pwc-archive/papers-with-abstracts huggingface dataset:
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture
Authors: S. Bazrafkan, H. Javidnia, J. Lemley, P. Corcoran
Date: 2017-03-10 00:00:00
Tasks: Depth Estimation, Stereo Depth Estimation, Stereo Matching, Stereo Matching Hand

Deep neural networks are applied to a wide range of problems in recent years.
In this work, Convolutional Neural Network (CNN) is applied to the problem of
determining the depth from a single camera image (monocular depth). Eight
different networks are designed to perform depth estimation, each of them
suitable for a feature level. Networks with different pooling sizes determine
different feature levels. After designing a set of networks, these models may
be combined into a single network topology using graph optimization techniques.
This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common
network layers, and can be further optimized by retraining to achieve an
improved model compared to the individual topologies. In this study, four SPDNN
models are trained and have been evaluated at 2 stages on the KITTI dataset.
The ground truth images in the first part of the experiment are provided by the
benchmark, and for the second part, the ground truth images are the depth map
results from applying a state-of-the-art stereo matching method. The results of
this evaluation demonstrate that using post-processing techniques to refine the
target of the network increases the accuracy of depth estimation on individual
mono images. The second evaluation shows that using segmentation data alongside
the original data as the input can improve the depth estimation results to a
point where performance is comparable with stereo depth estimation. The
computational time is also discussed in this study.
Exact Distributed Training: Random Forest with Billions of Examples
Authors: Mathieu Guillame-Bert, Olivier Teytaud
Date: 2018-04-18 00:00:00

We introduce an exact distributed algorithm to train Random Forest models as
well as other decision forest models without relying on approximating best
split search. We explain the proposed algorithm and compare it to related
approaches for various complexity measures (time, ram, disk, and network
complexity analysis). We report its running performances on artificial and
real-world datasets of up to 18 billions examples. This figure is several
orders of magnitude larger than datasets tackled in the existing literature.
Finally, we empirically show that Random Forest benefits from being trained on
more data, even in the case of already gigantic datasets. Given a dataset with
17.3B examples with 82 features (3 numerical, other categorical with high
arity), our implementation trains a tree in 22h.
Zero-Shot Deep Domain Adaptation
Authors: Kuan-Chuan Peng, Ziyan Wu, Jan Ernst
Date: 2017-07-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Classification, Domain Adaptation, General Classification, Scene Classification, Sensor Fusion

Domain adaptation is an important tool to transfer knowledge about a task
(e.g. classification) learned in a source domain to a second, or target domain.
Current approaches assume that task-relevant target-domain data is available
during training. We demonstrate how to perform domain adaptation when no such
task-relevant target-domain data is available. To tackle this issue, we propose
zero-shot deep domain adaptation (ZDDA), which uses privileged information from
task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation
which is not only tailored for the task of interest but also close to the
target-domain representation. Therefore, the source-domain task of interest
solution (e.g. a classifier for classification tasks) which is jointly trained
with the source-domain representation can be applicable to both the source and
target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN
RGB-D datasets, we show that ZDDA can perform domain adaptation in
classification tasks without access to task-relevant target-domain training
data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene
classification task by simulating task-relevant target-domain representations
with task-relevant source-domain data. To the best of our knowledge, ZDDA is
the first domain adaptation and sensor fusion method which requires no
task-relevant target-domain data. The underlying principle is not particular to
computer vision data, but should be extensible to other domains.
A Language for Function Signature Representations
Authors: Kyle Richardson
Date: 2018-03-31 00:00:00
Tasks: Natural Language Understanding, Question Answering, Translation

Recent work by (Richardson and Kuhn, 2017a,b; Richardson et al., 2018) looks
at semantic parser induction and question answering in the domain of source
code libraries and APIs. In this brief note, we formalize the representations
being learned in these studies and introduce a simple domain specific language
and a systematic translation from this language to first-order logic. By
recasting the target representations in terms of classical logic, we aim to
broaden the applicability of existing code datasets for investigating more
complex natural language understanding and reasoning problems in the software
domain.
Alquist: The Alexa Prize Socialbot
Authors: Jan Pichl, Petr Marek, Jakub Konrád, Martin Matulík, Hoang Long Nguyen, Jan Šedivý
Date: 2018-04-18 00:00:00
Tasks: BIG-bench Machine Learning

This paper describes a new open domain dialogue system Alquist developed as
part of the Alexa Prize competition for the Amazon Echo line of products. The
Alquist dialogue system is designed to conduct a coherent and engaging
conversation on popular topics. We are presenting a hybrid system combining
several machine learning and rule based approaches. We discuss and describe the
Alquist pipeline, data acquisition, and processing, dialogue manager, NLG,
knowledge aggregation and hierarchy of sub-dialogs. We present some of the
experimental results.
Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing
Authors: Sepehr Eghbali, Ladan Tahvildari
Date: 2016-09-14 00:00:00

