pwc-archive/papers-with-abstracts huggingface dataset:
IQA: Visual Question Answering in Interactive Environments
Authors: Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, Ali Farhadi
Date: 2017-12-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Navigate, Reinforcement Learning, Visual Question Answering, Visual Question Answering (VQA)

We introduce Interactive Question Answering (IQA), the task of answering
questions that require an autonomous agent to interact with a dynamic visual
environment. IQA presents the agent with a scene and a question, like: "Are
there any apples in the fridge?" The agent must navigate around the scene,
acquire visual understanding of scene elements, interact with objects (e.g.
open refrigerators) and plan for a series of actions conditioned on the
question. Popular reinforcement learning approaches with a single controller
perform poorly on IQA owing to the large and diverse state space. We propose
the Hierarchical Interactive Memory Network (HIMN), consisting of a factorized
set of controllers, allowing the system to operate at multiple levels of
temporal abstraction. To evaluate HIMN, we introduce IQUAD V1, a new dataset
built upon AI2-THOR, a simulated photo-realistic environment of configurable
indoor scenes with interactive objects (code and dataset available at
https://github.com/danielgordon10/thor-iqa-cvpr-2018). IQUAD V1 has 75,000
questions, each paired with a unique scene configuration. Our experiments show
that our proposed model outperforms popular single controller based methods on
IQUAD V1. For sample questions and results, please view our video:
https://youtu.be/pXd3C-1jr98
Statistical Inferences for Polarity Identification in Natural Language
Authors: Nicolas Pröllochs, Stefan Feuerriegel, Dirk Neumann
Date: 2017-06-21 00:00:00
Tasks: Decision Making, Marketing

Information forms the basis for all human behavior, including the ubiquitous
decision-making that people constantly perform in their every day lives. It is
thus the mission of researchers to understand how humans process information to
reach decisions. In order to facilitate this task, this work proposes a novel
method of studying the reception of granular expressions in natural language.
The approach utilizes LASSO regularization as a statistical tool to extract
decisive words from textual content and draw statistical inferences based on
the correspondence between the occurrences of words and an exogenous response
variable. Accordingly, the method immediately suggests significant implications
for social sciences and Information Systems research: everyone can now identify
text segments and word choices that are statistically relevant to authors or
readers and, based on this knowledge, test hypotheses from behavioral research.
We demonstrate the contribution of our method by examining how authors
communicate subjective information through narrative materials. This allows us
to answer the question of which words to choose when communicating negative
information. On the other hand, we show that investors trade not only upon
facts in financial disclosures but are distracted by filler words and
non-informative language. Practitioners - for example those in the fields of
investor communications or marketing - can exploit our insights to enhance
their writings based on the true perception of word choice.
Adaptive Diffusions for Scalable Learning over Graphs
Authors: Dimitris Berberidis, Athanasios N. Nikolakopoulos, Georgios B. Giannakis
Date: 2018-04-05 00:00:00
Tasks: Classification, General Classification

Diffusion-based classifiers such as those relying on the Personalized
PageRank and the Heat kernel, enjoy remarkable classification accuracy at
modest computational requirements. Their performance however is affected by the
extent to which the chosen diffusion captures a typically unknown label
propagation mechanism, that can be specific to the underlying graph, and
potentially different for each class. The present work introduces a
disciplined, data-efficient approach to learning class-specific diffusion
functions adapted to the underlying network topology. The novel learning
approach leverages the notion of "landing probabilities" of class-specific
random walks, which can be computed efficiently, thereby ensuring scalability
to large graphs. This is supported by rigorous analysis of the properties of
the model as well as the proposed algorithms. Furthermore, a robust version of
the classifier facilitates learning even in noisy environments.
  Classification tests on real networks demonstrate that adapting the diffusion
function to the given graph and observed labels, significantly improves the
performance over fixed diffusions; reaching -- and many times surpassing -- the
classification accuracy of computationally heavier state-of-the-art competing
methods, that rely on node embeddings and deep neural networks.
Analyzing Self-Driving Cars on Twitter
Authors: Rizwan Sadiq, Mohsin Khan
Date: 2018-04-05 00:00:00
Tasks: General Classification, Self-Driving Cars

