pwc-archive/papers-with-abstracts huggingface dataset:
Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net
Authors: Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, Elliot K. Fishman, Alan L. Yuille
Date: 2018-04-02 00:00:00
Tasks: Organ Segmentation, Segmentation

There has been a debate on whether to use 2D or 3D deep neural networks for
volumetric organ segmentation. Both 2D and 3D models have their advantages and
disadvantages. In this paper, we present an alternative framework, which trains
2D networks on different viewpoints for segmentation, and builds a 3D
Volumetric Fusion Net (VFN) to fuse the 2D segmentation results. VFN is
relatively shallow and contains much fewer parameters than most 3D networks,
making our framework more efficient at integrating 3D information for
segmentation. We train and test the segmentation and fusion modules
individually, and propose a novel strategy, named cross-cross-augmentation, to
make full use of the limited training data. We evaluate our framework on
several challenging abdominal organs, and verify its superiority in
segmentation accuracy and stability over existing 2D and 3D approaches.
Explainable Deterministic MDPs
Authors: Josh Bertram, Peng Wei
Date: 2018-06-09 00:00:00

We present a method for a certain class of Markov Decision Processes (MDPs)
that can relate the optimal policy back to one or more reward sources in the
environment. For a given initial state, without fully computing the value
function, q-value function, or the optimal policy the algorithm can determine
which rewards will and will not be collected, whether a given reward will be
collected only once or continuously, and which local maximum within the value
function the initial state will ultimately lead to. We demonstrate that the
method can be used to map the state space to identify regions that are
dominated by one reward source and can fully analyze the state space to explain
all actions. We provide a mathematical framework to show how all of this is
possible without first computing the optimal policy or value function.
Robust Lexical Features for Improved Neural Network Named-Entity Recognition
Authors: Abbas Ghaddar, Philippe Langlais
Date: 2018-06-09 00:00:00
Proceeding: COLING 2018 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Neural network approaches to Named-Entity Recognition reduce the need for
carefully hand-crafted features. While some features do remain in
state-of-the-art systems, lexical features have been mostly discarded, with the
exception of gazetteers. In this work, we show that this is unfair: lexical
features are actually quite useful. We propose to embed words and entity types
into a low-dimensional vector space we train from annotated data produced by
distant supervision thanks to Wikipedia. From this, we compute - offline - a
feature vector representing each word. When used with a vanilla recurrent
neural network model, this representation yields substantial improvements. We
establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while
matching state-of-the-art performance with a F1 score of 91.73 on the
over-studied CONLL-2003 dataset.
Learning to Grasp from a Single Demonstration
Authors: Pieter Van Molle, Tim Verbelen, Elias De Coninck, Cedric De Boom, Pieter Simoens, Bart Dhoedt
Date: 2018-06-09 00:00:00
Tasks: Robotic Grasping

Learning-based approaches for robotic grasping using visual sensors typically
require collecting a large size dataset, either manually labeled or by many
trial and errors of a robotic manipulator in the real or simulated world. We
propose a simpler learning-from-demonstration approach that is able to detect
the object to grasp from merely a single demonstration using a convolutional
neural network we call GraspNet. In order to increase robustness and decrease
the training time even further, we leverage data from previous demonstrations
to quickly fine-tune a GrapNet for each new demonstration. We present some
preliminary results on a grasping experiment with the Franka Panda cobot for
which we can train a GraspNet with only hundreds of train iterations.
DIR-ST$^2$: Delineation of Imprecise Regions Using Spatio--Temporal--Textual Information
Authors: Cong Tran, Won-Yong Shin, Sang-Il Choi
Date: 2018-06-09 00:00:00
Tasks: Clustering

An imprecise region is referred to as a geographical area without a
clearly-defined boundary in the literature. Previous clustering-based
approaches exploit spatial information to find such regions. However, the prior
studies suffer from the following two problems: the subjectivity in selecting
clustering parameters and the inclusion of a large portion of the undesirable
region (i.e., a large number of noise points). To overcome these problems, we
present DIR-ST$^2$, a novel framework for delineating an imprecise region by
iteratively performing density-based clustering, namely DBSCAN, along with not
only spatio--textual information but also temporal information on social media.
Specifically, we aim at finding a proper radius of a circle used in the
iterative DBSCAN process by gradually reducing the radius for each iteration in
which the temporal information acquired from all resulting clusters are
leveraged. Then, we propose an efficient and automated algorithm delineating
the imprecise region via hierarchical clustering. Experiment results show that
by virtue of the significant noise reduction in the region, our DIR-ST$^2$
method outperforms the state-of-the-art approach employing one-class support
vector machine in terms of the $\mathcal{F}_1$ score from comparison with
precisely-defined regions regarded as a ground truth, and returns apparently
better delineation of imprecise regions. The computational complexity of
DIR-ST$^2$ is also analytically and numerically shown.
Geometry Score: A Method For Comparing Generative Adversarial Networks
Authors: Valentin Khrulkov, Ivan Oseledets
Date: 2018-02-07 00:00:00
Proceeding: ICML 2018 7

