pwc-archive/papers-with-abstracts huggingface dataset:
SciSports: Learning football kinematics through two-dimensional tracking data
Authors: Anatoliy Babic, Harshit Bansal, Gianluca Finocchio, Julian Golak, Mark Peletier, Jim Portegies, Clara Stegehuis, Anuj Tyagi, Roland Vincze, William Weimin Yoo
Date: 2018-08-14 00:00:00
Tasks: Vocal Bursts Valence Prediction

SciSports is a Dutch startup company specializing in football analytics. This
paper describes a joint research effort with SciSports, during the Study Group
Mathematics with Industry 2018 at Eindhoven, the Netherlands. The main
challenge that we addressed was to automatically process empirical football
players' trajectories, in order to extract useful information from them. The
data provided to us was two-dimensional positional data during entire matches.
We developed methods based on Newtonian mechanics and the Kalman filter,
Generative Adversarial Nets and Variational Autoencoders. In addition, we
trained a discriminator network to recognize and discern different movement
patterns of players. The Kalman-filter approach yields an interpretable model,
in which a small number of player-dependent parameters can be fit; in theory
this could be used to distinguish among players. The
Generative-Adversarial-Nets approach appears promising in theory, and some
initial tests showed an improvement with respect to the baseline, but the
limits in time and computational power meant that we could not fully explore
it. We also trained a Discriminator network to distinguish between two players
based on their trajectories; after training, the network managed to distinguish
between some pairs of players, but not between others. After training, the
Variational Autoencoders generated trajectories that are difficult to
distinguish, visually, from the data. These experiments provide an indication
that deep generative models can learn the underlying structure and statistics
of football players' trajectories. This can serve as a starting point for
determining player qualities based on such trajectory data.
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics
Authors: Xinchen Yan, Akash Rastogi, Ruben Villegas, Kalyan Sunkavalli, Eli Shechtman, Sunil Hadap, Ersin Yumer, Honglak Lee
Date: 2018-08-14 00:00:00
Proceeding: ECCV 2018 9
Tasks: Human Dynamics, Human Pose Forecasting, motion prediction

Long-term human motion can be represented as a series of motion
modes---motion sequences that capture short-term temporal dynamics---with
transitions between them. We leverage this structure and present a novel Motion
Transformation Variational Auto-Encoders (MT-VAE) for learning motion sequence
generation. Our model jointly learns a feature embedding for motion modes (that
the motion sequence can be reconstructed from) and a feature transformation
that represents the transition of one motion mode to the next motion mode. Our
model is able to generate multiple diverse and plausible motion sequences in
the future from the same input. We apply our approach to both facial and full
body motion, and demonstrate applications like analogy-based motion transfer
and video synthesis.
Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks
Authors: Satya Krishna Gorti, Jeremy Ma
Date: 2018-08-14 00:00:00
Tasks: Image to text, Sentence, Translation

Text-to-Image translation has been an active area of research in the recent
past. The ability for a network to learn the meaning of a sentence and generate
an accurate image that depicts the sentence shows ability of the model to think
more like humans. Popular methods on text to image translation make use of
Generative Adversarial Networks (GANs) to generate high quality images based on
text input, but the generated images don't always reflect the meaning of the
sentence given to the model as input. We address this issue by using a
captioning network to caption on generated images and exploit the distance
between ground truth captions and generated captions to improve the network
further. We show extensive comparisons between our method and existing methods.
Learning Linear Transformations for Fast Arbitrary Style Transfer
Authors: Xueting Li, Sifei Liu, Jan Kautz, Ming-Hsuan Yang
Date: 2018-08-14 00:00:00
Tasks: Domain Adaptation, Style Transfer

