pwc-archive/papers-with-abstracts huggingface dataset:
Co-regularized Alignment for Unsupervised Domain Adaptation
Authors: Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, William T. Freeman, Gregory Wornell
Date: 2018-11-13 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Deep neural networks, trained with large amount of labeled data, can fail to
generalize well when tested with examples from a \emph{target domain} whose
distribution differs from the training data distribution, referred as the
\emph{source domain}. It can be expensive or even infeasible to obtain required
amount of labeled data in all possible domains. Unsupervised domain adaptation
sets out to address this problem, aiming to learn a good predictive model for
the target domain using labeled examples from the source domain but only
unlabeled examples from the target domain. Domain alignment approaches this
problem by matching the source and target feature distributions, and has been
used as a key component in many state-of-the-art domain adaptation methods.
However, matching the marginal feature distributions does not guarantee that
the corresponding class conditional distributions will be aligned across the
two domains. We propose co-regularized domain alignment for unsupervised domain
adaptation, which constructs multiple diverse feature spaces and aligns source
and target distributions in each of them individually, while encouraging that
alignments agree with each other with regard to the class predictions on the
unlabeled target examples. The proposed method is generic and can be used to
improve any domain adaptation method which uses domain alignment. We
instantiate it in the context of a recent state-of-the-art method and observe
that it provides significant performance improvements on several domain
adaptation benchmarks.
Argumentation for Explainable Scheduling (Full Paper with Proofs)
Authors: Kristijonas Čyras, Dimitrios Letsios, Ruth Misener, Francesca Toni
Date: 2018-11-13 00:00:00
Tasks: Abstract Argumentation, Scheduling

Mathematical optimization offers highly-effective tools for finding solutions
for problems with well-defined goals, notably scheduling. However, optimization
solvers are often unexplainable black boxes whose solutions are inaccessible to
users and which users cannot interact with. We define a novel paradigm using
argumentation to empower the interaction between optimization solvers and
users, supported by tractable explanations which certify or refute solutions. A
solution can be from a solver or of interest to a user (in the context of
'what-if' scenarios). Specifically, we define argumentative and natural
language explanations for why a schedule is (not) feasible, (not) efficient or
(not) satisfying fixed user decisions, based on models of the fundamental
makespan scheduling problem in terms of abstract argumentation frameworks
(AFs). We define three types of AFs, whose stable extensions are in one-to-one
correspondence with schedules that are feasible, efficient and satisfying fixed
decisions, respectively. We extract the argumentative explanations from these
AFs and the natural language explanations from the argumentative ones.
Deep Object-Centric Policies for Autonomous Driving
Authors: Dequan Wang, Coline Devin, Qi-Zhi Cai, Fisher Yu, Trevor Darrell
Date: 2018-11-13 00:00:00
Tasks: Autonomous Driving, Object

While learning visuomotor skills in an end-to-end manner is appealing, deep
neural networks are often uninterpretable and fail in surprising ways. For
robotics tasks, such as autonomous driving, models that explicitly represent
objects may be more robust to new scenes and provide intuitive visualizations.
We describe a taxonomy of "object-centric" models which leverage both object
instances and end-to-end learning. In the Grand Theft Auto V simulator, we show
that object-centric models outperform object-agnostic methods in scenes with
other vehicles and pedestrians, even with an imperfect detector. We also
demonstrate that our architectures perform well on real-world environments by
evaluating on the Berkeley DeepDrive Video dataset, where an object-centric
model outperforms object-agnostic models in the low-data regimes.
ABox Abduction via Forgetting in ALC (Long Version)
Authors: Warren Del-Pinto, Renate A. Schmidt
Date: 2018-11-13 00:00:00