One of the biggest challenges in the research of generative adversarial
networks (GANs) is assessing the quality of generated samples and detecting
various levels of mode collapse. In this work, we construct a novel measure of
performance of a GAN by comparing geometrical properties of the underlying data
manifold and the generated one, which provides both qualitative and
quantitative means for evaluation. Our algorithm can be applied to datasets of
an arbitrary nature and is not limited to visual data. We test the obtained
metric on various real-life models and datasets and demonstrate that our method
provides new insights into properties of GANs.
Holographic Automata for Ambient Immersive A. I. via Reservoir Computing
Authors: Theophanes E. Raptis
Date: 2018-06-09 00:00:00
Tasks: Distributed Computing

We prove the existence of a semilinear representation of Cellular Automata
(CA) with the introduction of multiple convolution kernels. Examples of the
technique are presented for rules akin to the "edge-of-chaos" including the
Turing universal rule 110 for further utilization in the area of reservoir
computing. We also examine the significance of their dual representation on a
frequency or wavelength domain as a superposition of plane waves for
distributed computing applications including a new proposal for a "Hologrid"
that could be realized with present Wi-Fi,Li-Fi technologies.
Orthogonal Random Forest for Causal Inference
Authors: Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu
Date: 2018-06-09 00:00:00
Tasks: Causal Inference

We propose the orthogonal random forest, an algorithm that combines Neyman-orthogonality to reduce sensitivity with respect to estimation error of nuisance parameters with generalized random forests (Athey et al., 2017)--a flexible non-parametric method for statistical estimation of conditional moment models using random forests. We provide a consistency rate and establish asymptotic normality for our estimator. We show that under mild assumptions on the consistency rate of the nuisance estimator, we can achieve the same error rate as an oracle with a priori knowledge of these nuisance parameters. We show that when the nuisance functions have a locally sparse parametrization, then a local $\ell_1$-penalized regression achieves the required rate. We apply our method to estimate heterogeneous treatment effects from observational data with discrete treatments or continuous treatments, and we show that, unlike prior work, our method provably allows to control for a high-dimensional set of variables under standard sparsity conditions. We also provide a comprehensive empirical evaluation of our algorithm on both synthetic and real data.
Robust Semantic Segmentation with Ladder-DenseNet Models
Authors: Ivan Krešo, Marin Oršić, Petra Bevandić, Siniša Šegvić
Date: 2018-06-09 00:00:00
Tasks: Semantic Segmentation

We present semantic segmentation experiments with a model capable to perform
predictions on four benchmark datasets: Cityscapes, ScanNet, WildDash and
KITTI. We employ a ladder-style convolutional architecture featuring a modified
DenseNet-169 model in the downsampling datapath, and only one convolution in
each stage of the upsampling datapath. Due to limited computing resources, we
perform the training only on Cityscapes Fine train+val, ScanNet train, WildDash
val and KITTI train. We evaluate the trained model on the test subsets of the
four benchmarks in concordance with the guidelines of the Robust Vision
Challenge ROB 2018. The performed experiments reveal several interesting
findings which we describe and discuss.
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Authors: Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade
Date: 2018-06-09 00:00:00
Proceeding: ICML 2018 7
Tasks: BIG-bench Machine Learning, Binarization, Prediction

Machine learning methods are widely used for a variety of prediction
problems. \emph{Prediction as a service} is a paradigm in which service
providers with technological expertise and computational resources may perform
predictions for clients. However, data privacy severely restricts the
applicability of such services, unless measures to keep client data private
(even from the service provider) are designed. Equally important is to minimize
the amount of computation and communication required between client and server.
Fully homomorphic encryption offers a possible way out, whereby clients may
encrypt their data, and on which the server may perform arithmetic
computations. The main drawback of using fully homomorphic encryption is the
amount of time required to evaluate large machine learning models on encrypted
data. We combine ideas from the machine learning literature, particularly work
on binarization and sparsification of neural networks, together with
algorithmic tools to speed-up and parallelize computation using encrypted data.
A Preliminary Exploration of Floating Point Grammatical Evolution
Authors: Brad Alexander
Date: 2018-06-09 00:00:00
Tasks: regression

