pwc-archive/papers-with-abstracts huggingface dataset:
zoNNscan : a boundary-entropy index for zone inspection of neural models
Authors: Adel Jaouen, Erwan Le Merrer
Date: 2018-08-21 00:00:00
Tasks: General Classification

The training of deep neural network classifiers results in decision
boundaries which geometry is still not well understood. This is in direct
relation with classification problems such as so called adversarial examples.
We introduce zoNNscan, an index that is intended to inform on the boundary
uncertainty (in terms of the presence of other classes) around one given input
datapoint. It is based on confidence entropy, and is implemented through
sampling in the multidimensional ball surrounding that input. We detail the
zoNNscan index, give an algorithm for approximating it, and finally illustrate
its benefits on four applications, including two important problems for the
adoption of deep networks in critical systems: adversarial examples and corner
case inputs. We highlight that zoNNscan exhibits significantly higher values
than for standard inputs in those two problem classes.
LRMM: Learning to Recommend with Missing Modalities
Authors: Cheng Wang, Mathias Niepert, Hui Li
Date: 2018-08-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Recommendation Systems

Multimodal learning has shown promising performance in content-based
recommendation due to the auxiliary user and item information of multiple
modalities such as text and images. However, the problem of incomplete and
missing modality is rarely explored and most existing methods fail in learning
a recommendation model with missing or corrupted modalities. In this paper, we
propose LRMM, a novel framework that mitigates not only the problem of missing
modalities but also more generally the cold-start problem of recommender
systems. We propose modality dropout (m-drop) and a multimodal sequential
autoencoder (m-auto) to learn multimodal representations for complementing and
imputing missing modalities. Extensive experiments on real-world Amazon data
show that LRMM achieves state-of-the-art performance on rating prediction
tasks. More importantly, LRMM is more robust to previous methods in alleviating
data-sparsity and the cold-start problem.
Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals
Authors: Daniel Lopez-Martinez, Ke Peng, Sarah C. Steele, Arielle J. Lee, David Borsook, Rosalind Picard
Date: 2018-08-21 00:00:00
Tasks: BIG-bench Machine Learning

Currently there is no validated objective measure of pain. Recent
neuroimaging studies have explored the feasibility of using functional
near-infrared spectroscopy (fNIRS) to measure alterations in brain function in
evoked and ongoing pain. In this study, we applied multi-task machine learning
methods to derive a practical algorithm for pain detection derived from fNIRS
signals in healthy volunteers exposed to a painful stimulus. Especially, we
employed multi-task multiple kernel learning to account for the inter-subject
variability in pain response. Our results support the use of fNIRS and machine
learning techniques in developing objective pain detection, and also highlight
the importance of adopting personalized analysis in the process.
Semi-Supervised Learning for Neural Keyphrase Generation
Authors: Hai Ye, Lu Wang
Date: 2018-08-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Keyphrase Extraction, Keyphrase Generation, Multi-Task Learning

We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large amounts of labeled data, which is only applicable to resource-rich domains. In this paper, we propose semi-supervised keyphrase generation methods by leveraging both labeled data and large-scale unlabeled samples for learning. Two strategies are proposed. First, unlabeled documents are first tagged with synthetic keyphrases obtained from unsupervised keyphrase extraction methods or a selflearning algorithm, and then combined with labeled samples for training. Furthermore, we investigate a multi-task learning framework to jointly learn to generate keyphrases as well as the titles of the articles. Experimental results show that our semi-supervised learning-based methods outperform a state-of-the-art model trained with labeled data only.
Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging
Authors: Diego Patiño, Jonathan Avendaño, John Willian Branch
Date: 2018-08-21 00:00:00
Tasks: Lesion Segmentation, Segmentation, Skin Lesion Segmentation, Superpixels

We present a superpixel-based strategy for segmenting skin lesion on
dermoscopic images. The segmentation is carried out by over-segmenting the
original image using the SLIC algorithm, and then merge the resulting
superpixels into two regions: healthy skin and lesion. The mean RGB color of
each superpixel was used as merging criterion. The presented method is capable
of dealing with segmentation problems commonly found in dermoscopic images such
as hair removal, oil bubbles, changes in illumination, and reflections images
without any additional steps. The method was evaluated on the PH2 and ISIC 2017
dataset with results comparable to the state-of-art.
Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion
Authors: Kejie Qiu, Tong Qin, Hongwen Xie, Shaojie Shen
Date: 2018-08-21 00:00:00
Tasks: 3D Object Tracking, Object, Object Tracking, Pose Estimation, State Estimation

