pwc-archive/papers-with-abstracts huggingface dataset:
SilhoNet: An RGB Method for 6D Object Pose Estimation
Authors: Gideon Billings, Matthew Johnson-Roberson
Date: 2018-09-18 00:00:00
Tasks: 3D Pose Estimation, 6D Pose Estimation, 6D Pose Estimation using RGB, Object, Pose Estimation, Robot Manipulation, Translation

Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a Convolutional Neural Network (CNN) pipeline that takes in Region of Interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed from the predicted silhouettes. We show that our method achieves better overall performance on the YCB-Video dataset than two state-of-the art networks for 6D pose estimation from monocular image input.
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity
Authors: Xinnuo Xu, Ondřej Dušek, Ioannis Konstas, Verena Rieser
Date: 2018-09-18 00:00:00
Tasks: Decoder, Diversity

We present three enhancements to existing encoder-decoder models for
open-domain conversational agents, aimed at effectively modeling coherence and
promoting output diversity: (1) We introduce a measure of coherence as the
GloVe embedding similarity between the dialogue context and the generated
response, (2) we filter our training corpora based on the measure of coherence
to obtain topically coherent and lexically diverse context-response pairs, (3)
we then train a response generator using a conditional variational autoencoder
model that incorporates the measure of coherence as a latent variable and uses
a context gate to guarantee topical consistency with the context and promote
lexical diversity. Experiments on the OpenSubtitles corpus show a substantial
improvement over competitive neural models in terms of BLEU score as well as
metrics of coherence and diversity.
FRAGE: Frequency-Agnostic Word Representation
Authors: Chengyue Gong, Di He, Xu Tan, Tao Qin, Li-Wei Wang, Tie-Yan Liu
Date: 2018-09-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Language Modeling, Language Modelling, Machine Translation, text-classification, Text Classification, Translation, Word Embeddings, Word Similarity

Continuous word representation (aka word embedding) is a basic building block in many neural network-based models used in natural language processing tasks. Although it is widely accepted that words with similar semantics should be close to each other in the embedding space, we find that word embeddings learned in several tasks are biased towards word frequency: the embeddings of high-frequency and low-frequency words lie in different subregions of the embedding space, and the embedding of a rare word and a popular word can be far from each other even if they are semantically similar. This makes learned word embeddings ineffective, especially for rare words, and consequently limits the performance of these neural network models. In this paper, we develop a neat, simple yet effective way to learn \emph{FRequency-AGnostic word Embedding} (FRAGE) using adversarial training. We conducted comprehensive studies on ten datasets across four natural language processing tasks, including word similarity, language modeling, machine translation and text classification. Results show that with FRAGE, we achieve higher performance than the baselines in all tasks.
Unbalanced Multi-Phase Distribution Grid Topology Estimation and Bus Phase Identification
Authors: Yizheng Liao, Yang Weng, Guangyi Liu, Zhongyang Zhao, Chin-Woo Tan, Ram Rajagopal
Date: 2018-09-18 00:00:00

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment, especially for \revv{low-voltage} distribution grids. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the topology of distribution grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.
Projective Splitting with Forward Steps only Requires Continuity
Authors: Patrick R. Johnstone, Jonathan Eckstein
Date: 2018-09-17 00:00:00

A recent innovation in projective splitting algorithms for monotone operator
inclusions has been the development of a procedure using two forward steps
instead of the customary proximal steps for operators that are Lipschitz
continuous. This paper shows that the Lipschitz assumption is unnecessary when
the forward steps are performed in finite-dimensional spaces: a backtracking
linesearch yields a convergent algorithm for operators that are merely
continuous with full domain.
Testing SensoGraph, a geometric approach for fast sensory evaluation
Authors: David Orden, Encarnación Fernández-Fernández, José M. Rodríguez-Nogales, Josefina Vila-Crespo
Date: 2018-09-16 00:00:00
Tasks: Clustering

