pwc-archive/papers-with-abstracts huggingface dataset:
Competitive Training of Mixtures of Independent Deep Generative Models
Authors: Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
Date: 2018-04-30 00:00:00
Tasks: Clustering

A common assumption in causal modeling posits that the data is generated by a
set of independent mechanisms, and algorithms should aim to recover this
structure. Standard unsupervised learning, however, is often concerned with
training a single model to capture the overall distribution or aspects thereof.
Inspired by clustering approaches, we consider mixtures of implicit generative
models that ``disentangle'' the independent generative mechanisms underlying
the data. Relying on an additional set of discriminators, we propose a
competitive training procedure in which the models only need to capture the
portion of the data distribution from which they can produce realistic samples.
As a by-product, each model is simpler and faster to train. We empirically show
that our approach splits the training distribution in a sensible way and
increases the quality of the generated samples.
An English-Hindi Code-Mixed Corpus: Stance Annotation and Baseline System
Authors: Sahil Swami, Ankush Khandelwal, Vinay Singh, Syed Sarfaraz Akhtar, Manish Shrivastava
Date: 2018-05-30 00:00:00
Tasks: General Classification, Stance Detection

Social media has become one of the main channels for peo- ple to communicate
and share their views with the society. We can often detect from these views
whether the person is in favor, against or neu- tral towards a given topic.
These opinions from social media are very useful for various companies. We
present a new dataset that consists of 3545 English-Hindi code-mixed tweets
with opinion towards Demoneti- sation that was implemented in India in 2016
which was followed by a large countrywide debate. We present a baseline
supervised classification system for stance detection developed using the same
dataset that uses various machine learning techniques to achieve an accuracy of
58.7% on 10-fold cross validation.
Using Inter-Sentence Diverse Beam Search to Reduce Redundancy in Visual Storytelling
Authors: Chao-Chun Hsu, Szu-Min Chen, Ming-Hsun Hsieh, Lun-Wei Ku
Date: 2018-05-30 00:00:00
Tasks: Image to text, Sentence, Visual Storytelling

Visual storytelling includes two important parts: coherence between the story
and images as well as the story structure. For image to text neural network
models, similar images in the sequence would provide close information for
story generator to obtain almost identical sentence. However, repeatedly
narrating same objects or events will undermine a good story structure. In this
paper, we proposed an inter-sentence diverse beam search to generate a more
expressive story. Comparing to some recent models of visual storytelling task,
which generate story without considering the generated sentence of the previous
picture, our proposed method can avoid generating identical sentence even given
a sequence of similar pictures.
Foresee: Attentive Future Projections of Chaotic Road Environments with Online Training
Authors: Anil Sharma, Prabhat Kumar
Date: 2018-05-30 00:00:00
Tasks: Autonomous Driving

In this paper, we train a recurrent neural network to learn dynamics of a
chaotic road environment and to project the future of the environment on an
image. Future projection can be used to anticipate an unseen environment for
example, in autonomous driving. Road environment is highly dynamic and complex
due to the interaction among traffic participants such as vehicles and
pedestrians. Even in this complex environment, a human driver is efficacious to
safely drive on chaotic roads irrespective of the number of traffic
participants. The proliferation of deep learning research has shown the
efficacy of neural networks in learning this human behavior. In the same
direction, we investigate recurrent neural networks to understand the chaotic
road environment which is shared by pedestrians, vehicles (cars, trucks,
bicycles etc.), and sometimes animals as well. We propose \emph{Foresee}, a
unidirectional gated recurrent units (GRUs) network with attention to project
future of the environment in the form of images. We have collected several
videos on Delhi roads consisting of various traffic participants, background
and infrastructure differences (like 3D pedestrian crossing) at various times
on various days. We train \emph{Foresee} in an unsupervised way and we use
online training to project frames up to $0.5$ seconds in advance. We show that
our proposed model performs better than state of the art methods (prednet and
Enc. Dec. LSTM) and finally, we show that our trained model generalizes to a
public dataset for future projections.
RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection
Authors: Tian Lan, Yuanyuan Li, Jonah Kimani Murugi, Yi Ding, Zhiguang Qin
Date: 2018-05-30 00:00:00

The early detection and early diagnosis of lung cancer are crucial to improve
the survival rate of lung cancer patients. Pulmonary nodules detection results
have a significant impact on the later diagnosis. In this work, we propose a
new network named RUN to complete nodule detection in a single step by
bypassing the candidate selection. The system introduces the shortcut of the
residual network to improve the traditional U-Net, thereby solving the
disadvantage of poor results due to its lack of depth. Furthermore, we compare
the experimental results with the traditional U-Net. We validate our method in
LUng Nodule Analysis 2016 (LUNA16) Nodule Detection Challenge. We acquire a
sensitivity of 90.90% at 2 false positives per scan and therefore achieve
better performance than the current state-of-the-art approaches.
Labelling as an unsupervised learning problem
Authors: Terry Lyons, Imanol Perez Arribas
Date: 2018-05-10 00:00:00

