We present ClaimRank, an online system for detecting check-worthy claims.
While originally trained on political debates, the system can work for any kind
of text, e.g., interviews or regular news articles. Its aim is to facilitate
manual fact-checking efforts by prioritizing the claims that fact-checkers
should consider first. ClaimRank supports both Arabic and English, it is
trained on actual annotations from nine reputable fact-checking organizations
(PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and
Washington Post), and thus it can mimic the claim selection strategies for each
and any of them, as well as for the union of them all.