pwc-archive/papers-with-abstracts huggingface dataset:
Back to square one: probabilistic trajectory forecasting without bells and whistles
Authors: Ehsan Pajouheshgar, Christoph H. Lampert
Date: 2018-12-07 00:00:00
Tasks: Relation, Trajectory Forecasting

We introduce a spatio-temporal convolutional neural network model for
trajectory forecasting from visual sources. Applied in an auto-regressive way
it provides an explicit probability distribution over continuations of a given
initial trajectory segment. We discuss it in relation to (more complicated)
existing work and report on experiments on two standard datasets for trajectory
forecasting: MNISTseq and Stanford Drones, achieving results on-par with or
better than previous methods.
ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search
Authors: Kevin Alexander Laube, Andreas Zell
Date: 2018-12-07 00:00:00
Tasks: General Classification, Neural Architecture Search

Neural network architectures found by sophistic search algorithms achieve
strikingly good test performance, surpassing most human-crafted network models
by significant margins. Although computationally efficient, their design is
often very complex, impairing execution speed. Additionally, finding models
outside of the search space is not possible by design. While our space is still
limited, we implement undiscoverable expert knowledge into the economic search
algorithm Efficient Neural Architecture Search (ENAS), guided by the design
principles and architecture of ShuffleNet V2. While maintaining baseline-like
2.85% test error on CIFAR-10, our ShuffleNASNets are significantly less
complex, require fewer parameters, and are two times faster than the ENAS
baseline in a classification task. These models also scale well to a low
parameter space, achieving less than 5% test error with little regularization
and only 236K parameters.
Improving Retrieval-Based Question Answering with Deep Inference Models
Authors: George-Sebastian Pirtoaca, Traian Rebedea, Stefan Ruseti
Date: 2018-12-07 00:00:00
Tasks: Information Retrieval, Natural Language Inference, Question Answering, Retrieval

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multi-choice question answering in the science domain. For each question-answer pair, we use standard retrieval-based models to find relevant candidate contexts and decompose the main problem into two different sub-problems. First, assign correctness scores for each candidate answer based on the context using retrieval models from Lucene. Second, we use deep learning architectures to compute if a candidate answer can be inferred from some well-chosen context consisting of sentences retrieved from the knowledge base. In the end, all these solvers are combined using a simple neural network to predict the correct answer. This proposed two-step model outperforms the best retrieval-based solver by over 3% in absolute accuracy.
Scale-aware multi-level guidance for interactive instance segmentation
Authors: Soumajit Majumder, Angela Yao
Date: 2018-12-07 00:00:00
Tasks: Instance Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

In interactive instance segmentation, users give feedback to iteratively
refine segmentation masks. The user-provided clicks are transformed into
guidance maps which provide the network with necessary cues on the whereabouts
of the object of interest. Guidance maps used in current systems are purely
distance-based and are either too localized or non-informative. We propose a
novel transformation of user clicks to generate scale-aware guidance maps that
leverage the hierarchical structural information present in an image. Using our
guidance maps, even the most basic FCNs are able to outperform existing
approaches that require state-of-the-art segmentation networks pre-trained on
large scale segmentation datasets. We demonstrate the effectiveness of our
proposed transformation strategy through comprehensive experimentation in which
we significantly raise state-of-the-art on four standard interactive
segmentation benchmarks.
Online Learning and Decision-Making under Generalized Linear Model with High-Dimensional Data
Authors: Xue Wang, Mike Mingcheng Wei, Tao Yao
Date: 2018-12-07 00:00:00
Tasks: Decision Making

We propose a minimax concave penalized multi-armed bandit algorithm under
generalized linear model (G-MCP-Bandit) for a decision-maker facing
high-dimensional data in an online learning and decision-making process. We
demonstrate that the G-MCP-Bandit algorithm asymptotically achieves the optimal
cumulative regret in the sample size dimension T , O(log T), and further
attains a tight bound in the covariate dimension d, O(log d). In addition, we
develop a linear approximation method, the 2-step weighted Lasso procedure, to
identify the MCP estimator for the G-MCP-Bandit algorithm under non-iid
samples. Under this procedure, the MCP estimator matches the oracle estimator
with high probability and converges to the true parameters with the optimal
convergence rate. Finally, through experiments based on synthetic data and two
real datasets (warfarin dosing dataset and Tencent search advertising dataset),
we show that the G-MCP-Bandit algorithm outperforms other benchmark algorithms,
especially when there is a high level of data sparsity or the decision set is
large.
LNEMLC: Label Network Embeddings for Multi-Label Classification
Authors: Piotr Szymański, Tomasz Kajdanowicz, Nitesh Chawla
Date: 2018-12-07 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Network Embedding