This paper studies users' perception regarding a controversial product,
namely self-driving (autonomous) cars. To find people's opinion regarding this
new technology, we used an annotated Twitter dataset, and extracted the topics
in positive and negative tweets using an unsupervised, probabilistic model
known as topic modeling. We later used the topics, as well as linguist and
Twitter specific features to classify the sentiment of the tweets. Regarding
the opinions, the result of our analysis shows that people are optimistic and
excited about the future technology, but at the same time they find it
dangerous and not reliable. For the classification task, we found Twitter
specific features, such as hashtags as well as linguistic features such as
emphatic words among top attributes in classifying the sentiment of the tweets.
Noise-resistant Deep Learning for Object Classification in 3D Point Clouds Using a Point Pair Descriptor
Authors: Dmytro Bobkov, Sili Chen, Ruiqing Jian, Muhammad Iqbal, Eckehard Steinbach
Date: 2018-04-05 00:00:00
Tasks: Classification, General Classification, Object, Retrieval

Object retrieval and classification in point cloud data is challenged by
noise, irregular sampling density and occlusion. To address this issue, we
propose a point pair descriptor that is robust to noise and occlusion and
achieves high retrieval accuracy. We further show how the proposed descriptor
can be used in a 4D convolutional neural network for the task of object
classification. We propose a novel 4D convolutional layer that is able to learn
class-specific clusters in the descriptor histograms. Finally, we provide
experimental validation on 3 benchmark datasets, which confirms the superiority
of the proposed approach.
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
Authors: Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, Anton Van Den Hengel
Date: 2017-11-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Reinforcement Learning, Translation, Vision and Language Navigation, Visual Navigation, Visual Question Answering, Visual Question Answering (VQA)

A robot that can carry out a natural-language instruction has been a dream
since before the Jetsons cartoon series imagined a life of leisure mediated by
a fleet of attentive robot helpers. It is a dream that remains stubbornly
distant. However, recent advances in vision and language methods have made
incredible progress in closely related areas. This is significant because a
robot interpreting a natural-language navigation instruction on the basis of
what it sees is carrying out a vision and language process that is similar to
Visual Question Answering. Both tasks can be interpreted as visually grounded
sequence-to-sequence translation problems, and many of the same methods are
applicable. To enable and encourage the application of vision and language
methods to the problem of interpreting visually-grounded navigation
instructions, we present the Matterport3D Simulator -- a large-scale
reinforcement learning environment based on real imagery. Using this simulator,
which can in future support a range of embodied vision and language tasks, we
provide the first benchmark dataset for visually-grounded natural language
navigation in real buildings -- the Room-to-Room (R2R) dataset.
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop
Authors: Katherine Bailey, Sunny Chopra
Date: 2018-04-05 00:00:00
Tasks: Few-Shot Learning, Few-Shot Text Classification, General Classification, Management, text-classification, Text Classification, Word Embeddings

Most of the literature around text classification treats it as a supervised
learning problem: given a corpus of labeled documents, train a classifier such
that it can accurately predict the classes of unseen documents. In industry,
however, it is not uncommon for a business to have entire corpora of documents
where few or none have been classified, or where existing classifications have
become meaningless. With web content, for example, poor taxonomy management can
result in labels being applied indiscriminately, making filtering by these
labels unhelpful. Our work aims to make it possible to classify an entire
corpus of unlabeled documents using a human-in-the-loop approach, where the
content owner manually classifies just one or two documents per category and
the rest can be automatically classified. This "few-shot" learning approach
requires rich representations of the documents such that those that have been
manually labeled can be treated as prototypes, and automatic classification of
the rest is a simple case of measuring the distance to prototypes. This
approach uses pre-trained word embeddings, where documents are represented
using a simple weighted average of constituent word embeddings. We have tested
the accuracy of the approach on existing labeled datasets and provide the
results here. We have also made code available for reproducing the results we
got on the 20 Newsgroups dataset.
FPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval
Authors: Sijia Chen, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani
Date: 2018-04-05 00:00:00
Tasks: Multi-Task Learning, Object, Object Localization, Object Tracking, Retrieval