A monocular 3D object tracking system generally has only up-to-scale pose
estimation results without any prior knowledge of the tracked object. In this
paper, we propose a novel idea to recover the metric scale of an arbitrary
dynamic object by optimizing the trajectory of the objects in the world frame,
without motion assumptions. By introducing an additional constraint in the time
domain, our monocular visual-inertial tracking system can obtain continuous six
degree of freedom (6-DoF) pose estimation without scale ambiguity. Our method
requires neither fixed multi-camera nor depth sensor settings for scale
observability, instead, the IMU inside the monocular sensing suite provides
scale information for both camera itself and the tracked object. We build the
proposed system on top of our monocular visual-inertial system (VINS) to obtain
accurate state estimation of the monocular camera in the world frame. The whole
system consists of a 2D object tracker, an object region-based visual bundle
adjustment (BA), VINS and a correlation analysis-based metric scale estimator.
Experimental comparisons with ground truth demonstrate the tracking accuracy of
our 3D tracking performance while a mobile augmented reality (AR) demo shows
the feasibility of potential applications.
Lessons from Natural Language Inference in the Clinical Domain
Authors: Alexey Romanov, Chaitanya Shivade
Date: 2018-08-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Natural Language Inference, Transfer Learning

State of the art models using deep neural networks have become very good in
learning an accurate mapping from inputs to outputs. However, they still lack
generalization capabilities in conditions that differ from the ones encountered
during training. This is even more challenging in specialized, and knowledge
intensive domains, where training data is limited. To address this gap, we
introduce MedNLI - a dataset annotated by doctors, performing a natural
language inference task (NLI), grounded in the medical history of patients. We
present strategies to: 1) leverage transfer learning using datasets from the
open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data
and lexical sources (e.g. medical terminologies). Our results demonstrate
performance gains using both strategies.
Abnormal Event Detection and Location for Dense Crowds using Repulsive Forces and Sparse Reconstruction
Authors: Pei Lv, Shunhua Liu, Mingliang Xu, Bing Zhou
Date: 2018-08-21 00:00:00
Tasks: Event Detection

This paper proposes a method based on repulsive forces and sparse
reconstruction for the detection and location of abnormal events in crowded
scenes. In order to avoid the challenging problem of accurately tracking each
specific individual in a dense or complex scene, we divide each frame of the
surveillance video into a fixed number of grids and select a single
representative point in each grid as the individual to track. The repulsive
force model, which can accurately reflect interactive behaviors of crowds, is
used to calculate the interactive forces between grid particles in crowded
scenes and to construct a force flow matrix using these discrete forces from a
fixed number of continuous frames. The force flow matrix, which contains
spatial and temporal information, is adopted to train a group of visual
dictionaries by sparse coding. To further improve the detection efficiency and
avoid concept drift, we propose a fully unsupervised global and local dynamic
updating algorithm, based on sparse reconstruction and a group of word pools.
For anomaly location, since our method is based on a fixed grid, we can judge
whether anomalies occur in a region intuitively according to the reconstruction
error of the corresponding visual words. We experimentally verify the proposed
method using the UMN dataset, the UCSD dataset and the Web dataset separately.
The results indicate that our method can not only detect abnormal events
accurately, but can also pinpoint the location of anomalies.
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
Authors: Ziyu Yao, Xiujun Li, Jianfeng Gao, Brian Sadler, Huan Sun
Date: 2018-08-21 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Parsing

Given a text description, most existing semantic parsers synthesize a program
in one shot. However, it is quite challenging to produce a correct program
solely based on the description, which in reality is often ambiguous or
incomplete. In this paper, we investigate interactive semantic parsing, where
the agent can ask the user clarification questions to resolve ambiguities via a
multi-turn dialogue, on an important type of programs called "If-Then recipes."
We develop a hierarchical reinforcement learning (HRL) based agent that
significantly improves the parsing performance with minimal questions to the
user. Results under both simulation and human evaluation show that our agent
substantially outperforms non-interactive semantic parsers and rule-based
agents.
Constrained-size Tensorflow Models for YouTube-8M Video Understanding Challenge
Authors: Tianqi Liu, Bo Liu
Date: 2018-08-21 00:00:00
Tasks: Video Understanding

This paper presents our 7th place solution to the second YouTube-8M video
understanding competition which challenges participates to build a
constrained-size model to classify millions of YouTube videos into thousands of
classes. Our final model consists of four single models aggregated into one
tensorflow graph. For each single model, we use the same network architecture
as in the winning solution of the first YouTube-8M video understanding
competition, namely Gated NetVLAD. We train the single models separately in
tensorflow's default float32 precision, then replace weights with float16
precision and ensemble them in the evaluation and inference stages., achieving
48.5% compression rate without loss of precision. Our best model achieved
88.324% GAP on private leaderboard. The code is publicly available at
https://github.com/boliu61/youtube-8m
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning
Authors: Tianyi Liu, Xinsong Zhang, Wanhao Zhou, Weijia Jia
Date: 2018-08-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Knowledge Base Completion, Relation, Relation Extraction, Relationship Extraction (Distant Supervised), Sentence, Transfer Learning

