pwc-archive/papers-with-abstracts huggingface dataset:
Classification and clustering for observations of event time data using non-homogeneous Poisson process models
Authors: Duncan Barrack, Simon Preston
Date: 2017-03-06 00:00:00
Tasks: Clustering, General Classification

Data of the form of event times arise in various applications. A simple model
for such data is a non-homogeneous Poisson process (NHPP) which is specified by
a rate function that depends on time. We consider the problem of having access
to multiple independent observations of event time data, observed on a common
interval, from which we wish to classify or cluster the observations according
to their rate functions. Each rate function is unknown but assumed to belong to
a finite number of rate functions each defining a distinct class. We model the
rate functions using a spline basis expansion, the coefficients of which need
to be estimated from data. The classification approach consists of using
training data for which the class membership is known, to calculate maximum
likelihood estimates of the coefficients for each group, then assigning test
observations to a group by a maximum likelihood criterion. For clustering, by
analogy to the Gaussian mixture model approach for Euclidean data, we consider
mixtures of NHPP and use the expectation-maximisation algorithm to estimate the
coefficients of the rate functions for the component models and group
membership probabilities for each observation. The classification and
clustering approaches perform well on both synthetic and real-world data sets.
Code associated with this paper is available at
https://github.com/duncan-barrack/NHPP .
Keyframe-based monocular SLAM: design, survey, and future directions
Authors: Georges Younes, Daniel Asmar, Elie Shammas, John Zelek
Date: 2016-07-02 00:00:00
Tasks: Survey

Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery.
Trading the Twitter Sentiment with Reinforcement Learning
Authors: Catherine Xiao, Wanfeng Chen
Date: 2018-01-07 00:00:00
Tasks: BIG-bench Machine Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper is to explore the possibility to use alternative data and
artificial intelligence techniques to trade stocks. The efficacy of the daily
Twitter sentiment on predicting the stock return is examined using machine
learning methods. Reinforcement learning(Q-learning) is applied to generate the
optimal trading policy based on the sentiment signal. The predicting power of
the sentiment signal is more significant if the stock price is driven by the
expectation of the company growth and when the company has a major event that
draws the public attention. The optimal trading strategy based on reinforcement
learning outperforms the trading strategy based on the machine learning
prediction.
Data-driven Advice for Applying Machine Learning to Bioinformatics Problems
Authors: Randal S. Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore
Date: 2017-08-08 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Model Selection

As the bioinformatics field grows, it must keep pace not only with new data
but with new algorithms. Here we contribute a thorough analysis of 13
state-of-the-art, commonly used machine learning algorithms on a set of 165
publicly available classification problems in order to provide data-driven
algorithm recommendations to current researchers. We present a number of
statistical and visual comparisons of algorithm performance and quantify the
effect of model selection and algorithm tuning for each algorithm and dataset.
The analysis culminates in the recommendation of five algorithms with
hyperparameters that maximize classifier performance across the tested
problems, as well as general guidelines for applying machine learning to
supervised classification problems.
Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture
Authors: Qiangeng Xu, John Kender
Date: 2017-12-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In the research of the impact of gestures using by a lecturer, one
challenging task is to infer the attention of a group of audiences. Two
important measurements that can help infer the level of attention are eye
movement data and Electroencephalography (EEG) data. Under the fundamental
assumption that a group of people would look at the same place if they all pay
attention at the same time, we apply a method, "Time Warp Edit Distance", to
calculate the similarity of their eye movement trajectories. Moreover, we also
cluster eye movement pattern of audiences based on these pair-wised similarity
metrics. Besides, since we don't have a direct metric for the "attention"
ground truth, a visual assessment would be beneficial to evaluate the
gesture-attention relationship. Thus we also implement a visualization tool.
Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding
Authors: Long Ang Lim, Hacer Yalim Keles
Date: 2018-01-07 00:00:00
Tasks: Change Detection, Decoder, Foreground Segmentation, Triplet