Abductive reasoning generates explanatory hypotheses for new observations
using prior knowledge. This paper investigates the use of forgetting, also
known as uniform interpolation, to perform ABox abduction in description logic
(ALC) ontologies. Non-abducibles are specified by a forgetting signature which
can contain concept, but not role, symbols. The resulting hypotheses are
semantically minimal and each consist of a set of disjuncts. These disjuncts
are each independent explanations, and are not redundant with respect to the
background ontology or the other disjuncts, representing a form of hypothesis
space. The observations and hypotheses handled by the method can contain both
atomic or complex ALC concepts, excluding role assertions, and are not
restricted to Horn clauses. Two approaches to redundancy elimination are
explored for practical use: full and approximate. Using a prototype
implementation, experiments were performed over a corpus of real world
ontologies to investigate the practicality of both approaches across several
settings.
Fast Human Pose Estimation
Authors: Feng Zhang, Xiatian Zhu, Mao Ye
Date: 2018-11-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Pose Estimation

Existing human pose estimation approaches often only consider how to improve
the model generalisation performance, but putting aside the significant
efficiency problem. This leads to the development of heavy models with poor
scalability and cost-effectiveness in practical use. In this work, we
investigate the under-studied but practically critical pose model efficiency
problem. To this end, we present a new Fast Pose Distillation (FPD) model
learning strategy. Specifically, the FPD trains a lightweight pose neural
network architecture capable of executing rapidly with low computational cost.
It is achieved by effectively transferring the pose structure knowledge of a
strong teacher network. Extensive evaluations demonstrate the advantages of our
FPD method over a broad range of state-of-the-art pose estimation approaches in
terms of model cost-effectiveness on two standard benchmark datasets, MPII
Human Pose and Leeds Sports Pose.
Home Activity Monitoring using Low Resolution Infrared Sensor
Authors: Lili Tao, Timothy Volonakis, Bo Tan, Yanguo Jing, Kevin Chetty, Melvyn Smith
Date: 2018-11-13 00:00:00
Tasks: Home Activity Monitoring, Specificity

Action monitoring in a home environment provides important information for
health monitoring and may serve as input into a smart home environment. Visual
analysis using cameras can recognise actions in a complex scene, such as
someones living room. However, although there the huge potential benefits and
importance, specifically for health, cameras are not widely accepted because of
privacy concerns. This paper recognises human activities using a sensor that
retains privacy. The sensor is not only different by being thermal, but it is
also of low resolution: 8x8 pixels. The combination of the thermal imaging, and
the low spatial resolution ensures the privacy of individuals. We present an
approach to recognise daily activities using this sensor based on a discrete
cosine transform. We evaluate the proposed method on a state-of-the-art dataset
and experimentally confirm that our approach outperforms the baseline method.
We also introduce a new dataset, and evaluate the method on it. Here we show
that the sensor is considered better at detecting the occurrence of falls and
Activities of Daily Living. Our method achieves an overall accuracy of 87.50%
across 7 activities with a fall detection sensitivity of 100% and specificity
of 99.21%.
Multi-task learning for Joint Language Understanding and Dialogue State Tracking
Authors: Abhinav Rastogi, Raghav Gupta, Dilek Hakkani-Tur
Date: 2018-11-13 00:00:00
Proceeding: WS 2018 7
Tasks: Dialogue State Tracking, Multi-Task Learning, Task-Oriented Dialogue Systems

This paper presents a novel approach for multi-task learning of language
understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue
systems. Multi-task training enables the sharing of the neural network layers
responsible for encoding the user utterance for both LU and DST and improves
performance while reducing the number of network parameters. In our proposed
framework, DST operates on a set of candidate values for each slot that has
been mentioned so far. These candidate sets are generated using LU slot
annotations for the current user utterance, dialogue acts corresponding to the
preceding system utterance and the dialogue state estimated for the previous
turn, enabling DST to handle slots with a large or unbounded set of possible
values and deal with slot values not seen during training. Furthermore, to
bridge the gap between training and inference, we investigate the use of
scheduled sampling on LU output for the current user utterance as well as the
DST output for the preceding turn.
Embedding Electronic Health Records for Clinical Information Retrieval
Authors: Xing Wei, Carsten Eickhoff
Date: 2018-11-13 00:00:00
Tasks: Information Retrieval, Representation Learning, Retrieval