Current GP frameworks are highly effective on a range of real and simulated
benchmarks. However, due to the high dimensionality of the genotypes for GP,
the task of visualising the fitness landscape for GP search can be difficult.
This paper describes a new framework: Floating Point Grammatical Evolution
(FP-GE) which uses a single floating point genotype to encode an individual
program. This encoding permits easier visualisation of the fitness landscape
arbitrary problems by providing a way to map fitness against a single
dimension. The new framework also makes it trivially easy to apply continuous
search algorithms, such as Differential Evolution, to the search problem. In
this work, the FP-GE framework is tested against several regression problems,
visualising the search landscape for these and comparing different search
meta-heuristics.
Deep Learning Topological Invariants of Band Insulators
Authors: Ning Sun, Jinmin Yi, Pengfei Zhang, Huitao Shen, Hui Zhai
Date: 2018-05-26 00:00:00
Tasks: Deep Learning

In this work we design and train deep neural networks to predict topological
invariants for one-dimensional four-band insulators in AIII class whose
topological invariant is the winding number, and two-dimensional two-band
insulators in A class whose topological invariant is the Chern number. Given
Hamiltonians in the momentum space as the input, neural networks can predict
topological invariants for both classes with accuracy close to or higher than
90%, even for Hamiltonians whose invariants are beyond the training data set.
Despite the complexity of the neural network, we find that the output of
certain intermediate hidden layers resembles either the winding angle for
models in AIII class or the solid angle (Berry curvature) for models in A
class, indicating that neural networks essentially capture the mathematical
formula of topological invariants. Our work demonstrates the ability of neural
networks to predict topological invariants for complicated models with local
Hamiltonians as the only input, and offers an example that even a deep neural
network is understandable.
Bounding and Counting Linear Regions of Deep Neural Networks
Authors: Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam
Date: 2017-11-06 00:00:00

We investigate the complexity of deep neural networks (DNN) that represent
piecewise linear (PWL) functions. In particular, we study the number of linear
regions, i.e. pieces, that a PWL function represented by a DNN can attain, both
theoretically and empirically. We present (i) tighter upper and lower bounds
for the maximum number of linear regions on rectifier networks, which are exact
for inputs of dimension one; (ii) a first upper bound for multi-layer maxout
networks; and (iii) a first method to perform exact enumeration or counting of
the number of regions by modeling the DNN with a mixed-integer linear
formulation. These bounds come from leveraging the dimension of the space
defining each linear region. The results also indicate that a deep rectifier
network can only have more linear regions than every shallow counterpart with
same number of neurons if that number exceeds the dimension of the input.
A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy
Authors: Akash Malhotra
Date: 2018-06-09 00:00:00
Tasks: BIG-bench Machine Learning, Econometrics

A measure of relative importance of variables is often desired by researchers when the explanatory aspects of econometric methods are of interest. To this end, the author briefly reviews the limitations of conventional econometrics in constructing a reliable measure of variable importance. The author highlights the relative stature of explanatory and predictive analysis in economics and the emergence of fruitful collaborations between econometrics and computer science. Learning lessons from both, the author proposes a hybrid approach based on conventional econometrics and advanced machine learning (ML) algorithms, which are otherwise, used in predictive analytics. The purpose of this article is two-fold, to propose a hybrid approach to assess relative importance and demonstrate its applicability in addressing policy priority issues with an example of food inflation in India, followed by a broader aim to introduce the possibility of conflation of ML and conventional econometrics to an audience of researchers in economics and social sciences, in general.
Abstaining Classification When Error Costs are Unequal and Unknown
Authors: Hongjiao Guan, Yingtao Zhang, H. D. Cheng, Xianglong Tang
Date: 2018-06-09 00:00:00
Tasks: Classification, General Classification

Abstaining classificaiton aims to reject to classify the easily misclassified
examples, so it is an effective approach to increase the clasificaiton
reliability and reduce the misclassification risk in the cost-sensitive
applications. In such applications, different types of errors (false positive
or false negative) usaully have unequal costs. And the error costs, which
depend on specific applications, are usually unknown. However, current
abstaining classification methods either do not distinguish the error types, or
they need the cost information of misclassification and rejection, which are
realized in the framework of cost-sensitive learning. In this paper, we propose
a bounded-abstention method with two constraints of reject rates (BA2), which
performs abstaining classification when error costs are unequal and unknown.
BA2 aims to obtain the optimal area under the ROC curve (AUC) by constraining
the reject rates of the positive and negative classes respectively.
Specifically, we construct the receiver operating characteristic (ROC) curve,
and stepwise search the optimal reject thresholds from both ends of the curve,
untill the two constraints are satisfied. Experimental results show that BA2
obtains higher AUC and lower total cost than the state-of-the-art abstaining
classification methods. Meanwhile, BA2 achieves controllable reject rates of
the positive and negative classes.
Hierarchical Imitation and Reinforcement Learning
Authors: Hoang M. Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III
Date: 2018-03-01 00:00:00
Proceeding: ICML 2018 7
Tasks: Decision Making, Imitation Learning, Montezuma's Revenge, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