A common approach for moving objects segmentation in a scene is to perform a
background subtraction. Several methods have been proposed in this domain.
However, they lack the ability of handling various difficult scenarios such as
illumination changes, background or camera motion, camouflage effect, shadow
etc. To address these issues, we propose a robust and flexible encoder-decoder
type neural network based approach. We adapt a pre-trained convolutional
network, i.e. VGG-16 Net, under a triplet framework in the encoder part to
embed an image in multiple scales into the feature space and use a transposed
convolutional network in the decoder part to learn a mapping from feature space
to image space. We train this network end-to-end by using only a few training
samples. Our network takes an RGB image in three different scales and produces
a foreground segmentation probability mask for the corresponding image. In
order to evaluate our model, we entered the Change Detection 2014 Challenge
(changedetection.net) and our method outperformed all the existing
state-of-the-art methods by an average F-Measure of 0.9770. Our source code
will be made publicly available at https://github.com/lim-anggun/FgSegNet.
Indian Regional Movie Dataset for Recommender Systems
Authors: Prerna Agarwal, Richa Verma, Angshul Majumdar
Date: 2018-01-07 00:00:00
Tasks: Collaborative Filtering, compressed sensing, Diversity, Matrix Completion, Recommendation Systems

Indian regional movie dataset is the first database of regional Indian
movies, users and their ratings. It consists of movies belonging to 18
different Indian regional languages and metadata of users with varying
demographics. Through this dataset, the diversity of Indian regional cinema and
its huge viewership is captured. We analyze the dataset that contains roughly
10K ratings of 919 users and 2,851 movies using some supervised and
unsupervised collaborative filtering techniques like Probabilistic Matrix
Factorization, Matrix Completion, Blind Compressed Sensing etc. The dataset
consists of metadata information of users like age, occupation, home state and
known languages. It also consists of metadata of movies like genre, language,
release year and cast. India has a wide base of viewers which is evident by the
large number of movies released every year and the huge box-office revenue.
This dataset can be used for designing recommendation systems for Indian users
and regional movies, which do not, yet, exist. The dataset can be downloaded
from \href{https://goo.gl/EmTPv6}{https://goo.gl/EmTPv6}.
Cross-modal Embeddings for Video and Audio Retrieval
Authors: Didac Surís, Amanda Duarte, Amaia Salvador, Jordi Torres, Xavier Giró-i-Nieto
Date: 2018-01-07 00:00:00
Tasks: Retrieval

The increasing amount of online videos brings several opportunities for
training self-supervised neural networks. The creation of large scale datasets
of videos such as the YouTube-8M allows us to deal with this large amount of
data in manageable way. In this work, we find new ways of exploiting this
dataset by taking advantage of the multi-modal information it provides. By
means of a neural network, we are able to create links between audio and visual
documents, by projecting them into a common region of the feature space,
obtaining joint audio-visual embeddings. These links are used to retrieve audio
samples that fit well to a given silent video, and also to retrieve images that
match a given a query audio. The results in terms of Recall@K obtained over a
subset of YouTube-8M videos show the potential of this unsupervised approach
for cross-modal feature learning. We train embeddings for both scales and
assess their quality in a retrieval problem, formulated as using the feature
extracted from one modality to retrieve the most similar videos based on the
features computed in the other modality.
Group Communication Analysis: A Computational Linguistics Approach for Detecting Sociocognitive Roles in Multi-Party Interactions
Authors: Nia Dowell, Tristian Nixon, Arthur Graesser
Date: 2018-01-07 00:00:00

Roles are one of the most important concepts in understanding human
sociocognitive behavior. During group interactions, members take on different
roles within the discussion. Roles have distinct patterns of behavioral
engagement (i.e., active or passive, leading or following), contribution
characteristics (i.e., providing new information or echoing given material),
and social orientation (i.e., individual or group). Different combinations of
these roles can produce characteristically different group outcomes, being
either less or more productive towards collective goals. In online
collaborative learning environments, this can lead to better or worse learning
outcomes for the individual participants. In this study, we propose and
validate a novel approach for detecting emergent roles from the participants'
contributions and patterns of interaction. Specifically, we developed a group
communication analysis (GCA) by combining automated computational linguistic
techniques with analyses of the sequential interactions of online group
communication. The GCA was applied to three large collaborative interaction
datasets (participant N = 2,429; group N = 3,598). Cluster analyses and linear
mixed-effects modeling were used to assess the validity of the GCA approach and
the influence of learner roles on student and group performance. The results
indicate that participants' patterns in linguistic coordination and cohesion
are representative of the roles that individuals play in collaborative
discussions. More broadly, GCA provides a framework for researchers to explore
the micro intra- and interpersonal patterns associated with the participants'
roles and the sociocognitive processes related to successful collaboration.
Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report
Authors: Michal Šustr, Jan Malý, Michal Čertický
Date: 2018-01-07 00:00:00
Tasks: Real-Time Strategy Games, Starcraft

