pwc-archive/papers-with-abstracts huggingface dataset:
Never look back - A modified EnKF method and its application to the training of neural networks without back propagation
Authors: Eldad Haber, Felix Lucka, Lars Ruthotto
Date: 2018-05-21 00:00:00
Tasks: global-optimization

In this work, we present a new derivative-free optimization method and
investigate its use for training neural networks. Our method is motivated by
the Ensemble Kalman Filter (EnKF), which has been used successfully for solving
optimization problems that involve large-scale, highly nonlinear dynamical
systems. A key benefit of the EnKF method is that it requires only the
evaluation of the forward propagation but not its derivatives. Hence, in the
context of neural networks, it alleviates the need for back propagation and
reduces the memory consumption dramatically. However, the method is not a pure
"black-box" global optimization heuristic as it efficiently utilizes the
structure of typical learning problems. Promising first results of the EnKF for
training deep neural networks have been presented recently by Kovachki and
Stuart. We propose an important modification of the EnKF that enables us to
prove convergence of our method to the minimizer of a strongly convex function.
Our method also bears similarity with implicit filtering and we demonstrate its
potential for minimizing highly oscillatory functions using a simple example.
Further, we provide numerical examples that demonstrate the potential of our
method for training deep neural networks.
Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework
Authors: Yang Liu
Date: 2018-05-03 00:00:00

To address modeling problems of brain-inspired intelligence, this thesis is
focused on researching in the semantic-oriented framework design for multimedia
and multimodal information. The Multimedia Neural Cognitive Computing (MNCC)
model was designed based on the nervous mechanism and cognitive architecture.
Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive
Computing (CNCC) framework was proposed based on MNCC model, and formal
description and analysis for CNCC framework was given. It would effectively
improve the performance of semantic processing for multimedia and cross-modal
information, and has far-reaching significance for exploration and realization
brain-inspired computing.
New Feature Detection Mechanism for Extended Kalman Filter Based Monocular SLAM with 1-Point RANSAC
Authors: Agniva Sengupta, Shafeeq Elanattil
Date: 2018-05-31 00:00:00

We present a different approach of feature point detection for improving the
accuracy of SLAM using single, monocular camera. Traditionally, Harris Corner
detection, SURF or FAST corner detectors are used for finding feature points of
interest in the image. We replace this with another approach, which involves
building a non-linear scale-space representation of images using Perona and
Malik Diffusion equation and computing the scale normalized Hessian at multiple
scale levels (KAZE feature). The feature points so detected are used to
estimate the state and pose of a mono camera using extended Kalman filter. By
using accelerated KAZE features and a more rigorous feature rejection routine
combined with 1-point RANSAC for outlier rejection, short baseline matching of
features are significantly improved, even with a lesser number of feature
points, especially in the presence of motion blur. We present a comparative
study of our proposal with FAST and show improved localization accuracy in
terms of absolute trajectory error.
Exact Mean Computation in Dynamic Time Warping Spaces
Authors: Markus Brill, Till Fluschnik, Vincent Froese, Brijnesh Jain, Rolf Niedermeier, David Schultz
Date: 2017-10-24 00:00:00
Tasks: Benchmarking, Dynamic Time Warping, Time Series, Time Series Analysis

Dynamic time warping constitutes a major tool for analyzing time series. In
particular, computing a mean series of a given sample of series in dynamic time
warping spaces (by minimizing the Fr\'echet function) is a challenging
computational problem, so far solved by several heuristic and inexact
strategies. We spot some inaccuracies in the literature on exact mean
computation in dynamic time warping spaces. Our contributions comprise an exact
dynamic program computing a mean (useful for benchmarking and evaluating known
heuristics). Based on this dynamic program, we empirically study properties
like uniqueness and length of a mean. Moreover, experimental evaluations reveal
substantial deficits of state-of-the-art heuristics in terms of their output
quality. We also give an exact polynomial-time algorithm for the special case
of binary time series.
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Authors: Louis Filstroff, Alberto Lumbreras, Cédric Févotte
Date: 2018-01-05 00:00:00
Proceeding: ICML 2018 7
Tasks: Form

We present novel understandings of the Gamma-Poisson (GaP) model, a
probabilistic matrix factorization model for count data. We show that GaP can
be rewritten free of the score/activation matrix. This gives us new insights
about the estimation of the topic/dictionary matrix by maximum marginal
likelihood estimation. In particular, this explains the robustness of this
estimator to over-specified values of the factorization rank, especially its
ability to automatically prune irrelevant dictionary columns, as empirically
observed in previous work. The marginalization of the activation matrix leads
in turn to a new Monte Carlo Expectation-Maximization algorithm with favorable
properties.
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
Authors: Sergi Valverde, Mostafa Salem, Mariano Cabezas, Deborah Pareto, Joan C. Vilanova, Lluís Ramió-Torrentà, Àlex Rovira, Joaquim Salvi, Arnau Oliver, Xavier Lladó
Date: 2018-05-31 00:00:00
Tasks: Domain Adaptation, Lesion Segmentation