Extracting relations is critical for knowledge base completion and
construction in which distant supervised methods are widely used to extract
relational facts automatically with the existing knowledge bases. However, the
automatically constructed datasets comprise amounts of low-quality sentences
containing noisy words, which is neglected by current distant supervised
methods resulting in unacceptable precisions. To mitigate this problem, we
propose a novel word-level distant supervised approach for relation extraction.
We first build Sub-Tree Parse(STP) to remove noisy words that are irrelevant to
relations. Then we construct a neural network inputting the sub-tree while
applying the entity-wise attention to identify the important semantic features
of relational words in each instance. To make our model more robust against
noisy words, we initialize our network with a priori knowledge learned from the
relevant task of entity classification by transfer learning. We conduct
extensive experiments using the corpora of New York Times(NYT) and Freebase.
Experiments show that our approach is effective and improves the area of
Precision/Recall(PR) from 0.35 to 0.39 over the state-of-the-art work.
Wrapped Loss Function for Regularizing Nonconforming Residual Distributions
Authors: Chun Ting Liu, Ming Chuan Yang, Meng Chang Chen
Date: 2018-08-21 00:00:00
Tasks: BIG-bench Machine Learning

Multi-output is essential in machine learning that it might suffer from nonconforming residual distributions, i.e., the multi-output residual distributions are not conforming to the expected distribution. In this paper, we propose "Wrapped Loss Function" to wrap the original loss function to alleviate the problem. This wrapped loss function acts just like the original loss function that its gradient can be used for backpropagation optimization. Empirical evaluations show wrapped loss function has advanced properties of faster convergence, better accuracy, and improving imbalanced data.
You Shall Know the Most Frequent Sense by the Company it Keeps
Authors: Bradley Hauer, Yixing Luan, Grzegorz Kondrak
Date: 2018-08-21 00:00:00
Tasks: Translation

Identification of the most frequent sense of a polysemous word is an
important semantic task. We introduce two concepts that can benefit MFS
detection: companions, which are the most frequently co-occurring words, and
the most frequent translation in a bitext. We present two novel methods that
incorporate these new concepts, and show that they advance the state of the art
on MFS detection.
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks
Authors: Jeeheh Oh, Jiaxuan Wang, Jenna Wiens
Date: 2018-08-21 00:00:00
Tasks: Computational Efficiency, Dynamic Time Warping, Time Series, Time Series Analysis

Recently, researchers have started applying convolutional neural networks
(CNNs) with one-dimensional convolutions to clinical tasks involving
time-series data. This is due, in part, to their computational efficiency,
relative to recurrent neural networks and their ability to efficiently exploit
certain temporal invariances, (e.g., phase invariance). However, it is
well-established that clinical data may exhibit many other types of invariances
(e.g., scaling). While preprocessing techniques, (e.g., dynamic time warping)
may successfully transform and align inputs, their use often requires one to
identify the types of invariances in advance. In contrast, we propose the use
of Sequence Transformer Networks, an end-to-end trainable architecture that
learns to identify and account for invariances in clinical time-series data.
Applied to the task of predicting in-hospital mortality, our proposed approach
achieves an improvement in the area under the receiver operating characteristic
curve (AUROC) relative to a baseline CNN (AUROC=0.851 vs. AUROC=0.838). Our
results suggest that a variety of valuable invariances can be learned directly
from the data.
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks
Authors: Sercan O. Arik, Heewoo Jun, Gregory Diamos
Date: 2018-08-20 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Synthesis

We propose the multi-head convolutional neural network (MCNN) architecture
for waveform synthesis from spectrograms. Nonlinear interpolation in MCNN is
employed with transposed convolution layers in parallel heads. MCNN achieves
more than an order of magnitude higher compute intensity than commonly-used
iterative algorithms like Griffin-Lim, yielding efficient utilization for
modern multi-core processors, and very fast (more than 300x real-time) waveform
synthesis. For training of MCNN, we use a large-scale speech recognition
dataset and losses defined on waveforms that are related to perceptual audio
quality. We demonstrate that MCNN constitutes a very promising approach for
high-quality speech synthesis, without any iterative algorithms or
autoregression in computations.
VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification
Authors: Songle Chen, Lintao Zheng, Yan Zhang, Zhixin Sun, Kai Xu
Date: 2018-08-20 00:00:00
Tasks: 3D Shape Classification, Classification, General Classification, Object Recognition