We present a dockerized version of a real-time strategy game StarCraft: Brood
War, commonly used as a domain for AI research, with a pre-installed collection
of AI developement tools supporting all the major types of StarCraft bots. This
provides a convenient way to deploy StarCraft AIs on numerous hosts at once and
across multiple platforms despite limited OS support of StarCraft. In this
technical report, we describe the design of our Docker images and present a few
use cases.
Objective Bayesian Analysis for Change Point Problems
Authors: Laurentiu Hinoveanu, Fabrizio Leisen, Cristiano Villa
Date: 2017-02-17 00:00:00
Tasks: Model Selection

In this paper we present a loss-based approach to change point analysis. In
particular, we look at the problem from two perspectives. The first focuses on
the definition of a prior when the number of change points is known a priori.
The second contribution aims to estimate the number of change points by using a
loss-based approach recently introduced in the literature. The latter considers
change point estimation as a model selection exercise. We show the performance
of the proposed approach on simulated data and real data sets.
Approximate FPGA-based LSTMs under Computation Time Constraints
Authors: Michalis Rizakis, Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis
Date: 2018-01-07 00:00:00
Tasks: Autonomous Vehicles, Image Captioning, Low-rank compression

Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM)
networks have demonstrated state-of-the-art accuracy in several emerging
Artificial Intelligence tasks. However, the models are becoming increasingly
demanding in terms of computational and memory load. Emerging latency-sensitive
applications including mobile robots and autonomous vehicles often operate
under stringent computation time constraints. In this paper, we address the
challenge of deploying computationally demanding LSTMs at a constrained time
budget by introducing an approximate computing scheme that combines iterative
low-rank compression and pruning, along with a novel FPGA-based LSTM
architecture. Combined in an end-to-end framework, the approximation method's
parameters are optimised and the architecture is configured to address the
problem of high-performance LSTM execution in time-constrained applications.
Quantitative evaluation on a real-life image captioning application indicates
that the proposed methods required up to 6.5x less time to achieve the same
application-level accuracy compared to a baseline method, while achieving an
average of 25x higher accuracy under the same computation time constraints.
Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning
Authors: Alexandre Attia, Sharone Dayan
Date: 2018-01-07 00:00:00
Tasks: Segmentation

Manual segmentation of the Left Ventricle (LV) is a tedious and meticulous
task that can vary depending on the patient, the Magnetic Resonance Images
(MRI) cuts and the experts. Still today, we consider manual delineation done by
experts as being the ground truth for cardiac diagnosticians. Thus, we are
reviewing the paper - written by Avendi and al. - who presents a combined
approach with Convolutional Neural Networks, Stacked Auto-Encoders and
Deformable Models, to try and automate the segmentation while performing more
accurately. Furthermore, we have implemented parts of the paper (around three
quarts) and experimented both the original method and slightly modified
versions when changing the architecture and the parameters.
Weakly Supervised Semantic Segmentation using Web-Crawled Videos
Authors: Seunghoon Hong, Donghun Yeo, Suha Kwak, Honglak Lee, Bohyung Han
Date: 2017-01-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: image-classification, Image Classification, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

We propose a novel algorithm for weakly supervised semantic segmentation
based on image-level class labels only. In weakly supervised setting, it is
commonly observed that trained model overly focuses on discriminative parts
rather than the entire object area. Our goal is to overcome this limitation
with no additional human intervention by retrieving videos relevant to target
class labels from web repository, and generating segmentation labels from the
retrieved videos to simulate strong supervision for semantic segmentation.
During this process, we take advantage of image classification with
discriminative localization technique to reject false alarms in retrieved
videos and identify relevant spatio-temporal volumes within retrieved videos.
Although the entire procedure does not require any additional supervision, the
segmentation annotations obtained from videos are sufficiently strong to learn
a model for semantic segmentation. The proposed algorithm substantially
outperforms existing methods based on the same level of supervision and is even
as competitive as the approaches relying on extra annotations.
High-throughput, high-resolution registration-free generated adversarial network microscopy
Authors: Hao Zhang, Xinlin Xie, Chunyu Fang, Yicong Yang, Di Jin, Peng Fei
Date: 2018-01-07 00:00:00
Tasks: Generative Adversarial Network, Image Registration, Image Super-Resolution, Super-Resolution, Vocal Bursts Intensity Prediction

