pwc-archive/papers-with-abstracts huggingface dataset:
Stingray Detection of Aerial Images Using Augmented Training Images Generated by A Conditional Generative Model
Authors: Yi-Min Chou, Chien-Hung Chen, Keng-Hao Liu, Chu-Song Chen
Date: 2018-05-11 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification, Object, object-detection, Object Detection

In this paper, we present an object detection method that tackles the
stingray detection problem based on aerial images. In this problem, the images
are aerially captured on a sea-surface area by using an Unmanned Aerial Vehicle
(UAV), and the stingrays swimming under (but close to) the sea surface are the
target we want to detect and locate. To this end, we use a deep object
detection method, faster RCNN, to train a stingray detector based on a limited
training set of images. To boost the performance, we develop a new generative
approach, conditional GLO, to increase the training samples of stingray, which
is an extension of the Generative Latent Optimization (GLO) approach. Unlike
traditional data augmentation methods that generate new data only for image
classification, our proposed method that mixes foreground and background
together can generate new data for an object detection task, and thus improve
the training efficacy of a CNN detector. Experimental results show that
satisfiable performance can be obtained by using our approach on stingray
detection in aerial images.
Convex Class Model on Symmetric Positive Definite Manifolds
Authors: Kun Zhao, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
Date: 2018-06-14 00:00:00
Tasks: Classification, General Classification, model, Object Recognition, Person Re-Identification, Scene Classification, Texture Classification

The effectiveness of Symmetric Positive Definite (SPD) manifold features has been proven in various computer vision tasks. However, due to the non-Euclidean geometry of these features, existing Euclidean machineries cannot be directly used. In this paper, we tackle the classification tasks with limited training data on SPD manifolds. Our proposed framework, named Manifold Convex Class Model, represents each class on SPD manifolds using a convex model, and classification can be performed by computing distances to the convex models. We provide three methods based on different metrics to address the optimization problem of the smallest distance of a point to the convex model on SPD manifold. The efficacy of our proposed framework is demonstrated both on synthetic data and several computer vision tasks including object recognition, texture classification, person re-identification and traffic scene classification.
Context-Aware Policy Reuse
Authors: Siyuan Li, Fangda Gu, Guangxiang Zhu, Chongjie Zhang
Date: 2018-06-11 00:00:00
Tasks: Reinforcement Learning, Transfer Learning

Transfer learning can greatly speed up reinforcement learning for a new task
by leveraging policies of relevant tasks.
  Existing works of policy reuse either focus on only selecting a single best
source policy for transfer without considering contexts, or cannot guarantee to
learn an optimal policy for a target task.
  To improve transfer efficiency and guarantee optimality, we develop a novel
policy reuse method, called Context-Aware Policy reuSe (CAPS), that enables
multi-policy transfer. Our method learns when and which source policy is best
for reuse, as well as when to terminate its reuse. CAPS provides theoretical
guarantees in convergence and optimality for both source policy selection and
target task learning. Empirical results on a grid-based navigation domain and
the Pygame Learning Environment demonstrate that CAPS significantly outperforms
other state-of-the-art policy reuse methods.
From Trailers to Storylines: An Efficient Way to Learn from Movies
Authors: Qingqiu Huang, Yuanjun Xiong, Yu Xiong, Yuqi Zhang, Dahua Lin
Date: 2018-06-14 00:00:00

The millions of movies produced in the human history are valuable resources
for computer vision research. However, learning a vision model from movie data
would meet with serious difficulties. A major obstacle is the computational
cost -- the length of a movie is often over one hour, which is substantially
longer than the short video clips that previous study mostly focuses on. In
this paper, we explore an alternative approach to learning vision models from
movies. Specifically, we consider a framework comprised of a visual module and
a temporal analysis module. Unlike conventional learning methods, the proposed
approach learns these modules from different sets of data -- the former from
trailers while the latter from movies. This allows distinctive visual features
to be learned within a reasonable budget while still preserving long-term
temporal structures across an entire movie. We construct a large-scale dataset
for this study and define a series of tasks on top. Experiments on this dataset
showed that the proposed method can substantially reduce the training time
while obtaining highly effective features and coherent temporal structures.
Hierarchical interpretations for neural network predictions
Authors: Chandan Singh, W. James Murdoch, Bin Yu
Date: 2018-06-14 00:00:00
Proceeding: ICLR 2019 5
Tasks: Clustering, Feature Importance, Interpretable Machine Learning