Multi-label classification aims to classify instances with discrete
non-exclusive labels. Most approaches on multi-label classification focus on
effective adaptation or transformation of existing binary and multi-class
learning approaches but fail in modelling the joint probability of labels or do
not preserve generalization abilities for unseen label combinations. To address
these issues we propose a new multi-label classification scheme, LNEMLC - Label
Network Embedding for Multi-Label Classification, that embeds the label network
and uses it to extend input space in learning and inference of any base
multi-label classifier. The approach allows capturing of labels' joint
probability at low computational complexity providing results comparable to the
best methods reported in the literature. We demonstrate how the method reveals
statistically significant improvements over the simple kNN baseline classifier.
We also provide hints for selecting the robust configuration that works
satisfactorily across data domains.
Building Ethics into Artificial Intelligence
Authors: Han Yu, Zhiqi Shen, Chunyan Miao, Cyril Leung, Victor R. Lesser, Qiang Yang
Date: 2018-12-07 00:00:00
Tasks: Decision Making, Ethics

As artificial intelligence (AI) systems become increasingly ubiquitous, the
topic of AI governance for ethical decision-making by AI has captured public
imagination. Within the AI research community, this topic remains less familiar
to many researchers. In this paper, we complement existing surveys, which
largely focused on the psychological, social and legal discussions of the
topic, with an analysis of recent advances in technical solutions for AI
governance. By reviewing publications in leading AI conferences including AAAI,
AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four
areas: 1) exploring ethical dilemmas; 2) individual ethical decision
frameworks; 3) collective ethical decision frameworks; and 4) ethics in
human-AI interactions. We highlight the intuitions and key techniques used in
each approach, and discuss promising future research directions towards
successful integration of ethical AI systems into human societies.
From Fair Decision Making to Social Equality
Authors: Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
Date: 2018-12-07 00:00:00
Tasks: Decision Making, Fairness

The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality between the qualifications of the groups. How can we incorporate influence dynamics in decision making? How well do dynamics-oblivious fairness policies fare in terms of reaching equality? In this paper, we propose a simple yet revealing model that encompasses (1) a selection process where an institution chooses from multiple groups according to their qualifications so as to maximize an institutional utility and (2) dynamics that govern the evolution of the groups' qualifications according to the imposed policies. We focus on demographic parity as the formalism of affirmative action. We then give conditions under which an unconstrained policy reaches equality on its own. In this case, surprisingly, imposing demographic parity may break equality. When it doesn't, one would expect the additional constraint to reduce utility, however, we show that utility may in fact increase. In more realistic scenarios, unconstrained policies do not lead to equality. In such cases, we show that although imposing demographic parity may remedy it, there is a danger that groups settle at a worse set of qualifications. As a silver lining, we also identify when the constraint not only leads to equality, but also improves all groups. This gives quantifiable insight into both sides of the mismatch hypothesis. These cases and trade-offs are instrumental in determining when and how imposing demographic parity can be beneficial in selection processes, both for the institution and for society on the long run.
A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control
Authors: Dario Izzo, Marcus Märtens, Binfeng Pan
Date: 2018-12-07 00:00:00
Tasks: Reinforcement Learning

The rapid developments of Artificial Intelligence in the last decade are
influencing Aerospace Engineering to a great extent and research in this
context is proliferating. We share our observations on the recent developments
in the area of Spacecraft Guidance Dynamics and Control, giving selected
examples on success stories that have been motivated by mission designs. Our
focus is on evolutionary optimisation, tree searches and machine learning,
including deep learning and reinforcement learning as the key technologies and
drivers for current and future research in the field. From a high-level
perspective, we survey various scenarios for which these approaches have been
successfully applied or are under strong scientific investigation. Whenever
possible, we highlight the relations and synergies that can be obtained by
combining different techniques and projects towards future domains for which
newly emerging artificial intelligence techniques are expected to become game
changers.
On Marginally Correct Approximations of Dempster-Shafer Belief Functions from Data
Authors: Mieczysław A. Kłopotek, Sławomir T. Wierzchoń
Date: 2018-12-07 00:00:00