We combine generative adversarial network (GAN) with light microscopy to
achieve deep learning super-resolution under a large field of view (FOV). By
appropriately adopting prior microscopy data in an adversarial training, the
neural network can recover a high-resolution, accurate image of new specimen
from its single low-resolution measurement. Its capacity has been broadly
demonstrated via imaging various types of samples, such as USAF resolution
target, human pathological slides, fluorescence-labelled fibroblast cells, and
deep tissues in transgenic mouse brain, by both wide-field and light-sheet
microscopes. The gigapixel, multi-color reconstruction of these samples
verifies a successful GAN-based single image super-resolution procedure. We
also propose an image degrading model to generate low resolution images for
training, making our approach free from the complex image registration during
training dataset preparation. After a welltrained network being created, this
deep learning-based imaging approach is capable of recovering a large FOV (~95
mm2), high-resolution (~1.7 {\mu}m) image at high speed (within 1 second),
while not necessarily introducing any changes to the setup of existing
microscopes.
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition
Authors: Yonatan Tariku Tesfaye
Date: 2018-01-07 00:00:00
Tasks: Clustering, geo-localization, Outlier Detection

Recently, several clustering algorithms have been used to solve variety of
problems from different discipline. This dissertation aims to address different
challenging tasks in computer vision and pattern recognition by casting the
problems as a clustering problem. We proposed novel approaches to solve
multi-target tracking, visual geo-localization and outlier detection problems
using a unified underlining clustering framework, i.e., dominant set clustering
and its extensions, and presented a superior result over several
state-of-the-art approaches.
Flexible Prior Distributions for Deep Generative Models
Authors: Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1

We consider the problem of training generative models with deep neural
networks as generators, i.e. to map latent codes to data points. Whereas the
dominant paradigm combines simple priors over codes with complex deterministic
models, we argue that it might be advantageous to use more flexible code
distributions. We demonstrate how these distributions can be induced directly
from the data. The benefits include: more powerful generative models, better
modeling of latent structure and explicit control of the degree of
generalization.
Interactive 3D Modeling with a Generative Adversarial Network
Authors: Jerry Liu, Fisher Yu, Thomas Funkhouser
Date: 2017-06-16 00:00:00
Tasks: Generative Adversarial Network, Minecraft

This paper proposes the idea of using a generative adversarial network (GAN)
to assist a novice user in designing real-world shapes with a simple interface.
The user edits a voxel grid with a painting interface (like Minecraft). Yet, at
any time, he/she can execute a SNAP command, which projects the current voxel
grid onto a latent shape manifold with a learned projection operator and then
generates a similar, but more realistic, shape using a learned generator
network. Then the user can edit the resulting shape and snap again until he/she
is satisfied with the result. The main advantage of this approach is that the
projection and generation operators assist novice users to create 3D models
characteristic of a background distribution of object shapes, but without
having to specify all the details. The core new research idea is to use a GAN
to support this application. 3D GANs have previously been used for shape
generation, interpolation, and completion, but never for interactive modeling.
The new challenge for this application is to learn a projection operator that
takes an arbitrary 3D voxel model and produces a latent vector on the shape
manifold from which a similar and realistic shape can be generated. We develop
algorithms for this and other steps of the SNAP processing pipeline and
integrate them into a simple modeling tool. Experiments with these algorithms
and tool suggest that GANs provide a promising approach to computer-assisted
interactive modeling.
Applications of Deep Learning and Reinforcement Learning to Biological Data
Authors: Mufti Mahmud, M. Shamim Kaiser, Amir Hussain, Stefano Vassanelli
Date: 2017-11-10 00:00:00
Tasks: Deep Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Rapid advances of hardware-based technologies during the past decades have
opened up new possibilities for Life scientists to gather multimodal data in
various application domains (e.g., Omics, Bioimaging, Medical Imaging, and
[Brain/Body]-Machine Interfaces), thus generating novel opportunities for
development of dedicated data intensive machine learning techniques. Overall,
recent research in Deep learning (DL), Reinforcement learning (RL), and their
combination (Deep RL) promise to revolutionize Artificial Intelligence. The
growth in computational power accompanied by faster and increased data storage
and declining computing costs have already allowed scientists in various fields
to apply these techniques on datasets that were previously intractable for
their size and complexity. This review article provides a comprehensive survey
on the application of DL, RL, and Deep RL techniques in mining Biological data.
In addition, we compare performances of DL techniques when applied to different
datasets across various application domains. Finally, we outline open issues in
this challenging research area and discuss future development perspectives.