Deep neural networks (DNNs) have achieved impressive predictive performance
due to their ability to learn complex, non-linear relationships between
variables. However, the inability to effectively visualize these relationships
has led to DNNs being characterized as black boxes and consequently limited
their applications. To ameliorate this problem, we introduce the use of
hierarchical interpretations to explain DNN predictions through our proposed
method, agglomerative contextual decomposition (ACD). Given a prediction from a
trained DNN, ACD produces a hierarchical clustering of the input features,
along with the contribution of each cluster to the final prediction. This
hierarchy is optimized to identify clusters of features that the DNN learned
are predictive. Using examples from Stanford Sentiment Treebank and ImageNet,
we show that ACD is effective at diagnosing incorrect predictions and
identifying dataset bias. Through human experiments, we demonstrate that ACD
enables users both to identify the more accurate of two DNNs and to better
trust a DNN's outputs. We also find that ACD's hierarchy is largely robust to
adversarial perturbations, implying that it captures fundamental aspects of the
input and ignores spurious noise.
Adversarial Learning with Local Coordinate Coding
Authors: Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan
Date: 2018-06-13 00:00:00
Proceeding: ICML 2018 7

Generative adversarial networks (GANs) aim to generate realistic data from
some prior distribution (e.g., Gaussian noises). However, such prior
distribution is often independent of real data and thus may lose semantic
information (e.g., geometric structure or content in images) of data. In
practice, the semantic information might be represented by some latent
distribution learned from data, which, however, is hard to be used for sampling
in GANs. In this paper, rather than sampling from the pre-defined prior
distribution, we propose a Local Coordinate Coding (LCC) based sampling method
to improve GANs. We derive a generalization bound for LCC based GANs and prove
that a small dimensional input is sufficient to achieve good generalization.
Extensive experiments on various real-world datasets demonstrate the
effectiveness of the proposed method.
Talakat: Bullet Hell Generation through Constrained Map-Elites
Authors: Ahmed Khalifa, Scott Lee, Andy Nealen, Julian Togelius
Date: 2018-06-12 00:00:00

We describe a search-based approach to generating new levels for bullet hell
games, which are action games characterized by and requiring avoidance of a
very large amount of projectiles. Levels are represented using a
domain-specific description language, and search in the space defined by this
language is performed by a novel variant of the Map-Elites algorithm which
incorporates a feasible- infeasible approach to constraint satisfaction.
Simulation-based evaluation is used to gauge the fitness of levels, using an
agent based on best-first search. The performance of the agent can be tuned
according to the two dimensions of strategy and dexterity, making it possible
to search for level configurations that require a specific combination of both.
As far as we know, this paper describes the first generator for this game
genre, and includes several algorithmic innovations.
A Generative Modeling Approach to Limited Channel ECG Classification
Authors: Deepta Rajan, Jayaraman J. Thiagarajan
Date: 2018-02-18 00:00:00
Tasks: Classification, Diagnostic, Disease Prediction, ECG Classification, General Classification, Temporal Sequences, Time Series, Time Series Analysis

Processing temporal sequences is central to a variety of applications in
health care, and in particular multi-channel Electrocardiogram (ECG) is a
highly prevalent diagnostic modality that relies on robust sequence modeling.
While Recurrent Neural Networks (RNNs) have led to significant advances in
automated diagnosis with time-series data, they perform poorly when models are
trained using a limited set of channels. A crucial limitation of existing
solutions is that they rely solely on discriminative models, which tend to
generalize poorly in such scenarios. In order to combat this limitation, we
develop a generative modeling approach to limited channel ECG classification.
This approach first uses a Seq2Seq model to implicitly generate the missing
channel information, and then uses the latent representation to perform the
actual supervisory task. This decoupling enables the use of unsupervised data
and also provides highly robust metric spaces for subsequent discriminative
learning. Our experiments with the Physionet dataset clearly evidence the
effectiveness of our approach over standard RNNs in disease prediction.
SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners
Authors: Huiyuan Zhuo, Xuelin Qian, Yanwei Fu, Heng Yang, xiangyang xue
Date: 2018-06-14 00:00:00
Tasks: Clustering, Model Compression

Deep Convolutional Neural Networks (CNN) has achieved significant success in
computer vision field. However, the high computational cost of the deep complex
models prevents the deployment on edge devices with limited memory and
computational resource. In this paper, we proposed a novel filter pruning for
convolutional neural networks compression, namely spectral clustering filter
pruning with soft self-adaption manners (SCSP). We first apply spectral
clustering on filters layer by layer to explore their intrinsic connections and
only count on efficient groups. By self-adaption manners, the pruning
operations can be done in few epochs to let the network gradually choose
meaningful groups. According to this strategy, we not only achieve model
compression while keeping considerable performance, but also find a novel angle
to interpret the model compression process.
Interpretable Partitioned Embedding for Customized Fashion Outfit Composition
Authors: Zunlei Feng, Zhenyun Yu, Yezhou Yang, Yongcheng Jing, Junxiao Jiang, Mingli Song
Date: 2018-06-13 00:00:00
Tasks: Attribute