Neural network representation learning frameworks have recently shown to be
highly effective at a wide range of tasks ranging from radiography
interpretation via data-driven diagnostics to clinical decision support. This
often superior performance comes at the price of dramatically increased
training data requirements that cannot be satisfied in every given institution
or scenario. As a means of countering such data sparsity effects, distant
supervision alleviates the need for scarce in-domain data by relying on a
related, resource-rich, task for training.
  This study presents an end-to-end neural clinical decision support system
that recommends relevant literature for individual patients (few available
resources) via distant supervision on the well-known MIMIC-III collection
(abundant resource). Our experiments show significant improvements in retrieval
effectiveness over traditional statistical as well as purely locally supervised
retrieval models.
Hallucinating Point Cloud into 3D Sculptural Object
Authors: Chun-Liang Li, Eunsu Kang, Songwei Ge, Lingyao Zhang, Austin Dill, Manzil Zaheer, Barnabas Poczos
Date: 2018-11-13 00:00:00
Tasks: Object

Our team of artists and machine learning researchers designed a creative
algorithm that can generate authentic sculptural artworks. These artworks do
not mimic any given forms and cannot be easily categorized into the dataset
categories. Our approach extends DeepDream from images to 3D point clouds. The
proposed algorithm, Amalgamated DeepDream (ADD), leverages the properties of
point clouds to create objects with better quality than the naive extension.
ADD presents promise for the creativity of machines, the kind of creativity
that pushes artists to explore novel methods or materials and to create new
genres instead of creating variations of existing forms or styles within one
genre. For example, from Realism to Abstract Expressionism, or to Minimalism.
Lastly, we present the sculptures that are 3D printed based on the point clouds
created by ADD.
Sorting out Lipschitz function approximation
Authors: Cem Anil, James Lucas, Roger Grosse
Date: 2018-11-13 00:00:00
Tasks: Adversarial Robustness, Generalization Bounds

Training neural networks under a strict Lipschitz constraint is useful for provable adversarial robustness, generalization bounds, interpretable gradients, and Wasserstein distance estimation. By the composition property of Lipschitz functions, it suffices to ensure that each individual affine transformation or nonlinear activation is 1-Lipschitz. The challenge is to do this while maintaining the expressive power. We identify a necessary property for such an architecture: each of the layers must preserve the gradient norm during backpropagation. Based on this, we propose to combine a gradient norm preserving activation function, GroupSort, with norm-constrained weight matrices. We show that norm-constrained GroupSort architectures are universal Lipschitz function approximators. Empirically, we show that norm-constrained GroupSort networks achieve tighter estimates of Wasserstein distance than their ReLU counterparts and can achieve provable adversarial robustness guarantees with little cost to accuracy.
Comparison of Feature Extraction Methods and Predictors for Income Inference
Authors: Martin Fixman, Martin Minnoni, Carlos Sarraute
Date: 2018-11-13 00:00:00
Tasks: BIG-bench Machine Learning

Patterns of mobile phone communications, coupled with the information of the
social network graph and financial behavior, allow us to make inferences of
users' socio-economic attributes such as their income level. We present here
several methods to extract features from mobile phone usage (calls and
messages), and compare different combinations of supervised machine learning
techniques and sets of features used as input for the inference of users'
income. Our experimental results show that the Bayesian method based on the
communication graph outperforms standard machine learning algorithms using
node-based features.
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
Authors: Abhishek Divekar, Meet Parekh, Vaibhav Savla, Rudra Mishra, Mahesh Shirole
Date: 2018-11-13 00:00:00
Tasks: Benchmarking, Intrusion Detection, Network Intrusion Detection

Machine Learning has been steadily gaining traction for its use in
Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this
domain is frequently performed using the KDD~CUP~99 dataset as a benchmark.
Several studies question its usability while constructing a contemporary NIDS,
due to the skewed response distribution, non-stationarity, and failure to
incorporate modern attacks. In this paper, we compare the performance for
KDD-99 alternatives when trained using classification models commonly found in
literature: Neural Network, Support Vector Machine, Decision Tree, Random
Forest, Naive Bayes and K-Means. Applying the SMOTE oversampling technique and
random undersampling, we create a balanced version of NSL-KDD and prove that
skewed target classes in KDD-99 and NSL-KDD hamper the efficacy of classifiers
on minority classes (U2R and R2L), leading to possible security risks. We
explore UNSW-NB15, a modern substitute to KDD-99 with greater uniformity of
pattern distribution. We benchmark this dataset before and after SMOTE
oversampling to observe the effect on minority performance. Our results
indicate that classifiers trained on UNSW-NB15 match or better the Weighted
F1-Score of those trained on NSL-KDD and KDD-99 in the binary case, thus
advocating UNSW-NB15 as a modern substitute to these datasets.
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
Authors: Aditya Siddhant, Anuj Goyal, Angeliki Metallinou
Date: 2018-11-13 00:00:00
Tasks: Language Modeling, Language Modelling, Spoken Language Understanding, Transfer Learning, Unsupervised Pre-training