Mathematical Theory of Evidence (MTE), a foundation for reasoning under
partial ignorance, is blamed to leave frequencies outside (or aside of) its
framework. The seriousness of this accusation is obvious: no experiment may be
run to compare the performance of MTE-based models of real world processes
against real world data.
  In this paper we consider this problem from the point of view of conditioning
in the MTE. We describe the class of belief functions for which marginal
consistency with observed frequencies may be achieved and conditional belief
functions are proper belief functions,%\ and deal with implications for
(marginal) approximation of general belief functions by this class of belief
functions and for inference models in MTE.
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation
Authors: Idoia Ruiz, Bogdan Raducanu, Rakesh Mehta, Jaume Amores
Date: 2018-12-07 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification, Knowledge Distillation, Metric Learning, Person Re-Identification

Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time response, it is crucial to find the balance between accuracy and speed. We analyse this trade-off, comparing a classical method, that comprises hand-crafted feature description and metric learning, in particular, LOMO and XQDA, to deep learning based techniques, using image classification networks, ResNet and MobileNets. Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time. We evaluate both methods on the Market-1501 and DukeMTMC-reID large-scale datasets, showing that distillation helps reducing the computational cost at inference time while even increasing the accuracy performance.
Local Distribution in Neighborhood for Classification
Authors: Chengsheng Mao, Bin Hu, Lei Chen, Philip Moore, Xiaowei Zhang
Date: 2018-12-07 00:00:00
Tasks: Classification, General Classification

The k-nearest-neighbor method performs classification tasks for a query
sample based on the information contained in its neighborhood. Previous studies
into the k-nearest-neighbor algorithm usually achieved the decision value for a
class by combining the support of each sample in the neighborhood. They have
generally considered the nearest neighbors separately, and potentially integral
neighborhood information important for classification was lost, e.g. the
distribution information. This article proposes a novel local learning method
that organizes the information in the neighborhood through local distribution.
In the proposed method, additional distribution information in the neighborhood
is estimated and then organized; the classification decision is made based on
maximum posterior probability which is estimated from the local distribution in
the neighborhood. Additionally, based on the local distribution, we generate a
generalized local classification form that can be effectively applied to
various datasets through tuning the parameters. We use both synthetic and real
datasets to evaluate the classification performance of the proposed method; the
experimental results demonstrate the dimensional scalability, efficiency,
effectiveness and robustness of the proposed method compared to some other
state-of-the-art classifiers. The results indicate that the proposed method is
effective and promising in a broad range of domains.
SeFM: A Sequential Feature Point Matching Algorithm for Object 3D Reconstruction
Authors: Zhihao Fang, He Ma, Xuemin Zhu, Xutao Guo, Ruixin Zhou
Date: 2018-12-07 00:00:00
Tasks: 3D Reconstruction, Object Reconstruction

3D reconstruction is a fundamental issue in many applications and the feature point matching problem is a key step while reconstructing target objects. Conventional algorithms can only find a small number of feature points from two images which is quite insufficient for reconstruction. To overcome this problem, we propose SeFM a sequential feature point matching algorithm. We first utilize the epipolar geometry to find the epipole of each image. Rotating along the epipole, we generate a set of the epipolar lines and reserve those intersecting with the input image. Next, a rough matching phase, followed by a dense matching phase, is applied to find the matching dot-pairs using dynamic programming. Furthermore, we also remove wrong matching dot-pairs by calculating the validity. Experimental results illustrate that SeFM can achieve around 1,000 to 10,000 times matching dot-pairs, depending on individual image, compared to conventional algorithms and the object reconstruction with only two images is semantically visible. Moreover, it outperforms conventional algorithms, such as SIFT and SURF, regarding precision and recall.
When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method
Authors: Xiu Yang, Xueyu Zhu, Jing Li
Date: 2018-12-07 00:00:00

In this work, we propose a framework that combines the
approximation-theory-based multifidelity method and
Gaussian-process-regression-based multifidelity method to achieve data-model
convergence when stochastic simulation models and sparse accurate observation
data are available. Specifically, the two types of multifidelity methods we use
are the bifidelity and CoKriging methods. The new approach uses the bifidelity
method to efficiently estimate the empirical mean and covariance of the
stochastic simulation outputs, then it uses these statistics to construct a
Gaussian process (GP) representing low-fidelity in CoKriging. We also combine
the bifidelity method with Kriging, where the approximated empirical statistics
are used to construct the GP as well. We prove that the resulting posterior
mean by the new physics-informed approach preserves linear physical constraints
up to an error bound. By using this method, we can obtain an accurate
construction of a state of interest based on a partially correct physical model
and a few accurate observations. We present numerical examples to demonstrate
performance of the method.
Intent Detection for code-mix utterances in task oriented dialogue systems
Authors: Pratik Jayarao, Aman Srivastava
Date: 2018-12-07 00:00:00
Tasks: Intent Detection, Task-Oriented Dialogue Systems