In recent years, several convolutional neural network (CNN) methods have been
proposed for the automated white matter lesion segmentation of multiple
sclerosis (MS) patient images, due to their superior performance compared with
those of other state-of-the-art methods. However, the accuracies of CNN methods
tend to decrease significantly when evaluated on different image domains
compared with those used for training, which demonstrates the lack of
adaptability of CNNs to unseen imaging data. In this study, we analyzed the
effect of intensity domain adaptation on our recently proposed CNN-based MS
lesion segmentation method. Given a source model trained on two public MS
datasets, we investigated the transferability of the CNN model when applied to
other MRI scanners and protocols, evaluating the minimum number of annotated
images needed from the new domain and the minimum number of layers needed to
re-train to obtain comparable accuracy. Our analysis comprised MS patient data
from both a clinical center and the public ISBI2015 challenge database, which
permitted us to compare the domain adaptation capability of our model to that
of other state-of-the-art methods. For the ISBI2015 challenge, our one-shot
domain adaptation model trained using only a single image showed a performance
similar to that of other CNN methods that were fully trained using the entire
available training set, yielding a comparable human expert rater performance.
We believe that our experiments will encourage the MS community to incorporate
its use in different clinical settings with reduced amounts of annotated data.
This approach could be meaningful not only in terms of the accuracy in
delineating MS lesions but also in the related reductions in time and economic
costs derived from manual lesion labeling.
Cartoonish sketch-based face editing in videos using identity deformation transfer
Authors: Long Zhao, Fangda Han, Xi Peng, Xun Zhang, Mubbasir Kapadia, Vladimir Pavlovic, Dimitris N. Metaxas
Date: 2017-03-25 00:00:00
Tasks: Face Model

We address the problem of using hand-drawn sketches to create exaggerated
deformations to faces in videos, such as enlarging the shape or modifying the
position of eyes or mouth. This task is formulated as a 3D face model
reconstruction and deformation problem. We first recover the facial identity
and expressions from the video by fitting a face morphable model for each
frame. At the same time, user's editing intention is recognized from input
sketches as a set of facial modifications. Then a novel identity deformation
algorithm is proposed to transfer these facial deformations from 2D space to
the 3D facial identity directly while preserving the facial expressions. After
an optional stage for further refining the 3D face model, these changes are
propagated to the whole video with the modified identity. Both the user study
and experimental results demonstrate that our sketching framework can help
users effectively edit facial identities in videos, while high consistency and
fidelity are ensured at the same time.
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension
Authors: Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, Karthik Sankaranarayanan
Date: 2018-04-21 00:00:00
Proceeding: ACL 2018 7
Tasks: Descriptive, Reading Comprehension

We propose DuoRC, a novel dataset for Reading Comprehension (RC) that
motivates several new challenges for neural approaches in language
understanding beyond those offered by existing RC datasets. DuoRC contains
186,089 unique question-answer pairs created from a collection of 7680 pairs of
movie plots where each pair in the collection reflects two versions of the same
movie - one from Wikipedia and the other from IMDb - written by two different
authors. We asked crowdsourced workers to create questions from one version of
the plot and a different set of workers to extract or synthesize answers from
the other version. This unique characteristic of DuoRC where questions and
answers are created from different versions of a document narrating the same
underlying story, ensures by design, that there is very little lexical overlap
between the questions created from one version and the segments containing the
answer in the other version. Further, since the two versions have different
levels of plot detail, narration style, vocabulary, etc., answering questions
from the second version requires deeper language understanding and
incorporating external background knowledge. Additionally, the narrative style
of passages arising from movie plots (as opposed to typical descriptive
passages in existing datasets) exhibits the need to perform complex reasoning
over events across multiple sentences. Indeed, we observe that state-of-the-art
neural RC models which have achieved near human performance on the SQuAD
dataset, even when coupled with traditional NLP techniques to address the
challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42%
on DuoRC v/s 86% on SQuAD dataset). This opens up several interesting research
avenues wherein DuoRC could complement other RC datasets to explore novel
neural approaches for studying language understanding.
Imitation Learning with Concurrent Actions in 3D Games
Authors: Jack Harmer, Linus Gisslén, Jorge del Val, Henrik Holst, Joakim Bergdahl, Tom Olsson, Kristoffer Sjöö, Magnus Nordin
Date: 2018-03-14 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work we describe a novel deep reinforcement learning architecture
that allows multiple actions to be selected at every time-step in an efficient
manner. Multi-action policies allow complex behaviours to be learnt that would
otherwise be hard to achieve when using single action selection techniques. We
use both imitation learning and temporal difference (TD) reinforcement learning
(RL) to provide a 4x improvement in training time and 2.5x improvement in
performance over single action selection TD RL. We demonstrate the capabilities
of this network using a complex in-house 3D game. Mimicking the behavior of the
expert teacher significantly improves world state exploration and allows the
agents vision system to be trained more rapidly than TD RL alone. This initial
training technique kick-starts TD learning and the agent quickly learns to
surpass the capabilities of the expert.
Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
Authors: Ernesto Jimenez-Ruiz, Asan Agibetov, Matthias Samwald, Valerie Cross
Date: 2018-05-31 00:00:00
Tasks: Ontology Matching

