pwc-archive/papers-with-abstracts huggingface dataset:
CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction
Authors: Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, Jianfeng Xu
Date: 2018-07-04 00:00:00
Tasks: Articles, Prediction, Text Classification

In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw
challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for
judgment prediction. \dataset contains more than $2.6$ million criminal cases
published by the Supreme People's Court of China, which are several times
larger than other datasets in existing works on judgment prediction. Moreover,
the annotations of judgment results are more detailed and rich. It consists of
applicable law articles, charges, and prison terms, which are expected to be
inferred according to the fact descriptions of cases. For comparison, we
implement several conventional text classification baselines for judgment
prediction and experimental results show that it is still a challenge for
current models to predict the judgment results of legal cases, especially on
prison terms. To help the researchers make improvements on legal judgment
prediction, both \dataset and baselines will be released after the CAIL
competition\footnote{http://cail.cipsc.org.cn/}.
Jacquard: A Large Scale Dataset for Robotic Grasp Detection
Authors: Amaury Depierre, Emmanuel Dellandréa, Liming Chen
Date: 2018-03-30 00:00:00
Tasks: Diversity, Robotic Grasping

Grasping skill is a major ability that a wide number of real-life
applications require for robotisation. State-of-the-art robotic grasping
methods perform prediction of object grasp locations based on deep neural
networks. However, such networks require huge amount of labeled data for
training making this approach often impracticable in robotics. In this paper,
we propose a method to generate a large scale synthetic dataset with ground
truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on
a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D
images and annotations of successful grasping positions based on grasp attempts
performed in a simulated environment. We carried out experiments using an
off-the-shelf CNN, with three different evaluation metrics, including real
grasping robot trials. The results show that Jacquard enables much better
generalization skills than a human labeled dataset thanks to its diversity of
objects and grasping positions. For the purpose of reproducible research in
robotics, we are releasing along with the Jacquard dataset a web interface for
researchers to evaluate the successfulness of their grasping position
detections using our dataset.
Multi-atomic Annealing Heuristic for Static Dial-a-ride Problem
Authors: Song Guang Ho, Ramesh Ramasamy Pandi, Sarat Chandra Nagavarapu, Justin Dauwels
Date: 2018-06-29 00:00:00

Dial-a-ride problem (DARP) deals with the transportation of users between
pickup and drop-off locations associated with specified time windows. This
paper proposes a novel algorithm called multi-atomic annealing (MATA) to solve
static dial-a-ride problem. Two new local search operators (burn and reform), a
new construction heuristic and two request sequencing mechanisms (Sorted List
and Random List) are developed. Computational experiments conducted on various
standard DARP test instances prove that MATA is an expeditious meta-heuristic
in contrast to other existing methods. In all experiments, MATA demonstrates
the capability to obtain high quality solutions, faster convergence, and
quicker attainment of a first feasible solution. It is observed that MATA
attains a first feasible solution 29.8 to 65.1% faster, and obtains a final
solution that is 3.9 to 5.2% better, when compared to other algorithms within
60 sec.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Authors: Stanislav Pidhorskyi, Ranya Almohsen, Donald A. Adjeroh, Gianfranco Doretto
Date: 2018-07-06 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decoder, Novelty Detection, One-class classifier

Novelty detection is the problem of identifying whether a new data point is
considered to be an inlier or an outlier. We assume that training data is
available to describe only the inlier distribution. Recent approaches primarily
leverage deep encoder-decoder network architectures to compute a reconstruction
error that is used to either compute a novelty score or to train a one-class
classifier. While we too leverage a novel network of that kind, we take a
probabilistic approach and effectively compute how likely is that a sample was
generated by the inlier distribution. We achieve this with two main
contributions. First, we make the computation of the novelty probability
feasible because we linearize the parameterized manifold capturing the
underlying structure of the inlier distribution, and show how the probability
factorizes and can be computed with respect to local coordinates of the
manifold tangent space. Second, we improved the training of the autoencoder
network. An extensive set of results show that the approach achieves
state-of-the-art results on several benchmark datasets.
Variational image compression with a scale hyperprior
Authors: Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston
Date: 2018-02-01 00:00:00
Proceeding: ICLR 2018
Tasks: Image Compression, MS-SSIM, SSIM