Intent detection is an essential component of task oriented dialogue systems.
Over the years, extensive research has been conducted resulting in many state
of the art models directed towards resolving user's intents in dialogue. A
variety of vector representations foruser utterances have been explored for the
same. However, these models and vectorization approaches have more so been
evaluated in a single language environment. Dialogude systems generally have to
deal with queries in different languages. We thus conduct experiments across
combinations of models and various vectors representations for Code Mix as well
as multi language utterances and evaluate how these models scale to a multi
language environment. Our aim is to find the best suitable combination of
vector representation and models for the process of intent detection for Code
Mix utterances. we have evaluated the experiments on two different datasets
consisting of only Code Mix utterances and the other dataset consisting of
English, Hindi and Code Mix English Hindi utterances.
Applied Federated Learning: Improving Google Keyboard Query Suggestions
Authors: Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, Françoise Beaufays
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning

Federated learning is a distributed form of machine learning where both the
training data and model training are decentralized. In this paper, we use
federated learning in a commercial, global-scale setting to train, evaluate and
deploy a model to improve virtual keyboard search suggestion quality without
direct access to the underlying user data. We describe our observations in
federated training, compare metrics to live deployments, and present resulting
quality increases. In whole, we demonstrate how federated learning can be
applied end-to-end to both improve user experiences and enhance user privacy.
Off-Policy Deep Reinforcement Learning without Exploration
Authors: Scott Fujimoto, David Meger, Doina Precup
Date: 2018-12-07 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for data collection. In this paper, we demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are incapable of learning with data uncorrelated to the distribution under the current policy, making them ineffective for this fixed batch setting. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space in order to force the agent towards behaving close to on-policy with respect to a subset of the given data. We present the first continuous control deep reinforcement learning algorithm which can learn effectively from arbitrary, fixed batch data, and empirically demonstrate the quality of its behavior in several tasks.
Deep Energies for Estimating Three-Dimensional Facial Pose and Expression
Authors: Michael Bao, Jane Wu, Xinwei Yao, Ronald Fedkiw
Date: 2018-12-07 00:00:00

While much progress has been made in capturing high-quality facial
performances using motion capture markers and shape-from-shading, high-end
systems typically also rely on rotoscope curves hand-drawn on the image. These
curves are subjective and difficult to draw consistently; moreover, ad-hoc
procedural methods are required for generating matching rotoscope curves on
synthetic renders embedded in the optimization used to determine
three-dimensional facial pose and expression. We propose an alternative
approach whereby these curves and other keypoints are detected automatically on
both the image and the synthetic renders using trained neural networks,
eliminating artist subjectivity and the ad-hoc procedures meant to mimic it.
More generally, we propose using machine learning networks to implicitly define
deep energies which when minimized using classical optimization techniques lead
to three-dimensional facial pose and expression estimation.
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
Authors: Yapeng Tian, Yulun Zhang, Yun Fu, Chenliang Xu
Date: 2018-12-07 00:00:00
Tasks: Optical Flow Estimation, Super-Resolution, Video Super-Resolution

Video super-resolution (VSR) aims to restore a photo-realistic
high-resolution (HR) video frame from both its corresponding low-resolution
(LR) frame (reference frame) and multiple neighboring frames (supporting
frames). Due to varying motion of cameras or objects, the reference frame and
each support frame are not aligned. Therefore, temporal alignment is a
challenging yet important problem for VSR. Previous VSR methods usually utilize
optical flow between the reference frame and each supporting frame to wrap the
supporting frame for temporal alignment. Therefore, the performance of these
image-level wrapping-based models will highly depend on the prediction accuracy
of optical flow, and inaccurate optical flow will lead to artifacts in the
wrapped supporting frames, which also will be propagated into the reconstructed
HR video frame. To overcome the limitation, in this paper, we propose a
temporal deformable alignment network (TDAN) to adaptively align the reference
frame and each supporting frame at the feature level without computing optical
flow. The TDAN uses features from both the reference frame and each supporting
frame to dynamically predict offsets of sampling convolution kernels. By using
the corresponding kernels, TDAN transforms supporting frames to align with the
reference frame. To predict the HR video frame, a reconstruction network taking
aligned frames and the reference frame is utilized. Experimental results
demonstrate the effectiveness of the proposed TDAN-based VSR model.