Intelligent fashion outfit composition becomes more and more popular in these
years. Some deep learning based approaches reveal competitive composition
recently. However, the unexplainable characteristic makes such deep learning
based approach cannot meet the the designer, businesses and consumers' urge to
comprehend the importance of different attributes in an outfit composition. To
realize interpretable and customized fashion outfit compositions, we propose a
partitioned embedding network to learn interpretable representations from
clothing items. The overall network architecture consists of three components:
an auto-encoder module, a supervised attributes module and a multi-independent
module. The auto-encoder module serves to encode all useful information into
the embedding. In the supervised attributes module, multiple attributes labels
are adopted to ensure that different parts of the overall embedding correspond
to different attributes. In the multi-independent module, adversarial operation
are adopted to fulfill the mutually independent constraint. With the
interpretable and partitioned embedding, we then construct an outfit
composition graph and an attribute matching map. Given specified attributes
description, our model can recommend a ranked list of outfit composition with
interpretable matching scores. Extensive experiments demonstrate that 1) the
partitioned embedding have unmingled parts which corresponding to different
attributes and 2) outfits recommended by our model are more desirable in
comparison with the existing methods.
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Authors: Lin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi
Date: 2018-02-22 00:00:00
Proceeding: ICML 2018 7

Online optimization has been a successful framework for solving large-scale
problems under computational constraints and partial information. Current
methods for online convex optimization require either a projection or exact
gradient computation at each step, both of which can be prohibitively expensive
for large-scale applications. At the same time, there is a growing trend of
non-convex optimization in machine learning community and a need for online
methods. Continuous DR-submodular functions, which exhibit a natural
diminishing returns condition, have recently been proposed as a broad class of
non-convex functions which may be efficiently optimized. Although online
methods have been introduced, they suffer from similar problems. In this work,
we propose Meta-Frank-Wolfe, the first online projection-free algorithm that
uses stochastic gradient estimates. The algorithm relies on a careful sampling
of gradients in each round and achieves the optimal $O( \sqrt{T})$ adversarial
regret bounds for convex and continuous submodular optimization. We also
propose One-Shot Frank-Wolfe, a simpler algorithm which requires only a single
stochastic gradient estimate in each round and achieves an $O(T^{2/3})$
stochastic regret bound for convex and continuous submodular optimization. We
apply our methods to develop a novel "lifting" framework for the online
discrete submodular maximization and also see that they outperform current
state-of-the-art techniques on various experiments.
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages
Authors: Shiyu Zhou, Shuang Xu, Bo Xu
Date: 2018-06-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Sentence, speech-recognition, Speech Recognition

Sequence-to-sequence attention-based models integrate an acoustic,
pronunciation and language model into a single neural network, which make them
very suitable for multilingual automatic speech recognition (ASR). In this
paper, we are concerned with multilingual speech recognition on low-resource
languages by a single Transformer, one of sequence-to-sequence attention-based
models. Sub-words are employed as the multilingual modeling unit without using
any pronunciation lexicon. First, we show that a single multilingual ASR
Transformer performs well on low-resource languages despite of some language
confusion. We then look at incorporating language information into the model by
inserting the language symbol at the beginning or at the end of the original
sub-words sequence under the condition of language information being known
during training. Experiments on CALLHOME datasets demonstrate that the
multilingual ASR Transformer with the language symbol at the end performs
better and can obtain relatively 10.5\% average word error rate (WER) reduction
compared to SHL-MLSTM with residual learning. We go on to show that, assuming
the language information being known during training and testing, about
relatively 12.4\% average WER reduction can be observed compared to SHL-MLSTM
with residual learning through giving the language symbol as the sentence start
token.
Deep Reinforcement Learning for Dynamic Urban Transportation Problems
Authors: Laura Schultz, Vadim Sokolov
Date: 2018-06-14 00:00:00
Tasks: Deep Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

We explore the use of deep learning and deep reinforcement learning for
optimization problems in transportation. Many transportation system analysis
tasks are formulated as an optimization problem - such as optimal control
problems in intelligent transportation systems and long term urban planning.
Often transportation models used to represent dynamics of a transportation
system involve large data sets with complex input-output interactions and are
difficult to use in the context of optimization. Use of deep learning
metamodels can produce a lower dimensional representation of those relations
and allow to implement optimization and reinforcement learning algorithms in an
efficient manner. In particular, we develop deep learning models for
calibrating transportation simulators and for reinforcement learning to solve
the problem of optimal scheduling of travelers on the network.
Stress Test Evaluation for Natural Language Inference
Authors: Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, Graham Neubig
Date: 2018-06-02 00:00:00
Proceeding: COLING 2018 8
Tasks: Natural Language Inference, Natural Language Understanding, Sentence