Given a random pair of images, an arbitrary style transfer method extracts
the feel from the reference image to synthesize an output based on the look of
the other content image. Recent arbitrary style transfer methods transfer
second order statistics from reference image onto content image via a
multiplication between content image features and a transformation matrix,
which is computed from features with a pre-determined algorithm. These
algorithms either require computationally expensive operations, or fail to
model the feature covariance and produce artifacts in synthesized images.
Generalized from these methods, in this work, we derive the form of
transformation matrix theoretically and present an arbitrary style transfer
approach that learns the transformation matrix with a feed-forward network. Our
algorithm is highly efficient yet allows a flexible combination of multi-level
styles while preserving content affinity during style transfer process. We
demonstrate the effectiveness of our approach on four tasks: artistic style
transfer, video and photo-realistic style transfer as well as domain
adaptation, including comparisons with the state-of-the-art methods.
Weight Learning in a Probabilistic Extension of Answer Set Programs
Authors: Joohyung Lee, Yi Wang
Date: 2018-08-14 00:00:00

LPMLN is a probabilistic extension of answer set programs with the weight
scheme derived from that of Markov Logic. Previous work has shown how inference
in LPMLN can be achieved. In this paper, we present the concept of weight
learning in LPMLN and learning algorithms for LPMLN derived from those for
Markov Logic. We also present a prototype implementation that uses answer set
solvers for learning as well as some example domains that illustrate distinct
features of LPMLN learning. Learning in LPMLN is in accordance with the stable
model semantics, thereby it learns parameters for probabilistic extensions of
knowledge-rich domains where answer set programming has shown to be useful but
limited to the deterministic case, such as reachability analysis and reasoning
about actions in dynamic domains. We also apply the method to learn the
parameters for probabilistic abductive reasoning about actions.
Discrete Structural Planning for Neural Machine Translation
Authors: Raphael Shu, Hideki Nakayama
Date: 2018-08-14 00:00:00
Tasks: Machine Translation, Sentence, Text Generation, Translation

Structural planning is important for producing long sentences, which is a
missing part in current language generation models. In this work, we add a
planning phase in neural machine translation to control the coarse structure of
output sentences. The model first generates some planner codes, then predicts
real output words conditioned on them. The codes are learned to capture the
coarse structure of the target sentence. In order to obtain the codes, we
design an end-to-end neural network with a discretization bottleneck, which
predicts the simplified part-of-speech tags of target sentences. Experiments
show that the translation performance are generally improved by planning ahead.
We also find that translations with different structures can be obtained by
manipulating the planner codes.
Adaptive Sampling for Convex Regression
Authors: Max Simchowitz, Kevin Jamieson, Jordan W. Suchow, Thomas L. Griffiths
Date: 2018-08-14 00:00:00
Tasks: regression

In this paper, we introduce the first principled adaptive-sampling procedure
for learning a convex function in the $L_\infty$ norm, a problem that arises
often in the behavioral and social sciences. We present a function-specific
measure of complexity and use it to prove that, for each convex function
$f_{\star}$, our algorithm nearly attains the information-theoretically
optimal, function-specific error rate. We also corroborate our theoretical
contributions with numerical experiments, finding that our method substantially
outperforms passive, uniform sampling for favorable synthetic and data-derived
functions in low-noise settings with large sampling budgets. Our results also
suggest an idealized "oracle strategy", which we use to gauge the potential
advance of any adaptive-sampling strategy over passive sampling, for any given
convex function.
Low Rank Regularization: A Review
Authors: Zhanxuan Hu, Feiping Nie, Rong Wang, Xuelong Li
Date: 2018-08-14 00:00:00
Tasks: BIG-bench Machine Learning, Denoising, Image Denoising

Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version. Over the last decade, much progress has been made in theories and practical applications. Nevertheless, the intersection between them is very slight. In order to construct a bridge between practical applications and theoretical research, in this paper we provide a comprehensive survey for low rank regularization. We first review several traditional machine learning models using low rank regularization, and then show their (or their variants) applications in solving practical issues, such as non-rigid structure from motion and image denoising. Subsequently, we summarize the regularizers and optimization methods that achieve great success in traditional machine learning tasks but are rarely seen in solving practical issues. Finally, we provide a discussion and comparison for some representative regularizers including convex and non-convex relaxations. Extensive experimental results demonstrate that non-convex regularizers can provide a large advantage over the nuclear norm, the regularizer widely used in solving practical issues.
Outfit Generation and Style Extraction via Bidirectional LSTM and Autoencoder
Authors: Takuma Nakamura, Ryosuke Goto
Date: 2018-06-29 00:00:00

