pwc-archive/papers-with-abstracts huggingface dataset:
Detection and Tracking of General Movable Objects in Large 3D Maps
Authors: Nils Bore, Johan Ekekrantz, Patric Jensfelt, John Folkesson
Date: 2017-12-22 00:00:00

This paper studies the problem of detection and tracking of general objects
with long-term dynamics, observed by a mobile robot moving in a large
environment. A key problem is that due to the environment scale, it can only
observe a subset of the objects at any given time. Since some time passes
between observations of objects in different places, the objects might be moved
when the robot is not there. We propose a model for this movement in which the
objects typically only move locally, but with some small probability they jump
longer distances, through what we call global motion. For filtering, we
decompose the posterior over local and global movements into two linked
processes. The posterior over the global movements and measurement associations
is sampled, while we track the local movement analytically using Kalman
filters. This novel filter is evaluated on point cloud data gathered
autonomously by a mobile robot over an extended period of time. We show that
tracking jumping objects is feasible, and that the proposed probabilistic
treatment outperforms previous methods when applied to real world data. The key
to efficient probabilistic tracking in this scenario is focused sampling of the
object posteriors.
Manuscripts in Time and Space: Experiments in Scriptometrics on an Old French Corpus
Authors: Jean-Baptiste Camps
Date: 2018-01-30 00:00:00
Tasks: Clustering

Witnesses of medieval literary texts, preserved in manuscript, are layered
objects , being almost exclusively copies of copies. This results in multiple
and hard to distinguish linguistic strata -- the author's scripta interacting
with the scriptae of the various scribes -- in a context where literary written
language is already a dialectal hybrid. Moreover, no single linguistic
phenomenon allows to distinguish between different scriptae, and only the
combination of multiple characteristics is likely to be significant [9] -- but
which ones? The most common approach is to search for these features in a set
of previously selected texts, that are supposed to be representative of a given
scripta. This can induce a circularity, in which texts are used to select
features that in turn characterise them as belonging to a linguistic area. To
counter this issue, this paper offers an unsupervised and corpus-based
approach, in which clustering methods are applied to an Old French corpus to
identify main divisions and groups. Ultimately, scriptometric profiles are
built for each of them.
Pilot study for the COST Action "Reassembling the Republic of Letters": language-driven network analysis of letters from the Hartlib's Papers
Authors: Barbara McGillivray, Federico Sangati
Date: 2018-01-30 00:00:00

The present report summarizes an exploratory study which we carried out in
the context of the COST Action IS1310 "Reassembling the Republic of Letters,
1500-1800", and which is relevant to the activities of Working Group 3 "Texts
and Topics" and Working Group 2 "People and Networks". In this study we
investigated the use of Natural Language Processing (NLP) and Network Text
Analysis on a small sample of seventeenth-century letters selected from Hartlib
Papers, whose records are in one of the catalogues of Early Modern Letters
Online (EMLO) and whose online edition is available on the website of the
Humanities Research Institute at the University of Sheffield
(http://www.hrionline.ac.uk/hartlib/). We outline the NLP pipeline used to
automatically process the texts into a network representation, in order to
identify the texts' "narrative centrality", i.e. the most central entities in
the texts, and the relations between them.
An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering
Authors: Hongzhi Zhang, Guandong Xu, Xiao Liang, Tinglei Huang, Kun fu
Date: 2018-01-30 00:00:00
Tasks: Knowledge Base Question Answering, Question Answering, Relation, Semantic Similarity, Semantic Textual Similarity

Relation detection plays a crucial role in Knowledge Base Question Answering
(KBQA) because of the high variance of relation expression in the question.
Traditional deep learning methods follow an encoding-comparing paradigm, where
the question and the candidate relation are represented as vectors to compare
their semantic similarity. Max- or average- pooling operation, which compresses
the sequence of words into fixed-dimensional vectors, becomes the bottleneck of
information. In this paper, we propose to learn attention-based word-level
interactions between questions and relations to alleviate the bottleneck issue.
Similar to the traditional models, the question and relation are firstly
represented as sequences of vectors. Then, instead of merging the sequence into
a single vector with pooling operation, soft alignments between words from the
question and the relation are learned. The aligned words are subsequently
compared with the convolutional neural network (CNN) and the comparison results
are merged finally. Through performing the comparison on low-level
representations, the attention-based word-level interaction model (ABWIM)
relieves the information loss issue caused by merging the sequence into a
fixed-dimensional vector before the comparison. The experimental results of
relation detection on both SimpleQuestions and WebQuestions datasets show that
ABWIM achieves state-of-the-art accuracy, demonstrating its effectiveness.
Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation
Authors: Stefan Hoermann, Philipp Henzler, Martin Bach, Klaus Dietmayer
Date: 2018-01-30 00:00:00
Tasks: Object, object-detection, Object Detection, Pose Estimation

