pwc-archive/papers-with-abstracts huggingface dataset:
Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC
Authors: Shannon Egan, Wojciech Fedorko, Alison Lister, Jannicke Pearkes, Colin Gay
Date: 2017-11-24 00:00:00

Multivariate techniques based on engineered features have found wide adoption
in the identification of jets resulting from hadronic top decays at the Large
Hadron Collider (LHC). Recent Deep Learning developments in this area include
the treatment of the calorimeter activation as an image or supplying a list of
jet constituent momenta to a fully connected network. This latter approach
lends itself well to the use of Recurrent Neural Networks. In this work the
applicability of architectures incorporating Long Short-Term Memory (LSTM)
networks is explored. Several network architectures, methods of ordering of jet
constituents, and input pre-processing are studied. The best performing LSTM
network achieves a background rejection of 100 for 50% signal efficiency. This
represents more than a factor of two improvement over a fully connected Deep
Neural Network (DNN) trained on similar types of inputs.
Cooperative Multi-Agent Planning: A Survey
Authors: Alejandro Torreño, Eva Onaindia, Antonín Komenda, Michal Štolba
Date: 2017-11-24 00:00:00
Tasks: Survey

Cooperative multi-agent planning (MAP) is a relatively recent research field
that combines technologies, algorithms and techniques developed by the
Artificial Intelligence Planning and Multi-Agent Systems communities. While
planning has been generally treated as a single-agent task, MAP generalizes
this concept by considering multiple intelligent agents that work cooperatively
to develop a course of action that satisfies the goals of the group.
  This paper reviews the most relevant approaches to MAP, putting the focus on
the solvers that took part in the 2015 Competition of Distributed and
Multi-Agent Planning, and classifies them according to their key features and
relative performance.
Interactive Robot Learning of Gestures, Language and Affordances
Authors: Giovanni Saponaro, Lorenzo Jamone, Alexandre Bernardino, Giampiero Salvi
Date: 2017-11-24 00:00:00

A growing field in robotics and Artificial Intelligence (AI) research is
human-robot collaboration, whose target is to enable effective teamwork between
humans and robots. However, in many situations human teams are still superior
to human-robot teams, primarily because human teams can easily agree on a
common goal with language, and the individual members observe each other
effectively, leveraging their shared motor repertoire and sensorimotor
resources. This paper shows that for cognitive robots it is possible, and
indeed fruitful, to combine knowledge acquired from interacting with elements
of the environment (affordance exploration) with the probabilistic observation
of another agent's actions.
  We propose a model that unites (i) learning robot affordances and word
descriptions with (ii) statistical recognition of human gestures with vision
sensors. We discuss theoretical motivations, possible implementations, and we
show initial results which highlight that, after having acquired knowledge of
its surrounding environment, a humanoid robot can generalize this knowledge to
the case when it observes another agent (human partner) performing the same
motor actions previously executed during training.
Ethical Challenges in Data-Driven Dialogue Systems
Authors: Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, Joelle Pineau
Date: 2017-11-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The use of dialogue systems as a medium for human-machine interaction is an
increasingly prevalent paradigm. A growing number of dialogue systems use
conversation strategies that are learned from large datasets. There are well
documented instances where interactions with these system have resulted in
biased or even offensive conversations due to the data-driven training process.
Here, we highlight potential ethical issues that arise in dialogue systems
research, including: implicit biases in data-driven systems, the rise of
adversarial examples, potential sources of privacy violations, safety concerns,
special considerations for reinforcement learning systems, and reproducibility
concerns. We also suggest areas stemming from these issues that deserve further
investigation. Through this initial survey, we hope to spur research leading to
robust, safe, and ethically sound dialogue systems.
A Compression-Inspired Framework for Macro Discovery
Authors: Francisco M. Garcia, Bruno C. da Silva, Philip S. Thomas
Date: 2017-11-24 00:00:00
Tasks: Efficient Exploration, Reinforcement Learning

In this paper we consider the problem of how a reinforcement learning agent
tasked with solving a set of related Markov decision processes can use
knowledge acquired early in its lifetime to improve its ability to more rapidly
solve novel, but related, tasks. One way of exploiting this experience is by
identifying recurrent patterns in trajectories obtained from well-performing
policies. We propose a three-step framework in which an agent 1) generates a
set of candidate open-loop macros by compressing trajectories drawn from
near-optimal policies; 2) evaluates the value of each macro; and 3) selects a
maximally diverse subset of macros that spans the space of policies typically
required for solving the set of related tasks. Our experiments show that
extending the original primitive action-set of the agent with the identified
macros allows it to more rapidly learn an optimal policy in unseen, but similar
MDPs.
Learning User Preferences to Incentivize Exploration in the Sharing Economy
Authors: Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause
Date: 2017-11-17 00:00:00