We describe an end-to-end trainable model for image compression based on
variational autoencoders. The model incorporates a hyperprior to effectively
capture spatial dependencies in the latent representation. This hyperprior
relates to side information, a concept universal to virtually all modern image
codecs, but largely unexplored in image compression using artificial neural
networks (ANNs). Unlike existing autoencoder compression methods, our model
trains a complex prior jointly with the underlying autoencoder. We demonstrate
that this model leads to state-of-the-art image compression when measuring
visual quality using the popular MS-SSIM index, and yields rate-distortion
performance surpassing published ANN-based methods when evaluated using a more
traditional metric based on squared error (PSNR). Furthermore, we provide a
qualitative comparison of models trained for different distortion metrics.
Dynamic Pricing with Finitely Many Unknown Valuations
Authors: Nicolò Cesa-Bianchi, Tommaso Cesari, Vianney Perchet
Date: 2018-07-09 00:00:00

Motivated by posted price auctions where buyers are grouped in an unknown
number of latent types characterized by their private values for the good on
sale, we investigate revenue maximization in stochastic dynamic pricing when
the distribution of buyers' private values is supported on an unknown set of
points in [0,1] of unknown cardinality $K$. This setting can be viewed as an
instance of a stochastic $K$-armed bandit problem where the location of the
arms (the $K$ unknown valuations) must be learned as well. In the
distribution-free case, we prove that our setting is just as hard as $K$-armed
stochastic bandits: no algorithm can achieve a regret significantly better than
$\sqrt{KT}$, (where T is the time horizon); we present an efficient algorithm
matching this lower bound up to logarithmic factors. In the
distribution-dependent case, we show that for all $K>2$ our setting is strictly
harder than $K$-armed stochastic bandits by proving that it is impossible to
obtain regret bounds that grow logarithmically in time or slower. On the other
hand, when a lower bound $\gamma>0$ on the smallest drop in the demand curve is
known, we prove an upper bound on the regret of order $(1/\Delta+(\log \log
T)/\gamma^2)(K\log T)$. This is a significant improvement on previously known
regret bounds for discontinuous demand curves, that are at best of order
$(K^{12}/\gamma^8)\sqrt{T}$. When $K=2$ in the distribution-dependent case, the
hardness of our setting reduces to that of a stochastic $2$-armed bandit: we
prove that an upper bound of order $(\log T)/\Delta$ (up to $\log\log$ factors)
on the regret can be achieved with no information on the demand curve. Finally,
we show a $O(\sqrt{T})$ upper bound on the regret for the setting in which the
buyers' decisions are nonstochastic, and the regret is measured with respect to
the best between two fixed valuations one of which is known to the seller.
Pooling Pyramid Network for Object Detection
Authors: Pengchong Jin, Vivek Rathod, Xiangxin Zhu
Date: 2018-07-09 00:00:00
Tasks: All, Object, object-detection, Object Detection

We'd like to share a simple tweak of Single Shot Multibox Detector (SSD)
family of detectors, which is effective in reducing model size while
maintaining the same quality. We share box predictors across all scales, and
replace convolution between scales with max pooling. This has two advantages
over vanilla SSD: (1) it avoids score miscalibration across scales; (2) the
shared predictor sees the training data over all scales. Since we reduce the
number of predictors to one, and trim all convolutions between them, model size
is significantly smaller. We empirically show that these changes do not hurt
model quality compared to vanilla SSD.
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Authors: Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski
Date: 2018-07-09 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Atari Games, Image Classification, Translation