We study how to effectively leverage expert feedback to learn sequential
decision-making policies. We focus on problems with sparse rewards and long
time horizons, which typically pose significant challenges in reinforcement
learning. We propose an algorithmic framework, called hierarchical guidance,
that leverages the hierarchical structure of the underlying problem to
integrate different modes of expert interaction. Our framework can incorporate
different combinations of imitation learning (IL) and reinforcement learning
(RL) at different levels, leading to dramatic reductions in both expert effort
and cost of exploration. Using long-horizon benchmarks, including Montezuma's
Revenge, we demonstrate that our approach can learn significantly faster than
hierarchical RL, and be significantly more label-efficient than standard IL. We
also theoretically analyze labeling cost for certain instantiations of our
framework.
Towards Multifocal Displays with Dense Focal Stacks
Authors: Jen-Hao Rick Chang, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan
Date: 2018-05-27 00:00:00

We present a virtual reality display that is capable of generating a dense
collection of depth/focal planes. This is achieved by driving a focus-tunable
lens to sweep a range of focal lengths at a high frequency and, subsequently,
tracking the focal length precisely at microsecond time resolutions using an
optical module. Precise tracking of the focal length, coupled with a high-speed
display, enables our lab prototype to generate 1600 focal planes per second.
This enables a novel first-of-its-kind virtual reality multifocal display that
is capable of resolving the vergence-accommodation conflict endemic to today's
displays.
Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism
Authors: Hector Zenil, Narsis A. Kiani, Allan A. Zea, Jesper Tegnér
Date: 2018-02-18 00:00:00

Complex data usually results from the interaction of objects produced by different generating mechanisms. Here we introduce a universal, unsupervised and parameter-free model-oriented approach, based upon the seminal concept of algorithmic probability, that decomposes an observation into its most likely algorithmic generative sources. Our approach uses a causal calculus to infer model representations. We demonstrate its ability to deconvolve interacting mechanisms regardless of whether the resultant objects are strings, space-time evolution diagrams, images or networks. While this is mostly a conceptual contribution and a novel framework, we provide numerical evidence evaluating the ability of our methods to separate data from observations produced by discrete dynamical systems such as cellular automata and complex networks. We think that these separating techniques can contribute to tackling the challenge of causation, thus complementing other statistically oriented approaches.
Hierarchical Clustering with Prior Knowledge
Authors: Xiaofei Ma, Satya Dhavala
Date: 2018-06-09 00:00:00
Tasks: Clustering, Constrained Clustering

Hierarchical clustering is a class of algorithms that seeks to build a
hierarchy of clusters. It has been the dominant approach to constructing
embedded classification schemes since it outputs dendrograms, which capture the
hierarchical relationship among members at all levels of granularity,
simultaneously. Being greedy in the algorithmic sense, a hierarchical
clustering partitions data at every step solely based on a similarity /
dissimilarity measure. The clustering results oftentimes depend on not only the
distribution of the underlying data, but also the choice of dissimilarity
measure and the clustering algorithm. In this paper, we propose a method to
incorporate prior domain knowledge about entity relationship into the
hierarchical clustering. Specifically, we use a distance function in
ultrametric space to encode the external ontological information. We show that
popular linkage-based algorithms can faithfully recover the encoded structure.
Similar to some regularized machine learning techniques, we add this distance
as a penalty term to the original pairwise distance to regulate the final
structure of the dendrogram. As a case study, we applied this method on real
data in the building of a customer behavior based product taxonomy for an
Amazon service, leveraging the information from a larger Amazon-wide browse
structure. The method is useful when one wants to leverage the relational
information from external sources, or the data used to generate the distance
matrix is noisy and sparse. Our work falls in the category of semi-supervised
or constrained clustering.
Word Familiarity and Frequency
Authors: Kumiko Tanaka-Ishii, Hiroshi Terada
Date: 2018-06-09 00:00:00

Word frequency is assumed to correlate with word familiarity, but the
strength of this correlation has not been thoroughly investigated. In this
paper, we report on our analysis of the correlation between a word familiarity
rating list obtained through a psycholinguistic experiment and the
log-frequency obtained from various corpora of different kinds and sizes (up to
the terabyte scale) for English and Japanese. Major findings are threefold:
First, for a given corpus, familiarity is necessary for a word to achieve high
frequency, but familiar words are not necessarily frequent. Second, correlation
increases with the corpus data size. Third, a corpus of spoken language
correlates better than one of written language. These findings suggest that
cognitive familiarity ratings are correlated to frequency, but more highly to
that of spoken rather than written language.