Unravelling hidden patterns in datasets is a classical problem with many
potential applications. In this paper, we present a challenge whose objective
is to discover nonlinear relationships in noisy cloud of points. If a set of
point satisfies a nonlinear relationship that is unlikely to be due to
randomness, we will label the set with this relationship. Since points can
satisfy one, many or no such nonlinear relationships, cloud of points will
typically have one, multiple or no labels at all. This introduces the labelling
problem that will be studied in this paper.
  The objective of this paper is to develop a framework for the labelling
problem. We introduce a precise notion of a label, and we propose an algorithm
to discover such labels in a given dataset, which is then tested in synthetic
datasets. We also analyse, using tools from random matrix theory, the problem
of discovering false labels in the dataset.
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio
Authors: Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Li Chen, Michael E. Kounavis, Duen Horng Chau
Date: 2018-05-30 00:00:00
Tasks: Adversarial Attack, Audio Compression, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Adversarial machine learning research has recently demonstrated the
feasibility to confuse automatic speech recognition (ASR) models by introducing
acoustically imperceptible perturbations to audio samples. To help researchers
and practitioners gain better understanding of the impact of such attacks, and
to provide them with tools to help them more easily evaluate and craft strong
defenses for their models, we present ADAGIO, the first tool designed to allow
interactive experimentation with adversarial attacks and defenses on an ASR
model in real time, both visually and aurally. ADAGIO incorporates AMR and MP3
audio compression techniques as defenses, which users can interactively apply
to attacked audio samples. We show that these techniques, which are based on
psychoacoustic principles, effectively eliminate targeted attacks, reducing the
attack success rate from 92.5% to 0%. We will demonstrate ADAGIO and invite the
audience to try it on the Mozilla Common Voice dataset.
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting
Authors: Jacopo Acquarelli, Elena Marchiori, Lutgarde M. C. Buydens, Thanh Tran, Twan van Laarhoven
Date: 2017-11-15 00:00:00
Tasks: Data Augmentation, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Spectral-spatial classification of hyperspectral images has been the subject
of many studies in recent years. In the presence of only very few labeled
pixels, this task becomes challenging. In this paper we address the following
two research questions: 1) Can a simple neural network with just a single
hidden layer achieve state of the art performance in the presence of few
labeled pixels? 2) How is the performance of hyperspectral image classification
methods affected when using disjoint train and test sets? We give a positive
answer to the first question by using three tricks within a very basic shallow
Convolutional Neural Network (CNN) architecture: a tailored loss function, and
smooth- and label-based data augmentation. The tailored loss function enforces
that neighborhood wavelengths have similar contributions to the features
generated during training. A new label-based technique here proposed favors
selection of pixels in smaller classes, which is beneficial in the presence of
very few labeled pixels and skewed class distributions. To address the second
question, we introduce a new sampling procedure to generate disjoint train and
test set. Then the train set is used to obtain the CNN model, which is then
applied to pixels in the test set to estimate their labels. We assess the
efficacy of the simple neural network method on five publicly available
hyperspectral images. On these images our method significantly outperforms
considered baselines. Notably, with just 1% of labeled pixels per class, on
these datasets our method achieves an accuracy that goes from 86.42%
(challenging dataset) to 99.52% (easy dataset). Furthermore we show that the
simple neural network method improves over other baselines in the new
challenging supervised setting. Our analysis substantiates the highly
beneficial effect of using the entire image (so train and test data) for
constructing a model.
Neural Joking Machine : Humorous image captioning
Authors: Kota Yoshida, Munetaka Minoguchi, Kenichiro Wani, Akio Nakamura, Hirokatsu Kataoka
Date: 2018-05-30 00:00:00
Tasks: Image Captioning

What is an effective expression that draws laughter from human beings? In the
present paper, in order to consider this question from an academic standpoint,
we generate an image caption that draws a "laugh" by a computer. A system that
outputs funny captions based on the image caption proposed in the computer
vision field is constructed. Moreover, we also propose the Funny Score, which
flexibly gives weights according to an evaluation database. The Funny Score
more effectively brings out "laughter" to optimize a model. In addition, we
build a self-collected BoketeDB, which contains a theme (image) and funny
caption (text) posted on "Bokete", which is an image Ogiri website. In an
experiment, we use BoketeDB to verify the effectiveness of the proposed method
by comparing the results obtained using the proposed method and those obtained
using MS COCO Pre-trained CNN+LSTM, which is the baseline and idiot created by
humans. We refer to the proposed method, which uses the BoketeDB pre-trained
model, as the Neural Joking Machine (NJM).
Learning Instance-Aware Object Detection Using Determinantal Point Processes
Authors: Nuri Kim, Donghoon Lee, Songhwai Oh
Date: 2018-05-28 00:00:00
Tasks: Object, object-detection, Object Detection, Point Processes