When creating an outfit, style is a criterion in selecting each fashion item.
This means that style can be regarded as a feature of the overall outfit.
However, in various previous studies on outfit generation, there have been few
methods focusing on global information obtained from an outfit. To address this
deficiency, we have incorporated an unsupervised style extraction module into a
model to learn outfits. Using the style information of an outfit as a whole,
the proposed model succeeded in generating outfits more flexibly without
requiring additional information. Moreover, the style information extracted by
the proposed model is easy to interpret. The proposed model was evaluated on
two human-generated outfit datasets. In a fashion item prediction task (missing
prediction task), the proposed model outperformed a baseline method. In a style
extraction task, the proposed model extracted some easily distinguishable
styles. In an outfit generation task, the proposed model generated an outfit
while controlling its styles. This capability allows us to generate fashionable
outfits according to various preferences.
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
Authors: Tianshui Chen, Wenxi Wu, Yuefang Gao, Le Dong, Xiaonan Luo, Liang Lin
Date: 2018-08-14 00:00:00
Tasks: Fine-Grained Image Classification, Fine-Grained Image Recognition, Representation Learning

Object categories inherently form a hierarchy with different levels of
concept abstraction, especially for fine-grained categories. For example, birds
(Aves) can be categorized according to a four-level hierarchy of order, family,
genus, and species. This hierarchy encodes rich correlations among various
categories across different levels, which can effectively regularize the
semantic space and thus make prediction less ambiguous. However, previous
studies of fine-grained image recognition primarily focus on categories of one
certain level and usually overlook this correlation information. In this work,
we investigate simultaneously predicting categories of different levels in the
hierarchy and integrating this structured correlation information into the deep
neural network by developing a novel Hierarchical Semantic Embedding (HSE)
framework. Specifically, the HSE framework sequentially predicts the category
score vector of each level in the hierarchy, from highest to lowest. At each
level, it incorporates the predicted score vector of the higher level as prior
knowledge to learn finer-grained feature representation. During training, the
predicted score vector of the higher level is also employed to regularize label
prediction by using it as soft targets of corresponding sub-categories. To
evaluate the proposed framework, we organize the 200 bird species of the
Caltech-UCSD birds dataset with the four-level category hierarchy and construct
a large-scale butterfly dataset that also covers four level categories.
Extensive experiments on these two and the newly-released VegFru datasets
demonstrate the superiority of our HSE framework over the baseline methods and
existing competitors.
Shared Multi-Task Imitation Learning for Indoor Self-Navigation
Authors: Junhong Xu, Qiwei Liu, Hanqing Guo, Aaron Kageza, Saeed AlQarni, Shaoen Wu
Date: 2018-08-14 00:00:00
Tasks: Imitation Learning

Deep imitation learning enables robots to learn from expert demonstrations to
perform tasks such as lane following or obstacle avoidance. However, in the
traditional imitation learning framework, one model only learns one task, and
thus it lacks of the capability to support a robot to perform various different
navigation tasks with one model in indoor environments. This paper proposes a
new framework, Shared Multi-headed Imitation Learning(SMIL), that allows a
robot to perform multiple tasks with one model without switching among
different models. We model each task as a sub-policy and design a multi-headed
policy to learn the shared information among related tasks by summing up
activations from all sub-policies. Compared to single or non-shared
multi-headed policies, this framework is able to leverage correlated
information among tasks to increase performance.We have implemented this
framework using a robot based on NVIDIA TX2 and performed extensive experiments
in indoor environments with different baseline solutions. The results
demonstrate that SMIL has doubled the performance over nonshared multi-headed
policy.
ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans
Authors: Felix Lau, Tom Hendriks, Jesse Lieman-Sifry, Berk Norman, Sean Sall, Daniel Golden
Date: 2018-08-14 00:00:00