User interaction with voice-powered agents generates large amounts of
unlabeled utterances. In this paper, we explore techniques to efficiently
transfer the knowledge from these unlabeled utterances to improve model
performance on Spoken Language Understanding (SLU) tasks. We use Embeddings
from Language Model (ELMo) to take advantage of unlabeled data by learning
contextualized word representations. Additionally, we propose ELMo-Light
(ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our
findings suggest unsupervised pre-training on a large corpora of unlabeled
utterances leads to significantly better SLU performance compared to training
from scratch and it can even outperform conventional supervised transfer.
Additionally, we show that the gains from unsupervised transfer techniques can
be further improved by supervised transfer. The improvements are more
pronounced in low resource settings and when using only 1000 labeled in-domain
samples, our techniques match the performance of training from scratch on
10-15x more labeled in-domain data.
Iteratively Training Look-Up Tables for Network Quantization
Authors: Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama, Javier Alonso García, Stephen Tiedemann, Thomas Kemp, Akira Nakamura
Date: 2018-11-13 00:00:00
Tasks: object-detection, Object Detection, Quantization

Operating deep neural networks on devices with limited resources requires the
reduction of their memory footprints and computational requirements. In this
paper we introduce a training method, called look-up table quantization, LUT-Q,
which learns a dictionary and assigns each weight to one of the dictionary's
values. We show that this method is very flexible and that many other
techniques can be seen as special cases of LUT-Q. For example, we can constrain
the dictionary trained with LUT-Q to generate networks with pruned weight
matrices or restrict the dictionary to powers-of-two to avoid the need for
multiplications. In order to obtain fully multiplier-less networks, we also
introduce a multiplier-less version of batch normalization. Extensive
experiments on image recognition and object detection tasks show that LUT-Q
consistently achieves better performance than other methods with the same
quantization bitwidth.
Detect or Track: Towards Cost-Effective Video Object Detection/Tracking
Authors: Hao Luo, Wenxuan Xie, Xinggang Wang, Wen-Jun Zeng
Date: 2018-11-13 00:00:00
Tasks: Object, object-detection, Object Detection, Video Object Detection

State-of-the-art object detectors and trackers are developing fast. Trackers
are in general more efficient than detectors but bear the risk of drifting. A
question is hence raised -- how to improve the accuracy of video object
detection/tracking by utilizing the existing detectors and trackers within a
given time budget? A baseline is frame skipping -- detecting every N-th frames
and tracking for the frames in between. This baseline, however, is suboptimal
since the detection frequency should depend on the tracking quality. To this
end, we propose a scheduler network, which determines to detect or track at a
certain frame, as a generalization of Siamese trackers. Although being
light-weight and simple in structure, the scheduler network is more effective
than the frame skipping baselines and flow-based approaches, as validated on
ImageNet VID dataset in video object detection/tracking.
Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-cameras
Authors: Qingwen Xu, Arturo Gomez Chavez, Heiko Bülow, Andreas Birk, Sören Schwertfeger
Date: 2018-11-13 00:00:00
Tasks: Image Registration, Pose Estimation, Robot Pose Estimation