We study platforms in the sharing economy and discuss the need for
incentivizing users to explore options that otherwise would not be chosen. For
instance, rental platforms such as Airbnb typically rely on customer reviews to
provide users with relevant information about different options. Yet, often a
large fraction of options does not have any reviews available. Such options are
frequently neglected as viable choices, and in turn are unlikely to be
evaluated, creating a vicious cycle. Platforms can engage users to deviate from
their preferred choice by offering monetary incentives for choosing a different
option instead. To efficiently learn the optimal incentives to offer, we
consider structural information in user preferences and introduce a novel
algorithm - Coordinated Online Learning (CoOL) - for learning with structural
information modeled as convex constraints. We provide formal guarantees on the
performance of our algorithm and test the viability of our approach in a user
study with data of apartments on Airbnb. Our findings suggest that our approach
is well-suited to learn appropriate incentives and increase exploration on the
investigated platform.
A survey of exemplar-based texture synthesis
Authors: Lara Raad, Axel Davy, Agnès Desolneux, Jean-Michel Morel
Date: 2017-07-22 00:00:00
Tasks: Survey, Texture Synthesis

Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statistical signature; then, a random sampling conditioned
to this signature produces genuinely different texture images. The second class
boils down to a clever "copy-paste" procedure, which stitches together large
regions of the sample. Hybrid methods try to combine ideas from both approaches
to avoid their hurdles. The recent approaches using convolutional neural
networks fit to this classification, some being statistical and others
performing patch re-arrangement in the feature space. They produce impressive
synthesis on various kinds of textures. Nevertheless, we found that most real
textures are organized at multiple scales, with global structures revealed at
coarse scales and highly varying details at finer ones. Thus, when confronted
with large natural images of textures the results of state-of-the-art methods
degrade rapidly, and the problem of modeling them remains wide open.
Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty
Authors: Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Autonomous Driving, Trajectory Prediction

Progress towards advanced systems for assisted and autonomous driving is
leveraging recent advances in recognition and segmentation methods. Yet, we are
still facing challenges in bringing reliable driving to inner cities, as those
are composed of highly dynamic scenes observed from a moving platform at
considerable speeds. Anticipation becomes a key element in order to react
timely and prevent accidents. In this paper we argue that it is necessary to
predict at least 1 second and we thus propose a new model that jointly predicts
ego motion and people trajectories over such large time horizons. We pay
particular attention to modeling the uncertainty of our estimates arising from
the non-deterministic nature of natural traffic scenes. Our experimental
results show that it is indeed possible to predict people trajectories at the
desired time horizons and that our uncertainty estimates are informative of the
prediction error. We also show that both sequence modeling of trajectories as
well as our novel method of long term odometry prediction are essential for
best performance.
Inference via low-dimensional couplings
Authors: Alessio Spantini, Daniele Bigoni, Youssef Marzouk
Date: 2017-03-17 00:00:00
Tasks: State Space Models

We investigate the low-dimensional structure of deterministic transformations
between random variables, i.e., transport maps between probability measures. In
the context of statistics and machine learning, these transformations can be
used to couple a tractable "reference" measure (e.g., a standard Gaussian) with
a target measure of interest. Direct simulation from the desired measure can
then be achieved by pushing forward reference samples through the map. Yet
characterizing such a map---e.g., representing and evaluating it---grows
challenging in high dimensions. The central contribution of this paper is to
establish a link between the Markov properties of the target measure and the
existence of low-dimensional couplings, induced by transport maps that are
sparse and/or decomposable. Our analysis not only facilitates the construction
of transformations in high-dimensional settings, but also suggests new
inference methodologies for continuous non-Gaussian graphical models. For
instance, in the context of nonlinear state-space models, we describe new
variational algorithms for filtering, smoothing, and sequential parameter
inference. These algorithms can be understood as the natural
generalization---to the non-Gaussian case---of the square-root
Rauch-Tung-Striebel Gaussian smoother.
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Authors: Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, Jaegul Choo
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Attribute, Image-to-Image Translation, Translation