We tackle the problem of object detection and pose estimation in a shared
space downtown environment. For perception multiple laser scanners with
360{\deg} coverage were fused in a dynamic occupancy grid map (DOGMa). A
single-stage deep convolutional neural network is trained to provide object
hypotheses comprising of shape, position, orientation and an existence score
from a single input DOGMa. Furthermore, an algorithm for offline object
extraction was developed to automatically label several hours of training data.
The algorithm is based on a two-pass trajectory extraction, forward and
backward in time. Typical for engineered algorithms, the automatic label
generation suffers from misdetections, which makes hard negative mining
impractical. Therefore, we propose a loss function counteracting the high
imbalance between mostly static background and extremely rare dynamic grid
cells. Experiments indicate, that the trained network has good generalization
capabilities since it detects objects occasionally lost by the label algorithm.
Evaluation reaches an average precision (AP) of 75.9%
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Authors: Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
Date: 2017-10-12 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: regression

Spectral decomposition of the Koopman operator is attracting attention as a
tool for the analysis of nonlinear dynamical systems. Dynamic mode
decomposition is a popular numerical algorithm for Koopman spectral analysis;
however, we often need to prepare nonlinear observables manually according to
the underlying dynamics, which is not always possible since we may not have any
a priori knowledge about them. In this paper, we propose a fully data-driven
method for Koopman spectral analysis based on the principle of learning Koopman
invariant subspaces from observed data. To this end, we propose minimization of
the residual sum of squares of linear least-squares regression to estimate a
set of functions that transforms data into a form in which the linear
regression fits well. We introduce an implementation with neural networks and
evaluate performance empirically using nonlinear dynamical systems and
applications.
Optimal Number of Choices in Rating Contexts
Authors: Sam Ganzfried, Farzana Yusuf
Date: 2016-05-21 00:00:00

In many settings people must give numerical scores to entities from a small
discrete set. For instance, rating physical attractiveness from 1--5 on dating
sites, or papers from 1--10 for conference reviewing. We study the problem of
understanding when using a different number of options is optimal. We consider
the case when scores are uniform random and Gaussian. We study computationally
when using 2, 3, 4, 5, and 10 options out of a total of 100 is optimal in these
models (though our theoretical analysis is for a more general setting with $k$
choices from $n$ total options as well as a continuous underlying space). One
may expect that using more options would always improve performance in this
model, but we show that this is not necessarily the case, and that using fewer
choices---even just two---can surprisingly be optimal in certain situations.
While in theory for this setting it would be optimal to use all 100 options, in
practice this is prohibitive, and it is preferable to utilize a smaller number
of options due to humans' limited computational resources. Our results could
have many potential applications, as settings requiring entities to be ranked
by humans are ubiquitous. There could also be applications to other fields such
as signal or image processing where input values from a large set must be
mapped to output values in a smaller set.
Deep Learning for Sentiment Analysis : A Survey
Authors: Lei Zhang, Shuai Wang, Bing Liu
Date: 2018-01-24 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Sentiment Analysis, Survey

Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.
A State-of-the-Art of Semantic Change Computation
Authors: Xuri Tang
Date: 2018-01-30 00:00:00
Tasks: Data Visualization

This paper reviews the state-of-the-art of semantic change computation, one
emerging research field in computational linguistics, proposing a framework
that summarizes the literature by identifying and expounding five essential
components in the field: diachronic corpus, diachronic word sense
characterization, change modelling, evaluation data and data visualization.
Despite the potential of the field, the review shows that current studies are
mainly focused on testifying hypotheses proposed in theoretical linguistics and
that several core issues remain to be solved: the need for diachronic corpora
of languages other than English, the need for comprehensive evaluation data for
evaluation, the comparison and construction of approaches to diachronic word
sense characterization and change modelling, and further exploration of data
visualization techniques for hypothesis justification.
Fast Power system security analysis with Guided Dropout
Authors: Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Antoine Marot, Patrick Panciatici
Date: 2018-01-30 00:00:00

We propose a new method to efficiently compute load-flows (the steady-state
of the power-grid for given productions, consumptions and grid topology),
substituting conventional simulators based on differential equation solvers. We
use a deep feed-forward neural network trained with load-flows precomputed by
simulation. Our architecture permits to train a network on so-called "n-1"
problems, in which load flows are evaluated for every possible line
disconnection, then generalize to "n-2" problems without retraining (a clear
advantage because of the combinatorial nature of the problem). To that end, we
developed a technique bearing similarity with "dropout", which we named "guided
dropout".
Accelerating recurrent neural network language model based online speech recognition system
Authors: Kyungmin Lee, Chiyoun Park, Namhoon Kim, Jaewon Lee
Date: 2018-01-30 00:00:00
Tasks: CPU, GPU, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