Recent object detectors find instances while categorizing candidate regions. As each region is evaluated independently, the number of candidate regions from a detector is usually larger than the number of objects. Since the final goal of detection is to assign a single detection to each object, a heuristic algorithm, such as non-maximum suppression (NMS), is used to select a single bounding box for an object. While simple heuristic algorithms are effective for stand-alone objects, they can fail to detect overlapped objects. In this paper, we address this issue by training a network to distinguish different objects using the relationship between candidate boxes. We propose an instance-aware detection network (IDNet), which can learn to extract features from candidate regions and measure their similarities. Based on pairwise similarities and detection qualities, the IDNet selects a subset of candidate bounding boxes using instance-aware determinantal point process inference (IDPP). Extensive experiments demonstrate that the proposed algorithm achieves significant improvements for detecting overlapped objects compared to existing state-of-the-art detection methods on the PASCAL VOC and MS COCO datasets.
Robot Localisation and 3D Position Estimation Using a Free-Moving Camera and Cascaded Convolutional Neural Networks
Authors: Justinas Miseikis, Patrick Knobelreiter, Inka Brijacak, Saeed Yahyanejad, Kyrre Glette, Ole Jakob Elle, Jim Torresen
Date: 2018-01-06 00:00:00
Tasks: Position

Many works in collaborative robotics and human-robot interaction focuses on
identifying and predicting human behaviour while considering the information
about the robot itself as given. This can be the case when sensors and the
robot are calibrated in relation to each other and often the reconfiguration of
the system is not possible, or extra manual work is required. We present a deep
learning based approach to remove the constraint of having the need for the
robot and the vision sensor to be fixed and calibrated in relation to each
other. The system learns the visual cues of the robot body and is able to
localise it, as well as estimate the position of robot joints in 3D space by
just using a 2D color image. The method uses a cascaded convolutional neural
network, and we present the structure of the network, describe our own
collected dataset, explain the network training and achieved results. A fully
trained system shows promising results in providing an accurate mask of where
the robot is located and a good estimate of its joints positions in 3D. The
accuracy is not good enough for visual servoing applications yet, however, it
can be sufficient for general safety and some collaborative tasks not requiring
very high precision. The main benefit of our method is the possibility of the
vision sensor to move freely. This allows it to be mounted on moving objects,
for example, a body of the person or a mobile robot working in the same
environment as the robots are operating in.
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels
Authors: Vadim Ratner, Yoel Shoshan, Tal Kachman
Date: 2018-05-30 00:00:00
Tasks: General Classification, image-classification, Image Classification, Medical Image Classification

Medical image classification involves thresholding of labels that represent
malignancy risk levels. Usually, a task defines a single threshold, and when
developing computer-aided diagnosis tools, a single network is trained per such
threshold, e.g. as screening out healthy (very low risk) patients to leave
possibly sick ones for further analysis (low threshold), or trying to find
malignant cases among those marked as non-risk by the radiologist ("second
reading", high threshold). We propose a way to rephrase the classification
problem in a manner that yields several problems (corresponding to different
thresholds) to be solved simultaneously. This allows the use of Multiple Task
Learning (MTL) methods, significantly improving the performance of the original
classifier, by facilitating effective extraction of information from existing
data.
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Authors: Tianyi Chen, Georgios B. Giannakis, Tao Sun, Wotao Yin
Date: 2018-05-25 00:00:00
Proceeding: NeurIPS 2018 12

This paper presents a new class of gradient methods for distributed machine
learning that adaptively skip the gradient calculations to learn with reduced
communication and computation. Simple rules are designed to detect
slowly-varying gradients and, therefore, trigger the reuse of outdated
gradients. The resultant gradient-based algorithms are termed Lazily Aggregated
Gradient --- justifying our acronym LAG used henceforth. Theoretically, the
merits of this contribution are: i) the convergence rate is the same as batch
gradient descent in strongly-convex, convex, and nonconvex smooth cases; and,
ii) if the distributed datasets are heterogeneous (quantified by certain
measurable constants), the communication rounds needed to achieve a targeted
accuracy are reduced thanks to the adaptive reuse of lagged gradients.
Numerical experiments on both synthetic and real data corroborate a significant
communication reduction compared to alternatives.
Anaphora and Coreference Resolution: A Review
Authors: Rhea Sukthanker, Soujanya Poria, Erik Cambria, Ramkumar Thirunavukarasu
Date: 2018-05-30 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Entity Resolution, Machine Translation, Sentiment Analysis, Translation