Spectral methods such as the improved Fourier Mellin Invariant (iFMI) transform have proved faster, more robust and accurate than feature based methods on image registration. However, iFMI is restricted to work only when the camera moves in 2D space and has not been applied on omni-cameras images so far. In this work, we extend the iFMI method and apply a motion model to estimate an omni-camera's pose when it moves in 3D space. This is particularly useful in field robotics applications to get a rapid and comprehensive view of unstructured environments, and to estimate robustly the robot pose. In the experiment section, we compared the extended iFMI method against ORB and AKAZE feature based approaches on three datasets showing different type of environments: office, lawn and urban scenery (MPI-omni dataset). The results show that our method boosts the accuracy of the robot pose estimation two to four times with respect to the feature registration techniques, while offering lower processing times. Furthermore, the iFMI approach presents the best performance against motion blur typically present in mobile robotics.
Self-Supervised Learning of Depth and Camera Motion from 360° Videos
Authors: Fu-En Wang, Hou-Ning Hu, Hsien-Tzu Cheng, Juan-Ting Lin, Shang-Ta Yang, Meng-Li Shih, Hung-Kuo Chu, Min Sun
Date: 2018-11-13 00:00:00
Tasks: Depth And Camera Motion, Depth Estimation, Depth Prediction, Motion Estimation, Self-Driving Cars, Self-Supervised Learning

As 360{\deg} cameras become prevalent in many autonomous systems (e.g.,
self-driving cars and drones), efficient 360{\deg} perception becomes more and
more important. We propose a novel self-supervised learning approach for
predicting the omnidirectional depth and camera motion from a 360{\deg} video.
In particular, starting from the SfMLearner, which is designed for cameras with
normal field-of-view, we introduce three key features to process 360{\deg}
images efficiently. Firstly, we convert each image from equirectangular
projection to cubic projection in order to avoid image distortion. In each
network layer, we use Cube Padding (CP), which pads intermediate features from
adjacent faces, to avoid image boundaries. Secondly, we propose a novel
"spherical" photometric consistency constraint on the whole viewing sphere. In
this way, no pixel will be projected outside the image boundary which typically
happens in images with normal field-of-view. Finally, rather than naively
estimating six independent camera motions (i.e., naively applying SfM-Learner
to each face on a cube), we propose a novel camera pose consistency loss to
ensure the estimated camera motions reaching consensus. To train and evaluate
our approach, we collect a new PanoSUNCG dataset containing a large amount of
360{\deg} videos with groundtruth depth and camera motion. Our approach
achieves state-of-the-art depth prediction and camera motion estimation on
PanoSUNCG with faster inference speed comparing to equirectangular. In
real-world indoor videos, our approach can also achieve qualitatively
reasonable depth prediction by acquiring model pre-trained on PanoSUNCG.
Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters
Authors: Denis Lukovnikov, Nilesh Chakraborty, Jens Lehmann, Asja Fischer
Date: 2018-11-13 00:00:00
Tasks: Question Answering, Semantic Parsing, Table-based Question Answering

Translating natural language to SQL queries for table-based question
answering is a challenging problem and has received significant attention from
the research community. In this work, we extend a pointer-generator and
investigate the order-matters problem in semantic parsing for SQL. Even though
our model is a straightforward extension of a general-purpose
pointer-generator, it outperforms early works for WikiSQL and remains
competitive to concurrently introduced, more complex models. Moreover, we
provide a deeper investigation of the potential order-matters problem that
could arise due to having multiple correct decoding paths, and investigate the
use of REINFORCE as well as a dynamic oracle in this context.
Genetic algorithm for optimal distribution in cities
Authors: Esteban Quintero, Mateo Sanchez, Nicolas Roldan, Mauricio Toro
Date: 2018-11-13 00:00:00

The problem to deal with in this project is the problem of routing electric
vehicles, which consists of finding the best routes for this type of vehicle,
so that they reach their destination, without running out of power and
optimizing to the maximum transportation costs. The importance of this problem
is mainly in the sector of shipments in the recent future, when obsolete energy
sources are replaced with renewable sources, where each vehicle contains a
number of packages that must be delivered at specific points in the city , but,
being electric, they do not have an optimal battery life, so having the ideal
routes traced is a vital aspect for the proper functioning of these. Now days
you can see applications of this problem in the cleaning sector, specifically
with the trucks responsible for collecting garbage, which aims to travel the
entire city in the most efficient way, without letting excessive garbage
accumulate.