Medical images with specific pathologies are scarce, but a large amount of
data is usually required for a deep convolutional neural network (DCNN) to
achieve good accuracy. We consider the problem of segmenting the left
ventricular (LV) myocardium on late gadolinium enhancement (LGE) cardiovascular
magnetic resonance (CMR) scans of which only some of the scans have scar
tissue. We propose ScarGAN to simulate scar tissue on healthy myocardium using
chained generative adversarial networks (GAN). Our novel approach factorizes
the simulation process into 3 steps: 1) a mask generator to simulate the shape
of the scar tissue; 2) a domain-specific heuristic to produce the initial
simulated scar tissue from the simulated shape; 3) a refining generator to add
details to the simulated scar tissue. Unlike other approaches that generate
samples from scratch, we simulate scar tissue on normal scans resulting in
highly realistic samples. We show that experienced radiologists are unable to
distinguish between real and simulated scar tissue. Training a U-Net with
additional scans with scar tissue simulated by ScarGAN increases the percentage
of scar pixels correctly included in LV myocardium prediction from 75.9% to
80.5%.
Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation
Authors: Jialei Chen, Yujia Xie, Kan Wang, Zih Huei Wang, Geet Lahoti, Chuck Zhang, Mani A. Vannan, Ben Wang, Zhen Qian
Date: 2018-08-14 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Machine learning methods play increasingly important roles in pre-procedural
planning for complex surgeries and interventions. Very often, however,
researchers find the historical records of emerging surgical techniques, such
as the transcatheter aortic valve replacement (TAVR), are highly scarce in
quantity. In this paper, we address this challenge by proposing novel
generative invertible networks (GIN) to select features and generate
high-quality virtual patients that may potentially serve as an additional data
source for machine learning. Combining a convolutional neural network (CNN) and
generative adversarial networks (GAN), GIN discovers the pathophysiologic
meaning of the feature space. Moreover, a test of predicting the surgical
outcome directly using the selected features results in a high accuracy of
81.55%, which suggests little pathophysiologic information has been lost while
conducting the feature selection. This demonstrates GIN can generate virtual
patients not only visually authentic but also pathophysiologically
interpretable.
CLAIRE: A distributed-memory solver for constrained large deformation diffeomorphic image registration
Authors: Andreas Mang, Amir Gholami, Christos Davatzikos, George Biros
Date: 2018-08-13 00:00:00
Tasks: Image Registration

With this work, we release CLAIRE, a distributed-memory implementation of an effective solver for constrained large deformation diffeomorphic image registration problems in three dimensions. We consider an optimal control formulation. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss--Newton--Krylov scheme. We exploit state-of-the-art techniques in scientific computing to develop an effective solver that scales to thousands of distributed memory nodes on high-end clusters. We present the formulation, discuss algorithmic features, describe the software package, and introduce an improved preconditioner for the reduced space Hessian to speed up the convergence of our solver. We test registration performance on synthetic and real data. We demonstrate registration accuracy on several neuroimaging datasets. We compare the performance of our scheme against different flavors of the Demons algorithm for diffeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5$\times$ with a peak performance of up to 17$\times$ compared to our former work.
Fast Convergence for Object Detection by Learning how to Combine Error Functions
Authors: Benjamin Schnieders, Karl Tuyls
Date: 2018-08-13 00:00:00
Tasks: object-detection, Object Detection