Recent studies have shown remarkable success in image-to-image translation
for two domains. However, existing approaches have limited scalability and
robustness in handling more than two domains, since different models should be
built independently for every pair of image domains. To address this
limitation, we propose StarGAN, a novel and scalable approach that can perform
image-to-image translations for multiple domains using only a single model.
Such a unified model architecture of StarGAN allows simultaneous training of
multiple datasets with different domains within a single network. This leads to
StarGAN's superior quality of translated images compared to existing models as
well as the novel capability of flexibly translating an input image to any
desired target domain. We empirically demonstrate the effectiveness of our
approach on a facial attribute transfer and a facial expression synthesis
tasks.
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
Authors: Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling
Date: 2017-11-24 00:00:00
Tasks: Gaze Estimation

Learning-based methods are believed to work well for unconstrained gaze
estimation, i.e. gaze estimation from a monocular RGB camera without
assumptions regarding user, environment, or camera. However, current gaze
datasets were collected under laboratory conditions and methods were not
evaluated across multiple datasets. Our work makes three contributions towards
addressing these limitations. First, we present the MPIIGaze that contains
213,659 full face images and corresponding ground-truth gaze positions
collected from 15 users during everyday laptop use over several months. An
experience sampling approach ensured continuous gaze and head poses and
realistic variation in eye appearance and illumination. To facilitate
cross-dataset evaluations, 37,667 images were manually annotated with eye
corners, mouth corners, and pupil centres. Second, we present an extensive
evaluation of state-of-the-art gaze estimation methods on three current
datasets, including MPIIGaze. We study key challenges including target gaze
range, illumination conditions, and facial appearance variation. We show that
image resolution and the use of both eyes affect gaze estimation performance
while head pose and pupil centre information are less informative. Finally, we
propose GazeNet, the first deep appearance-based gaze estimation method.
GazeNet improves the state of the art by 22% percent (from a mean error of 13.9
degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.
Dense 3D Regression for Hand Pose Estimation
Authors: Chengde Wan, Thomas Probst, Luc van Gool, Angela Yao
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation, regression

We present a simple and effective method for 3D hand pose estimation from a
single depth frame. As opposed to previous state-of-the-art methods based on
holistic 3D regression, our method works on dense pixel-wise estimation. This
is achieved by careful design choices in pose parameterization, which leverages
both 2D and 3D properties of depth map. Specifically, we decompose the pose
parameters into a set of per-pixel estimations, i.e., 2D heat maps, 3D heat
maps and unit 3D directional vector fields. The 2D/3D joint heat maps and 3D
joint offsets are estimated via multi-task network cascades, which is trained
end-to-end. The pixel-wise estimations can be directly translated into a vote
casting scheme. A variant of mean shift is then used to aggregate local votes
while enforcing consensus between the the estimated 3D pose and the pixel-wise
2D and 3D estimations by design. Our method is efficient and highly accurate.
On MSRA and NYU hand dataset, our method outperforms all previous
state-of-the-art approaches by a large margin. On the ICVL hand dataset, our
method achieves similar accuracy compared to the currently proposed nearly
saturated result and outperforms various other proposed methods. Code is
available $\href{"https://github.com/melonwan/denseReg"}{\text{online}}$.
Self-Supervised Vision-Based Detection of the Active Speaker as Support for Socially-Aware Language Acquisition
Authors: Kalin Stefanov, Jonas Beskow, Giampiero Salvi
Date: 2017-11-24 00:00:00
Tasks: Active Speaker Detection, Language Acquisition

This paper presents a self-supervised method for visual detection of the active speaker in a multi-person spoken interaction scenario. Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. The proposed method is intended to complement the acoustic detection of the active speaker, thus improving the system robustness in noisy conditions. The method can detect an arbitrary number of possibly overlapping active speakers based exclusively on visual information about their face. Furthermore, the method does not rely on external annotations, thus complying with cognitive development. Instead, the method uses information from the auditory modality to support learning in the visual domain. This paper reports an extensive evaluation of the proposed method using a large multi-person face-to-face interaction dataset. The results show good performance in a speaker dependent setting. However, in a speaker independent setting the proposed method yields a significantly lower performance. We believe that the proposed method represents an essential component of any artificial cognitive system or robotic platform engaging in social interactions.
Deep Depth From Focus
Authors: Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab, Laura Leal-Taixé, Daniel Cremers
Date: 2017-04-04 00:00:00
Tasks: Depth Estimation