The Localization of the target object for data retrieval is a key issue in
the Intelligent and Connected Transportation Systems (ICTS). However, due to
lack of intelligence in the traditional transportation system, it can take
tremendous resources to manually retrieve and locate the queried objects among
a large number of images. In order to solve this issue, we propose an effective
method to query-based object localization that uses artificial intelligence
techniques to automatically locate the queried object in the complex
background. The presented method is termed as Fine-grained and Progressive
Attention Localization Network (FPAN), which uses an image and a queried object
as input to accurately locate the target object in the image. Specifically, the
fine-grained attention module is naturally embedded into each layer of the
convolution neural network (CNN), thereby gradually suppressing the regions
that are irrelevant to the queried object and eventually shrinking attention to
the target area. We further employ top-down attentions fusion algorithm
operated by a learnable cascade up-sampling structure to establish the
connection between the attention map and the exact location of the queried
object in the original image. Furthermore, the FPAN is trained by multi-task
learning with box segmentation loss and cosine loss. At last, we conduct
comprehensive experiments on both queried-based digit localization and object
tracking with synthetic and benchmark datasets, respectively. The experimental
results show that our algorithm is far superior to other algorithms in the
synthesis datasets and outperforms most existing trackers on the OTB and VOT
datasets.
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction
Authors: Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang
Date: 2018-04-05 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification, Relation Classification

Reliably detecting relevant relations between entities in unstructured text
is a valuable resource for knowledge extraction, which is why it has awaken
significant interest in the field of Natural Language Processing. In this
paper, we present a system for relation classification and extraction based on
an ensemble of convolutional and recurrent neural networks that ranked first in
3 out of the 4 subtasks at SemEval 2018 Task 7. We provide detailed
explanations and grounds for the design choices behind the most relevant
features and analyze their importance.
Variational Knowledge Graph Reasoning
Authors: Wenhu Chen, Wenhan Xiong, Xifeng Yan, William Wang
Date: 2018-03-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Knowledge Graphs, Link Prediction, Relation, Variational Inference

Inferring missing links in knowledge graphs (KG) has attracted a lot of
attention from the research community. In this paper, we tackle a practical
query answering task involving predicting the relation of a given entity pair.
We frame this prediction problem as an inference problem in a probabilistic
graphical model and aim at resolving it from a variational inference
perspective. In order to model the relation between the query entity pair, we
assume that there exists an underlying latent variable (paths connecting two
nodes) in the KG, which carries the equivalent semantics of their relations.
However, due to the intractability of connections in large KGs, we propose to
use variation inference to maximize the evidence lower bound. More
specifically, our framework (\textsc{Diva}) is composed of three modules, i.e.
a posterior approximator, a prior (path finder), and a likelihood (path
reasoner). By using variational inference, we are able to incorporate them
closely into a unified architecture and jointly optimize them to perform KG
reasoning. With active interactions among these sub-modules, \textsc{Diva} is
better at handling noise and coping with more complex reasoning scenarios. In
order to evaluate our method, we conduct the experiment of the link prediction
task on multiple datasets and achieve state-of-the-art performances on both
datasets.
A Multi-Layer Approach to Superpixel-based Higher-order Conditional Random Field for Semantic Image Segmentation
Authors: Li Sulimowicz, Ishfaq Ahmad, Alexander Aved
Date: 2018-04-05 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Superpixel-based Higher-order Conditional random fields (SP-HO-CRFs) are
known for their effectiveness in enforcing both short and long spatial
contiguity for pixelwise labelling in computer vision. However, their
higher-order potentials are usually too complex to learn and often incur a high
computational cost in performing inference. We propose an new approximation
approach to SP-HO-CRFs that resolves these problems. Our approach is a
multi-layer CRF framework that inherits the simplicity from pairwise CRFs by
formulating both the higher-order and pairwise cues into the same pairwise
potentials in the first layer. Essentially, this approach provides accuracy
enhancement on the basis of pairwise CRFs without training by reusing their
pre-trained parameters and/or weights. The proposed multi-layer approach
performs especially well in delineating the boundary details (boarders) of
object categories such as "trees" and "bushes". Multiple sets of experiments
conducted on dataset MSRC-21 and PASCAL VOC 2012 validate the effectiveness and
efficiency of the proposed methods.
An Overview on Application of Machine Learning Techniques in Optical Networks
Authors: Francesco Musumeci, Cristina Rottondi, Avishek Nag, Irene Macaluso, Darko Zibar, Marco Ruffini, Massimo Tornatore
Date: 2018-03-21 00:00:00
Tasks: BIG-bench Machine Learning, Management

Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions.
Collaborative targeted inference from continuously indexed nuisance parameter estimators
Authors: Cheng Ju, Antoine Chambaz, Mark J. Van Der Laan
Date: 2018-03-31 00:00:00

We wish to infer the value of a parameter at a law from which we sample
independent observations. The parameter is smooth and we can define two
variation-independent features of the law, its $Q$- and $G$-components, such
that estimating them consistently at a fast enough product of rates allows to
build a confidence interval (CI) with a given asymptotic level from a plain
targeted minimum loss estimator (TMLE). Say that the above product is not fast
enough and the algorithm for the $G$-component is fine-tuned by a real-valued
$h$. A plain TMLE with an $h$ chosen by cross-validation would typically not
yield a CI. We construct a collaborative TMLE (C-TMLE) and show under mild
conditions that, if there exists an oracle $h$ that makes a bulky remainder
term asymptotically Gaussian, then the C-TMLE yields a CI. We illustrate our
findings with the inference of the average treatment effect. We conduct a
simulation study where the $G$-component is estimated by the LASSO and $h$ is
the bound on the coefficients' norms. It sheds light on small sample
properties, in the face of low- to high-dimensional baseline covariates, and
possibly positivity violation.
Inferring transportation modes from GPS trajectories using a convolutional neural network
Authors: Sina Dabiri, Kevin Heaslip
Date: 2018-04-05 00:00:00

Identifying the distribution of users' transportation modes is an essential
part of travel demand analysis and transportation planning. With the advent of
ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
for inferring commuters' mobility mode(s) is to leverage their GPS
trajectories. A majority of studies have proposed mode inference models based
on hand-crafted features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.
Regularizing Deep Networks by Modeling and Predicting Label Structure
Authors: Mohammadreza Mostajabi, Michael Maire, Gregory Shakhnarovich
Date: 2018-04-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Semantic Segmentation

We construct custom regularization functions for use in supervised training
of deep neural networks. Our technique is applicable when the ground-truth
labels themselves exhibit internal structure; we derive a regularizer by
learning an autoencoder over the set of annotations. Training thereby becomes a
two-phase procedure. The first phase models labels with an autoencoder. The
second phase trains the actual network of interest by attaching an auxiliary
branch that must predict output via a hidden layer of the autoencoder. After
training, we discard this auxiliary branch.
  We experiment in the context of semantic segmentation, demonstrating this
regularization strategy leads to consistent accuracy boosts over baselines,
both when training from scratch, or in combination with ImageNet pretraining.
Gains are also consistent over different choices of convolutional network
architecture. As our regularizer is discarded after training, our method has
zero cost at test time; the performance improvements are essentially free. We
are simply able to learn better network weights by building an abstract model
of the label space, and then training the network to understand this
abstraction alongside the original task.
Generalized Random Forests
Authors: Susan Athey, Julie Tibshirani, Stefan Wager
Date: 2016-10-05 00:00:00
Tasks: Heterogeneous Treatment Effect Estimation, quantile regression, valid

We propose generalized random forests, a method for non-parametric
statistical estimation based on random forests (Breiman, 2001) that can be used
to fit any quantity of interest identified as the solution to a set of local
moment equations. Following the literature on local maximum likelihood
estimation, our method considers a weighted set of nearby training examples;
however, instead of using classical kernel weighting functions that are prone
to a strong curse of dimensionality, we use an adaptive weighting function
derived from a forest designed to express heterogeneity in the specified
quantity of interest. We propose a flexible, computationally efficient
algorithm for growing generalized random forests, develop a large sample theory
for our method showing that our estimates are consistent and asymptotically
Gaussian, and provide an estimator for their asymptotic variance that enables
valid confidence intervals. We use our approach to develop new methods for
three statistical tasks: non-parametric quantile regression, conditional
average partial effect estimation, and heterogeneous treatment effect
estimation via instrumental variables. A software implementation, grf for R and
C++, is available from CRAN.