In this paper, we introduce an innovative method to improve the convergence
speed and accuracy of object detection neural networks. Our approach,
CONVERGE-FAST-AUXNET, is based on employing multiple, dependent loss metrics
and weighting them optimally using an on-line trained auxiliary network.
Experiments are performed in the well-known RoboCup@Work challenge environment.
A fully convolutional segmentation network is trained on detecting objects'
pickup points. We empirically obtain an approximate measure for the rate of
success of a robotic pickup operation based on the accuracy of the object
detection network. Our experiments show that adding an optimally weighted
Euclidean distance loss to a network trained on the commonly used Intersection
over Union (IoU) metric reduces the convergence time by 42.48%. The estimated
pickup rate is improved by 39.90%. Compared to state-of-the-art task weighting
methods, the improvement is 24.5% in convergence, and 15.8% on the estimated
pickup rate.
Kernel Flows: from learning kernels from data into the abyss
Authors: Houman Owhadi, Gene Ryan Yoo
Date: 2018-08-13 00:00:00

Learning can be seen as approximating an unknown function by interpolating
the training data. Kriging offers a solution to this problem based on the prior
specification of a kernel. We explore a numerical approximation approach to
kernel selection/construction based on the simple premise that a kernel must be
good if the number of interpolation points can be halved without significant
loss in accuracy (measured using the intrinsic RKHS norm $\|\cdot\|$ associated
with the kernel). We first test and motivate this idea on a simple problem of
recovering the Green's function of an elliptic PDE (with inhomogeneous
coefficients) from the sparse observation of one of its solutions. Next we
consider the problem of learning non-parametric families of deep kernels of the
form $K_1(F_n(x),F_n(x'))$ with $F_{n+1}=(I_d+\epsilon G_{n+1})\circ F_n$ and
$G_{n+1} \in \operatorname{Span}\{K_1(F_n(x_i),\cdot)\}$. With the proposed
approach constructing the kernel becomes equivalent to integrating a stochastic
data driven dynamical system, which allows for the training of very deep
(bottomless) networks and the exploration of their properties. These networks
learn by constructing flow maps in the kernel and input spaces via incremental
data-dependent deformations/perturbations (appearing as the cooperative
counterpart of adversarial examples) and, at profound depths, they (1) can
achieve accurate classification from only one data point per class (2) appear
to learn archetypes of each class (3) expand distances between points that are
in different classes and contract distances between points in the same class.
For kernels parameterized by the weights of Convolutional Neural Networks,
minimizing approximation errors incurred by halving random subsets of
interpolation points, appears to outperform training (the same CNN
architecture) with relative entropy and dropout.
Deep Randomized Ensembles for Metric Learning
Authors: Hong Xuan, Richard Souvenir, Robert Pless
Date: 2018-08-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, Image Retrieval, Information Retrieval, Metric Learning, Retrieval

Learning embedding functions, which map semantically related inputs to nearby
locations in a feature space supports a variety of classification and
information retrieval tasks. In this work, we propose a novel, generalizable
and fast method to define a family of embedding functions that can be used as
an ensemble to give improved results. Each embedding function is learned by
randomly bagging the training labels into small subsets. We show experimentally
that these embedding ensembles create effective embedding functions. The
ensemble output defines a metric space that improves state of the art
performance for image retrieval on CUB-200-2011, Cars-196, In-Shop Clothes
Retrieval and VehicleID.
Risk-Sensitive Generative Adversarial Imitation Learning
Authors: Jonathan Lacotte, Mohammad Ghavamzadeh, Yin-Lam Chow, Marco Pavone
Date: 2018-08-13 00:00:00
Tasks: Imitation Learning, MuJoCo

We study risk-sensitive imitation learning where the agent's goal is to
perform at least as well as the expert in terms of a risk profile. We first
formulate our risk-sensitive imitation learning setting. We consider the
generative adversarial approach to imitation learning (GAIL) and derive an
optimization problem for our formulation, which we call it risk-sensitive GAIL
(RS-GAIL). We then derive two different versions of our RS-GAIL optimization
problem that aim at matching the risk profiles of the agent and the expert
w.r.t. Jensen-Shannon (JS) divergence and Wasserstein distance, and develop
risk-sensitive generative adversarial imitation learning algorithms based on
these optimization problems. We evaluate the performance of our algorithms and
compare them with GAIL and the risk-averse imitation learning (RAIL) algorithms
in two MuJoCo and two OpenAI classical control tasks.