Given a large dataset of binary codes and a binary query point, we address
how to efficiently find $K$ codes in the dataset that yield the largest cosine
similarities to the query. The straightforward answer to this problem is to
compare the query with all items in the dataset, but this is practical only for
small datasets. One potential solution to enhance the search time and achieve
sublinear cost is to use a hash table populated with binary codes of the
dataset and then look up the nearby buckets to the query to retrieve the
nearest neighbors. However, if codes are compared in terms of cosine similarity
rather than the Hamming distance, then the main issue is that the order of
buckets to probe is not evident. To examine this issue, we first elaborate on
the connection between the Hamming distance and the cosine similarity. Doing
this allows us to systematically find the probing sequence in the hash table.
However, solving the nearest neighbor search with a single table is only
practical for short binary codes. To address this issue, we propose the angular
multi-index hashing search algorithm which relies on building multiple hash
tables on binary code substrings. The proposed search algorithm solves the
exact angular $K$ nearest neighbor problem in a time that is often orders of
magnitude faster than the linear scan baseline and even approximation methods.
Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos
Authors: Santhosh Kelathodi Kumaran, Debi Prosad Dogra, Partha Pratim Roy
Date: 2018-04-18 00:00:00
Tasks: Clustering, Optical Flow Estimation

Optimized scene representation is an important characteristic of a framework
for detecting abnormalities on live videos. One of the challenges for detecting
abnormalities in live videos is real-time detection of objects in a
non-parametric way. Another challenge is to efficiently represent the state of
objects temporally across frames. In this paper, a Gibbs sampling based
heuristic model referred to as Temporal Unknown Incremental Clustering (TUIC)
has been proposed to cluster pixels with motion. Pixel motion is first detected
using optical flow and a Bayesian algorithm has been applied to associate
pixels belonging to similar cluster in subsequent frames. The algorithm is fast
and produces accurate results in $\Theta(kn)$ time, where $k$ is the number of
clusters and $n$ the number of pixels. Our experimental validation with
publicly available datasets reveals that the proposed framework has good
potential to open-up new opportunities for real-time traffic analysis.
Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation
Authors: Rana Ali Amjad, Kairen Liu, Bernhard C. Geiger
Date: 2018-04-18 00:00:00
Tasks: Classification, General Classification

In this work, we investigate the use of three information-theoretic quantities -- entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler divergence -- to understand and study the behavior of already trained fully-connected feed-forward neural networks. We analyze the connection between these information-theoretic quantities and classification performance on the test set by cumulatively ablating neurons in networks trained on MNIST, FashionMNIST, and CIFAR-10. Our results parallel those recently published by Morcos et al., indicating that class selectivity is not a good indicator for classification performance. However, looking at individual layers separately, both mutual information and class selectivity are positively correlated with classification performance, at least for networks with ReLU activation functions. We provide explanations for this phenomenon and conclude that it is ill-advised to compare the proposed information-theoretic quantities across layers. Furthermore, we show that cumulative ablation of neurons with ascending or descending information-theoretic quantities can be used to formulate hypotheses regarding the joint behavior of multiple neurons, such as redundancy and synergy, with comparably low computational cost. We also draw connections to the information bottleneck theory for neural networks.
Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule
Authors: Youhei Akimoto, Anne Auger, Tobias Glasmachers
Date: 2018-02-09 00:00:00

This paper explores the use of the standard approach for proving runtime
bounds in discrete domains---often referred to as drift analysis---in the
context of optimization on a continuous domain. Using this framework we analyze
the (1+1) Evolution Strategy with one-fifth success rule on the sphere
function. To deal with potential functions that are not lower-bounded, we
formulate novel drift theorems. We then use the theorems to prove bounds on the
expected hitting time to reach a certain target fitness in finite dimension
$d$. The bounds are akin to linear convergence. We then study the dependency of
the different terms on $d$ proving a convergence rate dependency of
$\Theta(1/d)$. Our results constitute the first non-asymptotic analysis for the
algorithm considered as well as the first explicit application of drift
analysis to a randomized search heuristic with continuous domain.
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Authors: Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki
Date: 2018-04-18 00:00:00