Large ontologies still pose serious challenges to state-of-the-art ontology
alignment systems. In the paper we present an approach that combines a lexical
index, a neural embedding model and locality modules to effectively divide an
input ontology matching task into smaller and more tractable matching
(sub)tasks. We have conducted a comprehensive evaluation using the datasets of
the Ontology Alignment Evaluation Initiative. The results are encouraging and
suggest that the proposed methods are adequate in practice and can be
integrated within the workflow of state-of-the-art systems.
Deep Learning with unsupervised data labeling for weeds detection on UAV images
Authors: M. Dian. Bah, Adel Hafiane, Raphael Canals
Date: 2018-05-31 00:00:00
Tasks: Management

In modern agriculture, usually weeds control consists in spraying herbicides
all over the agricultural field. This practice involves significant waste and
cost of herbicide for farmers and environmental pollution. One way to reduce
the cost and environmental impact is to allocate the right doses of herbicide
at the right place and at the right time (Precision Agriculture). Nowadays,
Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for
weeds localization and management due to its ability to obtain the images of
the entire agricultural field with a very high spatial resolution and at low
cost. Despite the important advances in UAV acquisition systems, automatic
weeds detection remains a challenging problem because of its strong similarity
with the crops. Recently Deep Learning approach has shown impressive results in
different complex classification problem. However, this approach needs a
certain amount of training data but, creating large agricultural datasets with
pixel-level annotations by expert is an extremely time consuming task. In this
paper, we propose a novel fully automatic learning method using Convolutional
Neuronal Networks (CNNs) with unsupervised training dataset collection for
weeds detection from UAV images. The proposed method consists in three main
phases. First we automatically detect the crop lines and using them to identify
the interline weeds. In the second phase, interline weeds are used to
constitute the training dataset. Finally, we performed CNNs on this dataset to
build a model able to detect the crop and weeds in the images. The results
obtained are comparable to the traditional supervised training data labeling.
The accuracy gaps are 1.5% in the spinach field and 6% in the bean field.
KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings
Authors: Yuyu Zhang, Hanjun Dai, Kamil Toraman, Le Song
Date: 2018-05-31 00:00:00
Tasks: AI2 Reasoning Challenge, ARC, Knowledge Graph Embeddings, Knowledge Graphs, Question Answering

The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question
answering (QA) has been recently released. ARC only contains natural science
questions authored for human exams, which are hard to answer and require
advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art
QA systems fail to significantly outperform random baseline, reflecting the
difficult nature of this task. In this paper, we propose a novel framework for
answering science exam questions, which mimics human solving process in an
open-book exam. To address the reasoning challenge, we construct contextual
knowledge graphs respectively for the question itself and supporting sentences.
Our model learns to reason with neural embeddings of both knowledge graphs.
Experiments on the ARC Challenge Set show that our model outperforms the
previous state-of-the-art QA systems.
Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
Authors: Fangyi Zhang, Jürgen Leitner, ZongYuan Ge, Michael Milford, Peter Corke
Date: 2017-09-18 00:00:00

Various approaches have been proposed to learn visuo-motor policies for
real-world robotic applications. One solution is first learning in simulation
then transferring to the real world. In the transfer, most existing approaches
need real-world images with labels. However, the labelling process is often
expensive or even impractical in many robotic applications. In this paper, we
propose an adversarial discriminative sim-to-real transfer approach to reduce
the cost of labelling real data. The effectiveness of the approach is
demonstrated with modular networks in a table-top object reaching task where a
7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter
through visual observations. The adversarial transfer approach reduced the
labelled real data requirement by 50%. Policies can be transferred to real
environments with only 93 labelled and 186 unlabelled real images. The
transferred visuo-motor policies are robust to novel (not seen in training)
objects in clutter and even a moving target, achieving a 97.8% success rate and
1.8 cm control accuracy.
Sample Reuse via Importance Sampling in Information Geometric Optimization
Authors: Shinichi Shirakawa, Youhei Akimoto, Kazuki Ouchi, Kouzou Ohara
Date: 2018-05-31 00:00:00
Tasks: Evolutionary Algorithms, Incremental Learning