Few ideas have enjoyed as large an impact on deep learning as convolution.
For any problem involving pixels or spatial representations, common intuition
holds that convolutional neural networks may be appropriate. In this paper we
show a striking counterexample to this intuition via the seemingly trivial
coordinate transform problem, which simply requires learning a mapping between
coordinates in (x,y) Cartesian space and one-hot pixel space. Although
convolutional networks would seem appropriate for this task, we show that they
fail spectacularly. We demonstrate and carefully analyze the failure first on a
toy problem, at which point a simple fix becomes obvious. We call this solution
CoordConv, which works by giving convolution access to its own input
coordinates through the use of extra coordinate channels. Without sacrificing
the computational and parametric efficiency of ordinary convolution, CoordConv
allows networks to learn either complete translation invariance or varying
degrees of translation dependence, as required by the end task. CoordConv
solves the coordinate transform problem with perfect generalization and 150
times faster with 10--100 times fewer parameters than convolution. This stark
contrast raises the question: to what extent has this inability of convolution
persisted insidiously inside other tasks, subtly hampering performance from
within? A complete answer to this question will require further investigation,
but we show preliminary evidence that swapping convolution for CoordConv can
improve models on a diverse set of tasks. Using CoordConv in a GAN produced
less mode collapse as the transform between high-level spatial latents and
pixels becomes easier to learn. A Faster R-CNN detection model trained on MNIST
showed 24% better IOU when using CoordConv, and in the RL domain agents playing
Atari games benefit significantly from the use of CoordConv layers.
Exploring Brain-wide Development of Inhibition through Deep Learning
Authors: Asim Iqbal, Asfandyar Sheikh, Theofanis Karayannis
Date: 2018-07-09 00:00:00
Tasks: Deep Learning

We introduce here a fully automated convolutional neural network-based method
for brain image processing to Detect Neurons in different brain Regions during
Development (DeNeRD). Our method takes a developing mouse brain as input and i)
registers the brain sections against a developing mouse reference atlas, ii)
detects various types of neurons, and iii) quantifies the neural density in
many unique brain regions at different postnatal (P) time points. Our method is
invariant to the shape, size and expression of neurons and by using DeNeRD, we
compare the brain-wide neural density of all GABAergic neurons in developing
brains of ages P4, P14 and P56. We discover and report 6 different clusters of
regions in the mouse brain in which GABAergic neurons develop in a differential
manner from early age (P4) to adulthood (P56). These clusters reveal key steps
of GABAergic cell development that seem to track with the functional
development of diverse brain regions as the mouse transitions from a passive
receiver of sensory information (<P14) to an active seeker (>P14).
Automatic multi-objective based feature selection for classification
Authors: Zhiguo Zhou, Shulong Li, Genggeng Qin, Michael Folkert, Steve Jiang, Jing Wang
Date: 2018-07-09 00:00:00
Tasks: Classification, feature selection, General Classification, Specificity

Objective: Accurately classifying the malignancy of lesions detected in a
screening scan is critical for reducing false positives. Radiomics holds great
potential to differentiate malignant from benign tumors by extracting and
analyzing a large number of quantitative image features. Since not all radiomic
features contribute to an effective classifying model, selecting an optimal
feature subset is critical. Methods: This work proposes a new multi-objective
based feature selection (MO-FS) algorithm that considers sensitivity and
specificity simultaneously as the objective functions during feature selection.
For MO-FS, we developed a modified entropy based termination criterion (METC)
that stops the algorithm automatically rather than relying on a preset number
of generations. We also designed a solution selection methodology for
multi-objective learning that uses the evidential reasoning approach (SMOLER)
to automatically select the optimal solution from the Pareto-optimal set.
Furthermore, we developed an adaptive mutation operation to generate the
mutation probability in MO-FS automatically. Results: We evaluated the MO-FS
for classifying lung nodule malignancy in low-dose CT and breast lesion
malignancy in digital breast tomosynthesis. Conclusion: The experimental
results demonstrated that the feature set selected by MO-FS achieved better
classification performance than features selected by other commonly used
methods. Significance: The proposed method is general and more effective
radiomic feature selection strategy.
Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources
Authors: Hosnieh Sattar, Gerard Pons-Moll, Mario Fritz
Date: 2018-07-09 00:00:00