Entity resolution aims at resolving repeated references to an entity in a
document and forms a core component of natural language processing (NLP)
research. This field possesses immense potential to improve the performance of
other NLP fields like machine translation, sentiment analysis, paraphrase
detection, summarization, etc. The area of entity resolution in NLP has seen
proliferation of research in two separate sub-areas namely: anaphora resolution
and coreference resolution. Through this review article, we aim at clarifying
the scope of these two tasks in entity resolution. We also carry out a detailed
analysis of the datasets, evaluation metrics and research methods that have
been adopted to tackle this NLP problem. This survey is motivated with the aim
of providing the reader with a clear understanding of what constitutes this NLP
problem and the issues that require attention.
Generic CP-Supported CMSA for Binary Integer Linear Programs
Authors: Christian Blum, Haroldo Gambini Santos
Date: 2018-05-30 00:00:00
Tasks: Combinatorial Optimization

Construct, Merge, Solve and Adapt (CMSA) is a general hybrid metaheuristic
for solving combinatorial optimization problems. At each iteration, CMSA (1)
constructs feasible solutions to the tackled problem instance in a
probabilistic way and (2) solves a reduced problem instance (if possible) to
optimality. The construction of feasible solutions is hereby problem-specific,
usually involving a fast greedy heuristic. The goal of this paper is to design
a problem-agnostic CMSA variant whose exclusive input is an integer linear
program (ILP). In order to reduce the complexity of this task, the current
study is restricted to binary ILPs. In addition to a basic problem-agnostic
CMSA variant, we also present an extended version that makes use of a
constraint propagation engine for constructing solutions. The results show that
our technique is able to match the upper bounds of the standalone application
of CPLEX in the context of rather easy-to-solve instances, while it generally
outperforms the standalone application of CPLEX in the context of hard
instances. Moreover, the results indicate that the support of the constraint
propagation engine is useful in the context of problems for which finding
feasible solutions is rather difficult.
Visual Referring Expression Recognition: What Do Systems Actually Learn?
Authors: Volkan Cirik, Louis-Philippe Morency, Taylor Berg-Kirkpatrick
Date: 2018-05-30 00:00:00
Proceeding: NAACL 2018 6
Tasks: Referring Expression

We present an empirical analysis of the state-of-the-art systems for
referring expression recognition -- the task of identifying the object in an
image referred to by a natural language expression -- with the goal of gaining
insight into how these systems reason about language and vision. Surprisingly,
we find strong evidence that even sophisticated and linguistically-motivated
models for this task may ignore the linguistic structure, instead relying on
shallow correlations introduced by unintended biases in the data selection and
annotation process. For example, we show that a system trained and tested on
the input image $\textit{without the input referring expression}$ can achieve a
precision of 71.2% in top-2 predictions. Furthermore, a system that predicts
only the object category given the input can achieve a precision of 84.2% in
top-2 predictions. These surprisingly positive results for what should be
deficient prediction scenarios suggest that careful analysis of what our models
are learning -- and further, how our data is constructed -- is critical as we
seek to make substantive progress on grounded language tasks.
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash
Authors: Puneet Kohli, Anjali Chadha
Date: 2018-05-30 00:00:00
Tasks: Image Enhancement, Object Recognition

Human lives are important. The decision to allow self-driving vehicles
operate on our roads carries great weight. This has been a hot topic of debate
between policy-makers, technologists and public safety institutions. The recent
Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has
strengthened the argument that autonomous vehicle technology is still not ready
for deployment on public roads. In this work, we analyze the Uber car crash and
shed light on the question, "Could the Uber Car Crash have been avoided?". We
apply state-of-the-art Computer Vision models to this highly practical
scenario. More generally, our experimental results are an evaluation of various
image enhancement and object recognition techniques for enabling pedestrian
safety in low-lighting conditions using the Uber crash as a case study.
A Progressive Batching L-BFGS Method for Machine Learning
Authors: Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang
Date: 2018-02-15 00:00:00
Proceeding: ICML 2018 7
Tasks: BIG-bench Machine Learning

The standard L-BFGS method relies on gradient approximations that are not
dominated by noise, so that search directions are descent directions, the line
search is reliable, and quasi-Newton updating yields useful quadratic models of
the objective function. All of this appears to call for a full batch approach,
but since small batch sizes give rise to faster algorithms with better
generalization properties, L-BFGS is currently not considered an algorithm of
choice for large-scale machine learning applications. One need not, however,
choose between the two extremes represented by the full batch or highly
stochastic regimes, and may instead follow a progressive batching approach in
which the sample size increases during the course of the optimization. In this
paper, we present a new version of the L-BFGS algorithm that combines three
basic components - progressive batching, a stochastic line search, and stable
quasi-Newton updating - and that performs well on training logistic regression
and deep neural networks. We provide supporting convergence theory for the
method.