Metabolic flux balance analyses are a standard tool in analysing metabolic
reaction rates compatible with measurements, steady-state and the metabolic
reaction network stoichiometry. Flux analysis methods commonly place
unrealistic assumptions on fluxes due to the convenience of formulating the
problem as a linear programming model, and most methods ignore the notable
uncertainty in flux estimates. We introduce a novel paradigm of Bayesian
metabolic flux analysis that models the reactions of the whole genome-scale
cellular system in probabilistic terms, and can infer the full flux vector
distribution of genome-scale metabolic systems based on exchange and
intracellular (e.g. 13C) flux measurements, steady-state assumptions, and
target function assumptions. The Bayesian model couples all fluxes jointly
together in a simple truncated multivariate posterior distribution, which
reveals informative flux couplings. Our model is a plug-in replacement to
conventional metabolic balance methods, such as flux balance analysis (FBA).
Our experiments indicate that we can characterise the genome-scale flux
covariances, reveal flux couplings, and determine more intracellular unobserved
fluxes in C. acetobutylicum from 13C data than flux variability analysis. The
COBRA compatible software is available at github.com/markusheinonen/bamfa
Active Learning for Breast Cancer Identification
Authors: Xinpeng Xie, Yuexiang Li, Linlin Shen
Date: 2018-04-18 00:00:00
Tasks: Active Learning

Breast cancer is the second most common malignancy among women and has become
a major public health problem in current society. Traditional breast cancer
identification requires experienced pathologists to carefully read the breast
slice, which is laborious and suffers from inter-observer variations.
Consequently, an automatic classification framework for breast cancer
identification is worthwhile to develop. Recent years witnessed the development
of deep learning technique. Increasing number of medical applications start to
use deep learning to improve diagnosis accuracy. In this paper, we proposed a
novel training strategy, namely reversed active learning (RAL), to train
network to automatically classify breast cancer images. Our RAL is applied to
the training set of a simple convolutional neural network (CNN) to remove
mislabeled images. We evaluate the CNN trained with RAL on publicly available
ICIAR 2018 Breast Cancer Dataset (IBCD). The experimental results show that our
RAL increases the slice-based accuracy of CNN from 93.75% to 96.25%.
Short Term Electric Load Forecast with Artificial Neural Networks
Authors: Cristian Vasar, Iosif Szeidert, Ioan Filip, Gabriela Prostean
Date: 2018-04-18 00:00:00
Tasks: Load Forecasting, Management

This paper presents issues regarding short term electric load forecasting
using feedforward and Elman recurrent neural networks. The study cases were
developed using measured data representing electrical energy consume from Banat
area. There were considered 35 different types of structure for both
feedforward and recurrent network cases. For each type of neural network
structure were performed many trainings and best solution was selected. The
issue of forecasting the load on short term is essential in the effective
energetic consume management in an open market environment.
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
Authors: Christos Baziotis, Nikos Athanasiou, Pinelopi Papalampidi, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Alexandros Potamianos
Date: 2018-04-18 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Word Embeddings

In this paper we present two deep-learning systems that competed at
SemEval-2018 Task 3 "Irony detection in English tweets". We design and ensemble
two independent models, based on recurrent neural networks (Bi-LSTM), which
operate at the word and character level, in order to capture both the semantic
and syntactic information in tweets. Our models are augmented with a
self-attention mechanism, in order to identify the most informative words. The
embedding layer of our word-level model is initialized with word2vec word
embeddings, pretrained on a collection of 550 million English tweets. We did
not utilize any handcrafted features, lexicons or external datasets as prior
information and our models are trained end-to-end using back propagation on
constrained data. Furthermore, we provide visualizations of tweets with
annotations for the salient tokens of the attention layer that can help to
interpret the inner workings of the proposed models. We ranked 2nd out of 42
teams in Subtask A and 2nd out of 31 teams in Subtask B. However,
post-task-completion enhancements of our models achieve state-of-the-art
results ranking 1st for both subtasks.
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
Authors: Christos Baziotis, Nikos Athanasiou, Alexandra Chronopoulou, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Shrikanth Narayanan, Alexandros Potamianos
Date: 2018-04-18 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, Transfer Learning, Word Embeddings

In this paper we present deep-learning models that submitted to the
SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all
subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a
multi-layer self attention mechanism. The attention mechanism improves the
model performance and allows us to identify salient words in tweets, as well as
gain insight into the models making them more interpretable. Our model utilizes
a set of word2vec word embeddings trained on a large collection of 550 million
Twitter messages, augmented by a set of word affective features. Due to the
limited amount of task-specific training data, we opted for a transfer learning
approach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A.
The proposed approach ranked 1st in Subtask E "Multi-Label Emotion
Classification", 2nd in Subtask A "Emotion Intensity Regression" and achieved
competitive results in other subtasks.
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
Authors: Christos Baziotis, Nikos Athanasiou, Georgios Paraskevopoulos, Nikolaos Ellinas, Athanasia Kolovou, Alexandros Potamianos
Date: 2018-04-18 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Task 2, Word Embeddings