To study the correlation between clothing garments and body shape, we
collected a new dataset (Fashion Takes Shape), which includes images of users
with clothing category annotations. We employ our multi-photo approach to
estimate body shapes of each user and build a conditional model of clothing
categories given body-shape. We demonstrate that in real-world data, clothing
categories and body-shapes are correlated and show that our multi-photo
approach leads to a better predictive model for clothing categories compared to
models based on single-view shape estimates or manually annotated body types.
We see our method as the first step towards the large-scale understanding of
clothing preferences from body shape.
Design and Evaluation of a Tutor Platform for Personalized Vocabulary Learning
Authors: Ravi Kokku, Aditya Vempaty, Tamer Abuelsaad, Prasenjit Dey, Tammy Humphrey, Akimi Gibson, Jennifer Kotler
Date: 2018-07-09 00:00:00

This paper presents our experiences in designing, implementing, and piloting
an intelligent vocabulary learning tutor. The design builds on several
intelligent tutoring design concepts, including graph-based knowledge
representation, learner modeling, and adaptive learning content and assessment
exposition. Specifically, we design a novel phased learner model approach to
enable systematic exposure to words during vocabulary instruction. We also
built an example application over the tutor platform that uses a learning
activity involving videos and an assessment activity involving word to
picture/image association. More importantly, the tutor adapts to the
significant variation in children's knowledge at the beginning of kindergarten,
and evolves the application at the speed of each individual learner. A pilot
study with 180 kindergarten learners allowed the tutor to collect various kinds
of activity information suitable for insights and interventions both at an
individual- and class-level. The effort also demonstrates that we can do A/B
testing for a variety of hypotheses at scale with such a framework.
Entropy Maximization for Markov Decision Processes Under Temporal Logic Constraints
Authors: Yagiz Savas, Melkior Ornik, Murat Cubuktepe, Mustafa O. Karabag, Ufuk Topcu
Date: 2018-07-09 00:00:00
Tasks: Motion Planning

We study the problem of synthesizing a policy that maximizes the entropy of a Markov decision process (MDP) subject to a temporal logic constraint. Such a policy minimizes the predictability of the paths it generates, or dually, maximizes the exploration of different paths in an MDP while ensuring the satisfaction of a temporal logic specification. We first show that the maximum entropy of an MDP can be finite, infinite or unbounded. We provide necessary and sufficient conditions under which the maximum entropy of an MDP is finite, infinite or unbounded. We then present an algorithm which is based on a convex optimization problem to synthesize a policy that maximizes the entropy of an MDP. We also show that maximizing the entropy of an MDP is equivalent to maximizing the entropy of the paths that reach a certain set of states in the MDP. Finally, we extend the algorithm to an MDP subject to a temporal logic specification. In numerical examples, we demonstrate the proposed method on different motion planning scenarios and illustrate the relation between the restrictions imposed on the paths by a specification, the maximum entropy, and the predictability of paths.
Efficient Decentralized Deep Learning by Dynamic Model Averaging
Authors: Michael Kamp, Linara Adilova, Joachim Sicking, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel
Date: 2018-07-09 00:00:00
Tasks: Autonomous Driving, Deep Learning, General Classification, image-classification, Image Classification, model