Depth from focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
learning approach to this problem. One of the main challenges we face is the
hunger for data of deep neural networks. In order to obtain a significant
amount of focal stacks with corresponding groundtruth depth, we propose to
leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
to digitally create focal stacks of varying sizes. Compared to existing
benchmarks our dataset is 25 times larger, enabling the use of machine learning
for this inverse problem. We compare our results with state-of-the-art DFF
methods and we also analyze the effect of several key deep architectural
components. These experiments show that our proposed method `DDFFNet' achieves
state-of-the-art performance in all scenes, reducing depth error by more than
75% compared to the classical DFF methods.
Image Generation from Sketch Constraint Using Contextual GAN
Authors: Yongyi Lu, Shangzhe Wu, Yu-Wing Tai, Chi-Keung Tang
Date: 2017-11-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Generation, Image-to-Image Translation, Translation

In this paper we investigate image generation guided by hand sketch. When the
input sketch is badly drawn, the output of common image-to-image translation
follows the input edges due to the hard condition imposed by the translation
process. Instead, we propose to use sketch as weak constraint, where the output
edges do not necessarily follow the input edges. We address this problem using
a novel joint image completion approach, where the sketch provides the image
context for completing, or generating the output image. We train a generated
adversarial network, i.e, contextual GAN to learn the joint distribution of
sketch and the corresponding image by using joint images. Our contextual GAN
has several advantages. First, the simple joint image representation allows for
simple and effective learning of joint distribution in the same image-sketch
space, which avoids complicated issues in cross-domain learning. Second, while
the output is related to its input overall, the generated features exhibit more
freedom in appearance and do not strictly align with the input features as
previous conditional GANs do. Third, from the joint image's point of view,
image and sketch are of no difference, thus exactly the same deep joint image
completion network can be used for image-to-sketch generation. Experiments
evaluated on three different datasets show that our contextual GAN can generate
more realistic images than state-of-the-art conditional GANs on challenging
inputs and generalize well on common categories.
Rectifier Neural Network with a Dual-Pathway Architecture for Image Denoising
Authors: Keting Zhang, Liqing Zhang
Date: 2016-09-10 00:00:00
Tasks: Denoising, Image Denoising

Recently deep neural networks based on tanh activation function have shown their impressive power in image denoising. In this letter, we try to use rectifier function instead of tanh and propose a dual-pathway rectifier neural network by combining two rectifier neurons with reversed input and output weights in the same hidden layer. We drive the equivalent activation function and compare it to some typical activation functions for image denoising under the same network architecture. The experimental results show that our model achieves superior performances faster especially when the noise is small.
Action Branching Architectures for Deep Reinforcement Learning
Authors: Arash Tavakoli, Fabio Pardo, Petar Kormushev
Date: 2017-11-24 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Discrete-action algorithms have been central to numerous recent successes of
deep reinforcement learning. However, applying these algorithms to
high-dimensional action tasks requires tackling the combinatorial increase of
the number of possible actions with the number of action dimensions. This
problem is further exacerbated for continuous-action tasks that require fine
control of actions via discretization. In this paper, we propose a novel neural
architecture featuring a shared decision module followed by several network
branches, one for each action dimension. This approach achieves a linear
increase of the number of network outputs with the number of degrees of freedom
by allowing a level of independence for each individual action dimension. To
illustrate the approach, we present a novel agent, called Branching Dueling
Q-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network
(Dueling DDQN). We evaluate the performance of our agent on a set of
challenging continuous control tasks. The empirical results show that the
proposed agent scales gracefully to environments with increasing action
dimensionality and indicate the significance of the shared decision module in
coordination of the distributed action branches. Furthermore, we show that the
proposed agent performs competitively against a state-of-the-art continuous
control algorithm, Deep Deterministic Policy Gradient (DDPG).
Learned Primal-dual Reconstruction
Authors: Jonas Adler, Ozan Öktem
Date: 2017-07-20 00:00:00
Tasks: CT Reconstruction, Rolling Shutter Correction, SSIM

We propose the Learned Primal-Dual algorithm for tomographic reconstruction.
The algorithm accounts for a (possibly non-linear) forward operator in a deep
neural network by unrolling a proximal primal-dual optimization method, but
where the proximal operators have been replaced with convolutional neural
networks. The algorithm is trained end-to-end, working directly from raw
measured data and it does not depend on any initial reconstruction such as FBP.
  We compare performance of the proposed method on low dose CT reconstruction
against FBP, TV, and deep learning based post-processing of FBP. For the
Shepp-Logan phantom we obtain >6dB PSNR improvement against all compared
methods. For human phantoms the corresponding improvement is 6.6dB over TV and
2.2dB over learned post-processing along with a substantial improvement in the
SSIM. Finally, our algorithm involves only ten forward-back-projection
computations, making the method feasible for time critical clinical
applications.