Natural language inference (NLI) is the task of determining if a natural
language hypothesis can be inferred from a given premise in a justifiable
manner. NLI was proposed as a benchmark task for natural language
understanding. Existing models perform well at standard datasets for NLI,
achieving impressive results across different genres of text. However, the
extent to which these models understand the semantic content of sentences is
unclear. In this work, we propose an evaluation methodology consisting of
automatically constructed "stress tests" that allow us to examine whether
systems have the ability to make real inferential decisions. Our evaluation of
six sentence-encoder models on these stress tests reveals strengths and
weaknesses of these models with respect to challenging linguistic phenomena,
and suggests important directions for future work in this area.
Geometric Shape Features Extraction Using a Steady State Partial Differential Equation System
Authors: Takayuki Yamada
Date: 2018-06-13 00:00:00

A unified method for extracting geometric shape features from binary image
data using a steady state partial differential equation (PDE) system as a
boundary value problem is presented in this paper. The PDE and functions are
formulated to extract the thickness, orientation, and skeleton simultaneously.
The main advantages of the proposed method is that the orientation is defined
without derivatives and thickness computation is not imposed a topological
constraint on the target shape. A one-dimensional analytical solution is
provided to validate the proposed method. In addition, two-dimensional
numerical examples are presented to confirm the usefulness of the proposed
method.
Apuntes de Redes Neuronales Artificiales
Authors: J. C. Cuevas-Tello
Date: 2018-06-13 00:00:00

These handouts are designed for people who is just starting involved with the
topic artificial neural networks. We show how it works a single artificial
neuron (McCulloch & Pitt model), mathematically and graphically. We do explain
the delta rule, a learning algorithm to find the neuron weights. We also
present some examples in MATLAB/Octave. There are examples for classification
task for lineal and non-lineal problems. At the end, we present an artificial
neural network, a feed-forward neural network along its learning algorithm
backpropagation.
  -----
  Estos apuntes est\'an dise\~nados para personas que por primera vez se
introducen en el tema de las redes neuronales artificiales. Se muestra el
funcionamiento b\'asico de una neurona, matem\'aticamente y gr\'aficamente. Se
explica la Regla Delta, algoritmo deaprendizaje para encontrar los pesos de una
neurona. Tambi\'en se muestran ejemplos en MATLAB/Octave. Hay ejemplos para
problemas de clasificaci\'on, para problemas lineales y no-lineales. En la
parte final se muestra la arquitectura de red neuronal artificial conocida como
backpropagation.
Pattern Dependence Detection using n-TARP Clustering
Authors: Tarun Yellamraju, Mireille Boutin
Date: 2018-06-13 00:00:00
Tasks: Clustering, valid

Consider an experiment involving a potentially small number of subjects. Some
random variables are observed on each subject: a high-dimensional one called
the "observed" random variable, and a one-dimensional one called the "outcome"
random variable. We are interested in the dependencies between the observed
random variable and the outcome random variable. We propose a method to
quantify and validate the dependencies of the outcome random variable on the
various patterns contained in the observed random variable. Different degrees
of relationship are explored (linear, quadratic, cubic, ...). This work is
motivated by the need to analyze educational data, which often involves
high-dimensional data representing a small number of students. Thus our
implementation is designed for a small number of subjects; however, it can be
easily modified to handle a very large dataset. As an illustration, the
proposed method is used to study the influence of certain skills on the course
grade of students in a signal processing class. A valid dependency of the grade
on the different skill patterns is observed in the data.
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
Authors: Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
Date: 2018-06-12 00:00:00
Tasks: Domain Adaptation, Sentiment Analysis, Sentiment Classification

Domain adaptation for sentiment analysis is challenging due to the fact that
supervised classifiers are very sensitive to changes in domain. The two most
prominent approaches to this problem are structural correspondence learning and
autoencoders. However, they either require long training times or suffer
greatly on highly divergent domains. Inspired by recent advances in
cross-lingual sentiment analysis, we provide a novel perspective and cast the
domain adaptation problem as an embedding projection task. Our model takes as
input two mono-domain embedding spaces and learns to project them to a
bi-domain space, which is jointly optimized to (1) project across domains and
to (2) predict sentiment. We perform domain adaptation experiments on 20
source-target domain pairs for sentiment classification and report novel
state-of-the-art results on 11 domain pairs, including the Amazon domain
adaptation datasets and SemEval 2013 and 2016 datasets. Our analysis shows that
our model performs comparably to state-of-the-art approaches on domains that
are similar, while performing significantly better on highly divergent domains.
Our code is available at https://github.com/jbarnesspain/domain_blse