In this paper we propose a technique to reduce the number of function
evaluations, which is often the bottleneck of the black-box optimization, in
the information geometric optimization (IGO) that is a generic framework of the
probability model-based black-box optimization algorithms and generalizes
several well-known evolutionary algorithms, such as the population-based
incremental learning (PBIL) and the pure rank-$\mu$ update covariance matrix
adaptation evolution strategy (CMA-ES). In each iteration, the IGO algorithms
update the parameters of the probability distribution to the natural gradient
direction estimated by Monte-Carlo with the samples drawn from the current
distribution. Our strategy is to reuse previously generated and evaluated
samples based on the importance sampling. It is a technique to reduce the
estimation variance without introducing a bias in Monte-Carlo estimation. We
apply the sample reuse technique to the PBIL and the pure rank-$\mu$ update
CMA-ES and empirically investigate its effect. The experimental results show
that the sample reuse helps to reduce the number of function evaluations on
many benchmark functions for both the PBIL and the pure rank-$\mu$ update
CMA-ES. Moreover, we demonstrate how to combine the importance sampling
technique with a variant of the CMA-ES involving an algorithmic component that
is not derived in the IGO framework.
Agents and Devices: A Relative Definition of Agency
Authors: Laurent Orseau, Simon McGregor McGill, Shane Legg
Date: 2018-05-31 00:00:00

According to Dennett, the same system may be described using a `physical'
(mechanical) explanatory stance, or using an `intentional' (belief- and
goal-based) explanatory stance. Humans tend to find the physical stance more
helpful for certain systems, such as planets orbiting a star, and the
intentional stance for others, such as living animals. We define a formal
counterpart of physical and intentional stances within computational theory: a
description of a system as either a device, or an agent, with the key
difference being that `devices' are directly described in terms of an
input-output mapping, while `agents' are described in terms of the function
they optimise. Bayes' rule can then be applied to calculate the subjective
probability of a system being a device or an agent, based only on its
behaviour. We illustrate this using the trajectories of an object in a toy
grid-world domain.
SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation
Authors: Daniel Hershcovich, Leshem Choshen, Elior Sulem, Zohar Aizenbud, Ari Rappoport, Omri Abend
Date: 2018-05-31 00:00:00
Tasks: Semantic Parsing, UCCA Parsing

We announce a shared task on UCCA parsing in English, German and French, and call for participants to submit their systems. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general. Furthermore, existing applications for semantic evaluation that are based on UCCA will greatly benefit from better automatic methods for UCCA parsing. The competition website is https://competitions.codalab.org/competitions/19160
Imbalanced Ensemble Classifier for learning from imbalanced business school data set
Authors: Tanujit Chakraborty
Date: 2018-05-31 00:00:00
Tasks: feature selection, General Classification

Private business schools in India face a common problem of selecting quality
students for their MBA programs to achieve the desired placement percentage.
Generally, such data sets are biased towards one class, i.e., imbalanced in
nature. And learning from the imbalanced dataset is a difficult proposition.
This paper proposes an imbalanced ensemble classifier which can handle the
imbalanced nature of the dataset and achieves higher accuracy in case of the
feature selection (selection of important characteristics of students) cum
classification problem (prediction of placements based on the students'
characteristics) for Indian business school dataset. The optimal value of an
important model parameter is found. Numerical evidence is also provided using
Indian business school dataset to assess the outstanding performance of the
proposed classifier.
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
Authors: Su Young Lee, Sungik Choi, Sae-Young Chung
Date: 2018-05-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose Episodic Backward Update (EBU) - a novel deep reinforcement learning algorithm with a direct value propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states. Our computationally efficient recursive algorithm allows sparse and delayed rewards to propagate directly through all transitions of the sampled episode. We theoretically prove the convergence of the EBU method and experimentally demonstrate its performance in both deterministic and stochastic environments. Especially in 49 games of Atari 2600 domain, EBU achieves the same mean and median human normalized performance of DQN by using only 5% and 10% of samples, respectively.
Learning Tree Distributions by Hidden Markov Models
Authors: Davide Bacciu, Daniele Castellana
Date: 2018-05-31 00:00:00

Hidden tree Markov models allow learning distributions for tree structured
data while being interpretable as nondeterministic automata. We provide a
concise summary of the main approaches in literature, focusing in particular on
the causality assumptions introduced by the choice of a specific tree visit
direction. We will then sketch a novel non-parametric generalization of the
bottom-up hidden tree Markov model with its interpretation as a
nondeterministic tree automaton with infinite states.