Multi-view deep neural network is perhaps the most successful approach in 3D
shape classification. However, the fusion of multi-view features based on max
or average pooling lacks a view selection mechanism, limiting its application
in, e.g., multi-view active object recognition by a robot. This paper presents
VERAM, a recurrent attention model capable of actively selecting a sequence of
views for highly accurate 3D shape classification. VERAM addresses an important
issue commonly found in existing attention-based models, i.e., the unbalanced
training of the subnetworks corresponding to next view estimation and shape
classification. The classification subnetwork is easily overfitted while the
view estimation one is usually poorly trained, leading to a suboptimal
classification performance. This is surmounted by three essential
view-enhancement strategies: 1) enhancing the information flow of gradient
backpropagation for the view estimation subnetwork, 2) devising a highly
informative reward function for the reinforcement training of view estimation
and 3) formulating a novel loss function that explicitly circumvents view
duplication. Taking grayscale image as input and AlexNet as CNN architecture,
VERAM with 9 views achieves instance-level and class-level accuracy of 95:5%
and 95:3% on ModelNet10, 93:7% and 92:1% on ModelNet40, both are the
state-of-the-art performance under the same number of views.
Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
Authors: Dmitry Ustalov, Alexander Panchenko, Chris Biemann, Simone Paolo Ponzetto
Date: 2018-08-20 00:00:00
Proceeding: CL 2019 9
Tasks: Clustering, Graph Clustering

We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph that reflects the "ambiguity" of its nodes. Then, it uses hard clustering to discover clusters in this "disambiguated" intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can be also applied to other networks of linguistic data.
Deep Multimodal Image-Repurposing Detection
Authors: Ekraam Sabir, Wael Abd-Almageed, Yue Wu, Prem Natarajan
Date: 2018-08-20 00:00:00

Nefarious actors on social media and other platforms often spread rumors and
falsehoods through images whose metadata (e.g., captions) have been modified to
provide visual substantiation of the rumor/falsehood. This type of modification
is referred to as image repurposing, in which often an unmanipulated image is
published along with incorrect or manipulated metadata to serve the actor's
ulterior motives. We present the Multimodal Entity Image Repurposing (MEIR)
dataset, a substantially challenging dataset over that which has been
previously available to support research into image repurposing detection. The
new dataset includes location, person, and organization manipulations on
real-world data sourced from Flickr. We also present a novel, end-to-end, deep
multimodal learning model for assessing the integrity of an image by combining
information extracted from the image with related information from a knowledge
base. The proposed method is compared against state-of-the-art techniques on
existing datasets as well as MEIR, where it outperforms existing methods across
the board, with AUC improvement up to 0.23.
Use Of Vapnik-Chervonenkis Dimension in Model Selection
Authors: Merlin Mpoudeu
Date: 2018-08-20 00:00:00
Tasks: model, Model Selection

In this dissertation, I derive a new method to estimate the
Vapnik-Chervonenkis Dimension (VCD) for the class of linear functions. This
method is inspired by the technique developed by Vapnik et al. Vapnik et al.
(1994). My contribution rests on the approximation of the expected maximum
difference between two empirical Losses (EMDBTEL). In fact, I use a
cross-validated form of the error to compute the EMDBTEL, and I make the bound
on the EMDBTEL tighter by minimizing a constant in of its right upper bound. I
also derive two bounds for the true unknown risk using the additive (ERM1) and
the multiplicative (ERM2) Chernoff bounds. These bounds depend on the estimated
VCD and the empirical risk. These bounds can be used to perform model selection
and to declare with high probability, the chosen model will perform better
without making strong assumptions about the data generating process (DG).
  I measure the accuracy of my technique on simulated datasets and also on
three real datasets. The model selection provided by VCD was always as good as
if not better than the other methods under reasonable conditions.
Class2Str: End to End Latent Hierarchy Learning
Authors: Soham Saha, Girish Varma, C. V. Jawahar
Date: 2018-08-20 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep neural networks for image classification typically consists of a
convolutional feature extractor followed by a fully connected classifier
network. The predicted and the ground truth labels are represented as one hot
vectors. Such a representation assumes that all classes are equally dissimilar.
However, classes have visual similarities and often form a hierarchy. Learning
this latent hierarchy explicitly in the architecture could provide invaluable
insights. We propose an alternate architecture to the classifier network called
the Latent Hierarchy (LH) Classifier and an end to end learned Class2Str
mapping which discovers a latent hierarchy of the classes. We show that for
some of the best performing architectures on CIFAR and Imagenet datasets, the
proposed replacement and training by LH classifier recovers the accuracy, with
a fraction of the number of parameters in the classifier part. Compared to the
previous work of HDCNN, which also learns a 2 level hierarchy, we are able to
learn a hierarchy at an arbitrary number of levels as well as obtain an
accuracy improvement on the Imagenet classification task over them. We also
verify that many visually similar classes are grouped together, under the
learnt hierarchy.