We propose an efficient protocol for decentralized training of deep neural
networks from distributed data sources. The proposed protocol allows to handle
different phases of model training equally well and to quickly adapt to concept
drifts. This leads to a reduction of communication by an order of magnitude
compared to periodically communicating state-of-the-art approaches. Moreover,
we derive a communication bound that scales well with the hardness of the
serialized learning problem. The reduction in communication comes at almost no
cost, as the predictive performance remains virtually unchanged. Indeed, the
proposed protocol retains loss bounds of periodically averaging schemes. An
extensive empirical evaluation validates major improvement of the trade-off
between model performance and communication which could be beneficial for
numerous decentralized learning applications, such as autonomous driving, or
voice recognition and image classification on mobile phones.
Bandit Online Learning with Unknown Delays
Authors: Bingcong Li, Tianyi Chen, Georgios B. Giannakis
Date: 2018-07-09 00:00:00

This paper deals with bandit online learning problems involving feedback of unknown delay that can emerge in multi-armed bandit (MAB) and bandit convex optimization (BCO) settings. MAB and BCO require only values of the objective function involved that become available through feedback, and are used to estimate the gradient appearing in the corresponding iterative algorithms. Since the challenging case of feedback with \emph{unknown} delays prevents one from constructing the sought gradient estimates, existing MAB and BCO algorithms become intractable. For such challenging setups, delayed exploration, exploitation, and exponential (DEXP3) iterations, along with delayed bandit gradient descent (DBGD) iterations are developed for MAB and BCO, respectively. Leveraging a unified analysis framework, it is established that the regret of DEXP3 and DBGD are ${\cal O}\big( \sqrt{K\bar{d}(T+D)} \big)$ and ${\cal O}\big( \sqrt{K(T+D)} \big)$, respectively, where $\bar{d}$ is the maximum delay and $D$ denotes the delay accumulated over $T$ slots. Numerical tests using both synthetic and real data validate the performance of DEXP3 and DBGD.
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
Authors: Andreas Hauptmann, Ben Cox, Felix Lucka, Nam Huynh, Marta Betcke, Paul Beard, Simon Arridge
Date: 2018-07-09 00:00:00
Tasks: Deep Learning

We present a framework for accelerated iterative reconstructions using a fast
and approximate forward model that is based on k-space methods for
photoacoustic tomography. The approximate model introduces aliasing artefacts
in the gradient information for the iterative reconstruction, but these
artefacts are highly structured and we can train a CNN that can use the
approximate information to perform an iterative reconstruction. We show
feasibility of the method for human in-vivo measurements in a limited-view
geometry. The proposed method is able to produce superior results to total
variation reconstructions with a speed-up of 32 times.
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation
Authors: Wanxiang Che, Yijia Liu, Yuxuan Wang, Bo Zheng, Ting Liu
Date: 2018-07-09 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing, Word Embeddings

This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared
task on Multilingual Parsing from Raw Text to Universal Dependencies. We base
our submission on Stanford's winning system for the CoNLL 2017 shared task and
make two effective extensions: 1) incorporating deep contextualized word
embeddings into both the part of speech tagger and parser; 2) ensembling
parsers trained with different initialization. We also explore different ways
of concatenating treebanks for further improvements. Experimental results on
the development data show the effectiveness of our methods. In the final
evaluation, our system was ranked first according to LAS (75.84%) and
outperformed the other systems by a large margin.
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
Authors: Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh
Date: 2018-07-09 00:00:00

Empirical evidence suggests that heavy-tailed degree distributions occurring
in many real networks are well-approximated by power laws with exponents $\eta$
that may take values either less than and greater than two. Models based on
various forms of exchangeability are able to capture power laws with $\eta <
2$, and admit tractable inference algorithms; we draw on previous results to
show that $\eta > 2$ cannot be generated by the forms of exchangeability used
in existing random graph models. Preferential attachment models generate power
law exponents greater than two, but have been of limited use as statistical
models due to the inherent difficulty of performing inference in
non-exchangeable models. Motivated by this gap, we design and implement
inference algorithms for a recently proposed class of models that generates
$\eta$ of all possible values. We show that although they are not exchangeable,
these models have probabilistic structure amenable to inference. Our methods
make a large class of previously intractable models useful for statistical
inference.