In this paper we present a deep-learning model that competed at SemEval-2018
Task 2 "Multilingual Emoji Prediction". We participated in subtask A, in which
we are called to predict the most likely associated emoji in English tweets.
The proposed architecture relies on a Long Short-Term Memory network, augmented
with an attention mechanism, that conditions the weight of each word, on a
"context vector" which is taken as the aggregation of a tweet's meaning.
Moreover, we initialize the embedding layer of our model, with word2vec word
embeddings, pretrained on a dataset of 550 million English tweets. Finally, our
model does not rely on hand-crafted features or lexicons and is trained
end-to-end with back-propagation. We ranked 2nd out of 48 teams.
Modular Verification of Vehicle Platooning with Respect to Decisions, Space and Time
Authors: Maryam Kamali, Sven Linker, Michael Fisher
Date: 2018-04-18 00:00:00
Tasks: Decision Making

The spread of autonomous systems into safety-critical areas has increased the
demand for their formal verification, not only due to stronger certification
requirements but also to public uncertainty over these new technologies.
However, the complex nature of such systems, for example, the intricate
combination of discrete and continuous aspects, ensures that whole system
verification is often infeasible. This motivates the need for novel analysis
approaches that modularise the problem, allowing us to restrict our analysis to
one particular aspect of the system while abstracting away from others. For
instance, while verifying the real-time properties of an autonomous system we
might hide the details of the internal decision-making components. In this
paper we describe verification of a range of properties across distinct
dimesnions on a practical hybrid agent architecture. This allows us to verify
the autonomous decision-making, real-time aspects, and spatial aspects of an
autonomous vehicle platooning system. This modular approach also illustrates
how both algorithmic and deductive verification techniques can be applied for
the analysis of different system subcomponents.
Superframes, A Temporal Video Segmentation
Authors: Hajar Sadeghi Sokeh, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino
Date: 2018-04-18 00:00:00
Tasks: Clustering, Motion Estimation, Optical Flow Estimation, Segmentation, Video Segmentation, Video Semantic Segmentation

The goal of video segmentation is to turn video data into a set of concrete
motion clusters that can be easily interpreted as building blocks of the video.
There are some works on similar topics like detecting scene cuts in a video,
but there is few specific research on clustering video data into the desired
number of compact segments. It would be more intuitive, and more efficient, to
work with perceptually meaningful entity obtained from a low-level grouping
process which we call it superframe. This paper presents a new simple and
efficient technique to detect superframes of similar content patterns in
videos. We calculate the similarity of content-motion to obtain the strength of
change between consecutive frames. With the help of existing optical flow
technique using deep models, the proposed method is able to perform more
accurate motion estimation efficiently. We also propose two criteria for
measuring and comparing the performance of different algorithms on various
databases. Experimental results on the videos from benchmark databases have
demonstrated the effectiveness of the proposed method.
Experiments with Universal CEFR Classification
Authors: Sowmya Vajjala, Taraka Rama
Date: 2018-04-18 00:00:00
Proceeding: WS 2018 6
Tasks: Classification, General Classification

The Common European Framework of Reference (CEFR) guidelines describe
language proficiency of learners on a scale of 6 levels. While the description
of CEFR guidelines is generic across languages, the development of automated
proficiency classification systems for different languages follow different
approaches. In this paper, we explore universal CEFR classification using
domain-specific and domain-agnostic, theory-guided as well as data-driven
features. We report the results of our preliminary experiments in monolingual,
cross-lingual, and multilingual classification with three languages: German,
Czech, and Italian. Our results show that both monolingual and multilingual
models achieve similar performance, and cross-lingual classification yields
lower, but comparable results to monolingual classification.
Variational Disparity Estimation Framework for Plenoptic Image
Authors: Trung-Hieu Tran, Zhe Wang, Sven Simon
Date: 2018-04-18 00:00:00
Tasks: Disparity Estimation

This paper presents a computational framework for accurately estimating the
disparity map of plenoptic images. The proposed framework is based on the
variational principle and provides intrinsic sub-pixel precision. The
light-field motion tensor introduced in the framework allows us to combine
advanced robust data terms as well as provides explicit treatments for
different color channels. A warping strategy is embedded in our framework for
tackling the large displacement problem. We also show that by applying a simple
regularization term and a guided median filtering, the accuracy of displacement
field at occluded area could be greatly enhanced. We demonstrate the excellent
performance of the proposed framework by intensive comparisons with the Lytro
software and contemporary approaches on both synthetic and real-world datasets.