This paper presents methods to accelerate recurrent neural network based
language models (RNNLMs) for online speech recognition systems. Firstly, a
lossy compression of the past hidden layer outputs (history vector) with
caching is introduced in order to reduce the number of LM queries. Next, RNNLM
computations are deployed in a CPU-GPU hybrid manner, which computes each layer
of the model on a more advantageous platform. The added overhead by data
exchanges between CPU and GPU is compensated through a frame-wise batching
strategy. The performance of the proposed methods evaluated on LibriSpeech test
sets indicates that the reduction in history vector precision improves the
average recognition speed by 1.23 times with minimum degradation in accuracy.
On the other hand, the CPU-GPU hybrid parallelization enables RNNLM based
real-time recognition with a four times improvement in speed.
Structured Memory based Deep Model to Detect as well as Characterize Novel Inputs
Authors: Pratik Prabhanjan Brahma, Qiuyuan Huang, Dapeng Wu
Date: 2018-01-30 00:00:00
Tasks: Memorization

While deep learning has pushed the boundaries in various machine learning
tasks, the current models are still far away from replicating many functions
that a normal human brain can do. Explicit memorization based deep architecture
have been recently proposed with the objective to understand and predict
better. In this work, we design a system that involves a primary learner and an
adjacent representational memory bank which is organized using a comparative
learner. This spatially forked deep architecture with a structured memory can
simultaneously predict and reason about the nature of an input, which may even
belong to a category never seen in the training data, by relating it with the
memorized past representations at the higher layers. Characterizing images of
unseen object classes in both synthetic and real world datasets is used as an
example to showcase the operational success of the proposed framework.
ReNN: Rule-embedded Neural Networks
Authors: Hu Wang
Date: 2018-01-30 00:00:00
Tasks: Time Series, Time Series Analysis

The artificial neural network shows powerful ability of inference, but it is
still criticized for lack of interpretability and prerequisite needs of big
dataset. This paper proposes the Rule-embedded Neural Network (ReNN) to
overcome the shortages. ReNN first makes local-based inferences to detect local
patterns, and then uses rules based on domain knowledge about the local
patterns to generate rule-modulated map. After that, ReNN makes global-based
inferences that synthesizes the local patterns and the rule-modulated map. To
solve the optimization problem caused by rules, we use a two-stage optimization
strategy to train the ReNN model. By introducing rules into ReNN, we can
strengthen traditional neural networks with long-term dependencies which are
difficult to learn with limited empirical dataset, thus improving inference
accuracy. The complexity of neural networks can be reduced since long-term
dependencies are not modeled with neural connections, and thus the amount of
data needed to optimize the neural networks can be reduced. Besides, inferences
from ReNN can be analyzed with both local patterns and rules, and thus have
better interpretability. In this paper, ReNN has been validated with a
time-series detection problem.
Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration
Authors: Tathagata Chakraborti, Subbarao Kambhampati
Date: 2018-01-30 00:00:00

Effective collaboration between humans and AI-based systems requires
effective modeling of the human in the loop, both in terms of the mental state
as well as the physical capabilities of the latter. However, these models can
also open up pathways for manipulating and exploiting the human in the hopes of
achieving some greater good, especially when the intent or values of the AI and
the human are not aligned or when they have an asymmetrical relationship with
respect to knowledge or computation power. In fact, such behavior does not
necessarily require any malicious intent but can rather be borne out of
cooperative scenarios. It is also beyond simple misinterpretation of intents,
as in the case of value alignment problems, and thus can be effectively
engineered if desired. Such techniques already exist and pose several
unresolved ethical and moral questions with regards to the design of autonomy.
In this paper, we illustrate some of these issues in a teaming scenario and
investigate how they are perceived by participants in a thought experiment.
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Authors: Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, Byoung-Tak Zhang
Date: 2017-03-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Transfer Learning

Catastrophic forgetting is a problem of neural networks that loses the
information of the first task after training the second task. Here, we propose
a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM
incrementally matches the moment of the posterior distribution of the neural
network which is trained on the first and the second task, respectively. To
make the search space of posterior parameter smooth, the IMM procedure is
complemented by various transfer learning techniques including weight transfer,
L2-norm of the old and the new parameter, and a variant of dropout with the old
parameter. We analyze our approach on a variety of datasets including the
MNIST, CIFAR-10, Caltech-UCSD-Birds, and Lifelog datasets. The experimental
results show that IMM achieves state-of-the-art performance by balancing the
information between an old and a new network.
Neural Collaborative Autoencoder
Authors: Qibing Li, Xiaolin Zheng, Xinyue Wu
Date: 2017-12-25 00:00:00
Tasks: Collaborative Filtering, Data Augmentation