This paper introduces SensoGraph, a novel approach for fast sensory
evaluation using two-dimensional geometric techniques. In the tasting sessions,
the assessors follow their own criteria to place samples on a tablecloth,
according to the similarity between samples. In order to analyse the data
collected, first a geometric clustering is performed to each tablecloth,
extracting connections between the samples. Then, these connections are used to
construct a global similarity matrix. Finally, a graph drawing algorithm is
used to obtain a 2D consensus graphic, which reflects the global opinion of the
panel by (1) positioning closer those samples that have been globally perceived
as similar and (2) showing the strength of the connections between samples. The
proposal is validated by performing four tasting sessions, with three types of
panels tasting different wines, and by developing a new software to implement
the proposed techniques. The results obtained show that the graphics provide
similar positionings of the samples as the consensus maps obtained by multiple
factor analysis (MFA), further providing extra information about connections
between samples, not present in any previous method. The main conclusion is
that the use of geometric techniques provides information complementary to MFA,
and of a different type. Finally, the method proposed is computationally able
to manage a significantly larger number of assessors than MFA, which can be
useful for the comparison of pictures by a huge number of consumers, via the
Internet.
Implementing Adaptive Separable Convolution for Video Frame Interpolation
Authors: Mart Kartašev, Carlo Rapisarda, Dominik Fay
Date: 2018-09-20 00:00:00
Tasks: Video Frame Interpolation

As Deep Neural Networks are becoming more popular, much of the attention is
being devoted to Computer Vision problems that used to be solved with more
traditional approaches. Video frame interpolation is one of such challenges
that has seen new research involving various techniques in deep learning. In
this paper, we replicate the work of Niklaus et al. on Adaptive Separable
Convolution, which claims high quality results on the video frame interpolation
task. We apply the same network structure trained on a smaller dataset and
experiment with various different loss functions, in order to determine the
optimal approach in data-scarce scenarios. The best resulting model is still
able to provide visually pleasing videos, although achieving lower evaluation
scores.
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
Authors: Krishnaram Kenthapadi, Thanh T. L. Tran
Date: 2018-09-20 00:00:00
Tasks: Privacy Preserving

Preserving privacy of users is a key requirement of web-scale analytics and
reporting applications, and has witnessed a renewed focus in light of recent
data breaches and new regulations such as GDPR. We focus on the problem of
computing robust, reliable analytics in a privacy-preserving manner, while
satisfying product requirements. We present PriPeARL, a framework for
privacy-preserving analytics and reporting, inspired by differential privacy.
We describe the overall design and architecture, and the key modeling
components, focusing on the unique challenges associated with privacy,
coverage, utility, and consistency. We perform an experimental study in the
context of ads analytics and reporting at LinkedIn, thereby demonstrating the
tradeoffs between privacy and utility needs, and the applicability of
privacy-preserving mechanisms to real-world data. We also highlight the lessons
learned from the production deployment of our system at LinkedIn.
Spline-Based Probability Calibration
Authors: Brian Lucena
Date: 2018-09-20 00:00:00
Tasks: General Classification

In many classification problems it is desirable to output well-calibrated
probabilities on the different classes. We propose a robust, non-parametric
method of calibrating probabilities called SplineCalib that utilizes smoothing
splines to determine a calibration function. We demonstrate how applying
certain transformations as part of the calibration process can improve
performance on problems in deep learning and other domains where the scores
tend to be "overconfident". We adapt the approach to multi-class problems and
find that better calibration can improve accuracy as well as log-loss by better
resolving uncertain cases. Finally, we present a cross-validated approach to
calibration which conserves data. Significant improvements to log-loss and
accuracy are shown on several different problems. We also introduce the
ml-insights python package which contains an implementation of the SplineCalib
algorithm.
Exemplar-based synthesis of geology using kernel discrepancies and generative neural networks
Authors: Shing Chan, Ahmed H. Elsheikh
Date: 2018-09-20 00:00:00

We propose a framework for synthesis of geological images based on an
exemplar image. We synthesize new realizations such that the discrepancy in the
patch distribution between the realizations and the exemplar image is
minimized. Such discrepancy is quantified using a kernel method for two-sample
test called maximum mean discrepancy. To enable fast synthesis, we train a
generative neural network in an offline phase to sample realizations
efficiently during deployment, while also providing a parametrization of the
synthesis process. We assess the framework on a classical binary image
representing channelized subsurface reservoirs, finding that the method
reproduces the visual patterns and spatial statistics (image histogram and
two-point probability functions) of the exemplar image.
Benchmarking Reinforcement Learning Algorithms on Real-World Robots
Authors: A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra
Date: 2018-09-20 00:00:00
Tasks: Benchmarking, continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Through many recent successes in simulation, model-free reinforcement
learning has emerged as a promising approach to solving continuous control
robotic tasks. The research community is now able to reproduce, analyze and
build quickly on these results due to open source implementations of learning
algorithms and simulated benchmark tasks. To carry forward these successes to
real-world applications, it is crucial to withhold utilizing the unique
advantages of simulations that do not transfer to the real world and experiment
directly with physical robots. However, reinforcement learning research with
physical robots faces substantial resistance due to the lack of benchmark tasks
and supporting source code. In this work, we introduce several reinforcement
learning tasks with multiple commercially available robots that present varying
levels of learning difficulty, setup, and repeatability. On these tasks, we
test the learning performance of off-the-shelf implementations of four
reinforcement learning algorithms and analyze sensitivity to their
hyper-parameters to determine their readiness for applications in various
real-world tasks. Our results show that with a careful setup of the task
interface and computations, some of these implementations can be readily
applicable to physical robots. We find that state-of-the-art learning
algorithms are highly sensitive to their hyper-parameters and their relative
ordering does not transfer across tasks, indicating the necessity of re-tuning
them for each task for best performance. On the other hand, the best
hyper-parameter configuration from one task may often result in effective
learning on held-out tasks even with different robots, providing a reasonable
default. We make the benchmark tasks publicly available to enhance
reproducibility in real-world reinforcement learning.
Symbolic Priors for RNN-based Semantic Parsing
Authors: Chunyang Xiao, Marc Dymetman, Claire Gardent
Date: 2018-09-20 00:00:00
Tasks: Question Answering, Semantic Parsing

Seq2seq models based on Recurrent Neural Networks (RNNs) have recently
received a lot of attention in the domain of Semantic Parsing for Question
Answering. While in principle they can be trained directly on pairs (natural
language utterances, logical forms), their performance is limited by the amount
of available data. To alleviate this problem, we propose to exploit various
sources of prior knowledge: the well-formedness of the logical forms is modeled
by a weighted context-free grammar; the likelihood that certain entities
present in the input utterance are also present in the logical form is modeled
by weighted finite-state automata. The grammar and automata are combined
together through an efficient intersection algorithm to form a soft guide
("background") to the RNN. We test our method on an extension of the Overnight
dataset and show that it not only strongly improves over an RNN baseline, but
also outperforms non-RNN models based on rich sets of hand-crafted features.
Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements
Authors: Shreyas S. Shivakumar, Kartik Mohta, Bernd Pfrommer, Vijay Kumar, Camillo J. Taylor
Date: 2018-09-20 00:00:00
Tasks: Depth Estimation

We present an approach to depth estimation that fuses information from a
stereo pair with sparse range measurements derived from a LIDAR sensor or a
range camera. The goal of this work is to exploit the complementary strengths
of the two sensor modalities, the accurate but sparse range measurements and
the ambiguous but dense stereo information. These two sources are effectively
and efficiently fused by combining ideas from anisotropic diffusion and
semi-global matching.
  We evaluate our approach on the KITTI 2015 and Middlebury 2014 datasets,
using randomly sampled ground truth range measurements as our sparse depth
input. We achieve significant performance improvements with a small fraction of
range measurements on both datasets. We also provide qualitative results from
our platform using the PMDTec Monstar sensor. Our entire pipeline runs on an
NVIDIA TX-2 platform at 5Hz on 1280x1024 stereo images with 128 disparity
levels.
Joint Multilingual Supervision for Cross-lingual Entity Linking
Authors: Shyam Upadhyay, Nitish Gupta, Dan Roth
Date: 2018-09-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Cross-Lingual Entity Linking, Entity Linking

Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in
any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most
languages is challenging, owing to limited availability of resources as
supervision. We address this challenge by developing the first XEL approach
that combines supervision from multiple languages jointly. This enables our
approach to: (a) augment the limited supervision in the target language with
additional supervision from a high-resource language (like English), and (b)
train a single entity linking model for multiple languages, improving upon
individually trained models for each language. Extensive evaluation on three
benchmark datasets across 8 languages shows that our approach significantly
improves over the current state-of-the-art. We also provide analyses in two
limited resource settings: (a) zero-shot setting, when no supervision in the
target language is available, and in (b) low-resource setting, when some
supervision in the target language is available. Our analysis provides insights
into the limitations of zero-shot XEL approaches in realistic scenarios, and
shows the value of joint supervision in low-resource settings.
The unreasonable effectiveness of small neural ensembles in high-dimensional brain
Authors: A. N. Gorban, V. A. Makarov, I. Y. Tyukin
Date: 2018-09-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Despite the widely-spread consensus on the brain complexity, sprouts of the
single neuron revolution emerged in neuroscience in the 1970s. They brought
many unexpected discoveries, including grandmother or concept cells and sparse
coding of information in the brain.
  In machine learning for a long time, the famous curse of dimensionality
seemed to be an unsolvable problem. Nevertheless, the idea of the blessing of
dimensionality becomes gradually more and more popular. Ensembles of
non-interacting or weakly interacting simple units prove to be an effective
tool for solving essentially multidimensional problems. This approach is
especially useful for one-shot (non-iterative) correction of errors in large
legacy artificial intelligence systems.
  These simplicity revolutions in the era of complexity have deep fundamental
reasons grounded in geometry of multidimensional data spaces. To explore and
understand these reasons we revisit the background ideas of statistical
physics. In the course of the 20th century they were developed into the
concentration of measure theory. New stochastic separation theorems reveal the
fine structure of the data clouds.
  We review and analyse biological, physical, and mathematical problems at the
core of the fundamental question: how can high-dimensional brain organise
reliable and fast learning in high-dimensional world of data by simple tools?
  Two critical applications are reviewed to exemplify the approach: one-shot
correction of errors in intellectual systems and emergence of static and
associative memories in ensembles of single neurons.
Faster RER-CNN: application to the detection of vehicles in aerial images
Authors: Jean Ogier du Terrail, Frédéric Jurie
Date: 2018-09-20 00:00:00

Detecting small vehicles in aerial images is a difficult job that can be challenging even for humans. Rotating objects, low resolution, small inter-class variability and very large images comprising complicated backgrounds render the work of photo-interpreters tedious and wearisome. Unfortunately even the best classical detection pipelines like Faster R-CNN cannot be used off-the-shelf with good results because they were built to process object centric images from day-to-day life with multi-scale vertical objects. In this work we build on the Faster R-CNN approach to turn it into a detection framework that deals appropriately with the rotation equivariance inherent to any aerial image task. This new pipeline (Faster Rotation Equivariant Regions CNN) gives, without any bells and whistles, state-of-the-art results on one of the most challenging aerial imagery datasets: VeDAI and give good results w.r.t. the baseline Faster R-CNN on two others: Munich and GoogleEarth .
Lessons learned in multilingual grounded language learning
Authors: Ákos Kádár, Desmond Elliott, Marc-Alexandre Côté, Grzegorz Chrupała, Afra Alishahi
Date: 2018-09-20 00:00:00
Proceeding: CONLL 2018 10
Tasks: Grounded language learning, Sentence

Recent work has shown how to learn better visual-semantic embeddings by
leveraging image descriptions in more than one language. Here, we investigate
in detail which conditions affect the performance of this type of grounded
language learning model. We show that multilingual training improves over
bilingual training, and that low-resource languages benefit from training with
higher-resource languages. We demonstrate that a multilingual model can be
trained equally well on either translations or comparable sentence pairs, and
that annotating the same set of images in multiple language enables further
improvements via an additional caption-caption ranking objective.
Machine Learning for semi linear PDEs
Authors: Quentin Chan-Wai-Nam, Joseph Mikael, Xavier Warin
Date: 2018-09-20 00:00:00
Tasks: BIG-bench Machine Learning

Recent machine learning algorithms dedicated to solving semi-linear PDEs are
improved by using different neural network architectures and different
parameterizations. These algorithms are compared to a new one that solves a
fixed point problem by using deep learning techniques. This new algorithm
appears to be competitive in terms of accuracy with the best existing
algorithms.
Syntactico-Semantic Reasoning using PCFG, MEBN & PP Attachment Ambiguity
Authors: Shrinivasan R Patnaik Patnaikuni, Dr. Sachin R Gengaje
Date: 2018-09-20 00:00:00

Probabilistic context free grammars (PCFG) have been the core of the
probabilistic reasoning based parsers for several years especially in the
context of the NLP. Multi entity bayesian networks (MEBN) a First Order Logic
probabilistic reasoning methodology is widely adopted and used method for
uncertainty reasoning. Further upper ontology like Probabilistic Ontology Web
Language (PR-OWL) built using MEBN takes care of probabilistic ontologies which
model and capture the uncertainties inherent in the domain's semantic
information. The paper attempts to establish a link between probabilistic
reasoning in PCFG and MEBN by proposing a formal description of PCFG driven by
MEBN leading to usage of PR-OWL modeled ontologies in PCFG parsers.
Furthermore, the paper outlines an approach to resolve prepositional phrase
(PP) attachment ambiguity using the proposed mapping between PCFG and MEBN.