In recent years, deep neural networks have yielded state-of-the-art
performance on several tasks. Although some recent works have focused on
combining deep learning with recommendation, we highlight three issues of
existing models. First, these models cannot work on both explicit and implicit
feedback, since the network structures are specially designed for one
particular case. Second, due to the difficulty on training deep neural
networks, existing explicit models do not fully exploit the expressive
potential of deep learning. Third, neural network models are easier to overfit
on the implicit setting than shallow models. To tackle these issues, we present
a generic recommender framework called Neural Collaborative Autoencoder (NCAE)
to perform collaborative filtering, which works well for both explicit feedback
and implicit feedback. NCAE can effectively capture the subtle hidden
relationships between interactions via a non-linear matrix factorization
process. To optimize the deep architecture of NCAE, we develop a three-stage
pre-training mechanism that combines supervised and unsupervised feature
learning. Moreover, to prevent overfitting on the implicit setting, we propose
an error reweighting module and a sparsity-aware data-augmentation strategy.
Extensive experiments on three real-world datasets demonstrate that NCAE can
significantly advance the state-of-the-art.
Alternating minimization for dictionary learning: Local Convergence Guarantees
Authors: Niladri S. Chatterji, Peter L. Bartlett
Date: 2017-11-09 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Dictionary Learning

We present theoretical guarantees for an alternating minimization algorithm for the dictionary learning/sparse coding problem. The dictionary learning problem is to factorize vector samples $y^{1},y^{2},\ldots, y^{n}$ into an appropriate basis (dictionary) $A^*$ and sparse vectors $x^{1*},\ldots,x^{n*}$. Our algorithm is a simple alternating minimization procedure that switches between $\ell_1$ minimization and gradient descent in alternate steps. Dictionary learning and specifically alternating minimization algorithms for dictionary learning are well studied both theoretically and empirically. However, in contrast to previous theoretical analyses for this problem, we replace a condition on the operator norm (that is, the largest magnitude singular value) of the true underlying dictionary $A^*$ with a condition on the matrix infinity norm (that is, the largest magnitude term). Our guarantees are under a reasonable generative model that allows for dictionaries with growing operator norms, and can handle an arbitrary level of overcompleteness, while having sparsity that is information theoretically optimal. We also establish upper bounds on the sample complexity of our algorithm.
Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account
Authors: Ardavan S. Nobandegani, Kevin da Silva Castanheira, A. Ross Otto, Thomas R. Shultz
Date: 2018-01-30 00:00:00
Tasks: Decision Making

The Availability bias, manifested in the over-representation of extreme
eventualities in decision-making, is a well-known cognitive bias, and is
generally taken as evidence of human irrationality. In this work, we present
the first rational, metacognitive account of the Availability bias, formally
articulated at Marr's algorithmic level of analysis. Concretely, we present a
normative, metacognitive model of how a cognitive system should over-represent
extreme eventualities, depending on the amount of time available at its
disposal for decision-making. Our model also accounts for two well-known
framing effects in human decision-making under risk---the fourfold pattern of
risk preferences in outcome probability (Tversky & Kahneman, 1992) and in
outcome magnitude (Markovitz, 1952)---thereby providing the first
metacognitively-rational basis for those effects. Empirical evidence,
furthermore, confirms an important prediction of our model. Surprisingly, our
model is unimaginably robust with respect to its focal parameter. We discuss
the implications of our work for studies on human decision-making, and conclude
by presenting a counterintuitive prediction of our model, which, if confirmed,
would have intriguing implications for human decision-making under risk. To our
knowledge, our model is the first metacognitive, resource-rational process
model of cognitive biases in decision-making.
Open3D: A Modern Library for 3D Data Processing
Authors: Qian-Yi Zhou, Jaesik Park, Vladlen Koltun
Date: 2018-01-30 00:00:00
Tasks: Point Cloud Registration

Open3D is an open-source library that supports rapid development of software
that deals with 3D data. The Open3D frontend exposes a set of carefully
selected data structures and algorithms in both C++ and Python. The backend is
highly optimized and is set up for parallelization. Open3D was developed from a
clean slate with a small and carefully considered set of dependencies. It can
be set up on different platforms and compiled from source with minimal effort.
The code is clean, consistently styled, and maintained via a clear code review
mechanism. Open3D has been used in a number of published research projects and
is actively deployed in the cloud. We welcome contributions from the
open-source community.