pwc-archive/papers-with-abstracts huggingface dataset:
A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem
Authors: Ian Xiao
Date: 2018-10-09 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Rebalancing is a critical service bottleneck for many transportation
services, such as Citi Bike. Citi Bike relies on manual orchestrations of
rebalancing bikes between dispatchers and field agents. Motivated by such
problem and the lack of smart autonomous solutions in this area, this project
explored a new RL architecture called Distributed RL (DiRL) with Transfer
Learning (TL) capability. The DiRL solution is adaptive to changing traffic
dynamics when keeping bike stock under control at the minimum cost. DiRL
achieved a 350% improvement in bike rebalancing autonomously and TL offered a
62.4% performance boost in managing an entire bike network. Lastly, a field
trip to the dispatch office of Chariot, a ride-sharing service, provided
insights to overcome challenges of deploying an RL solution in the real world.
Dropout as a Structured Shrinkage Prior
Authors: Eric Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth
Date: 2018-10-09 00:00:00
Tasks: Bayesian Inference

Dropout regularization of deep neural networks has been a mysterious yet effective tool to prevent overfitting. Explanations for its success range from the prevention of "co-adapted" weights to it being a form of cheap Bayesian inference. We propose a novel framework for understanding multiplicative noise in neural networks, considering continuous distributions as well as Bernoulli noise (i.e. dropout). We show that multiplicative noise induces structured shrinkage priors on a network's weights. We derive the equivalence through reparametrization properties of scale mixtures and without invoking any approximations. Given the equivalence, we then show that dropout's Monte Carlo training objective approximates marginal MAP estimation. We leverage these insights to propose a novel shrinkage framework for resnets, terming the prior 'automatic depth determination' as it is the natural analog of automatic relevance determination for network depth. Lastly, we investigate two inference strategies that improve upon the aforementioned MAP approximation in regression benchmarks.
Selective Distillation of Weakly Annotated GTD for Vision-based Slab Identification System
Authors: Sang Jun Lee, Sang Woo Kim, Wookyong Kwon, Gyogwon Koo, Jong Pil Yun
Date: 2018-10-09 00:00:00

This paper proposes an algorithm for recognizing slab identification numbers
in factory scenes. In the development of a deep-learning based system, manual
labeling to make ground truth data (GTD) is an important but expensive task.
Furthermore, the quality of GTD is closely related to the performance of a
supervised learning algorithm. To reduce manual work in the labeling process,
we generated weakly annotated GTD by marking only character centroids. Whereas
bounding-boxes for characters require at least a drag-and-drop operation or two
clicks to annotate a character location, the weakly annotated GTD requires a
single click to record a character location. The main contribution of this
paper is on selective distillation to improve the quality of the weakly
annotated GTD. Because manual GTD are usually generated by many people, it may
contain personal bias or human error. To address this problem, the information
in manual GTD is integrated and refined by selective distillation. In the
process of selective distillation, a fully convolutional network is trained
using the weakly annotated GTD, and its prediction maps are selectively used to
revise locations and boundaries of semantic regions of characters in the
initial GTD. The modified GTD are used in the main training stage, and a
post-processing is conducted to retrieve text information. Experiments were
thoroughly conducted on actual industry data collected at a steelmaking factory
to demonstrate the effectiveness of the proposed method.
Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT
Authors: Hans Meine, Grzegorz Chlebus, Mohsen Ghafoorian, Itaru Endo, Andrea Schenk
Date: 2018-10-09 00:00:00
Tasks: Liver Segmentation

Various approaches for liver segmentation in CT have been proposed: Besides
statistical shape models, which played a major role in this research area,
novel approaches on the basis of convolutional neural networks have been
introduced recently. Using a set of 219 liver CT datasets with reference
segmentations from liver surgery planning, we evaluate the performance of
several neural network classifiers based on 2D and 3D U-net architectures. An
interesting observation is that slice-wise approaches perform surprisingly
well, with mean and median Dice coefficients above 0.97, and may be preferable
over 3D approaches given current hardware and software limitations.
Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement
Authors: Risheng Liu, Long Ma, Yiyang Wang, Lei Zhang
Date: 2018-10-09 00:00:00
Tasks: Image Enhancement

Enhancing visual qualities of images plays very important roles in various
vision and learning applications. In the past few years, both knowledge-driven
maximum a posterior (MAP) with prior modelings and fully data-dependent
convolutional neural network (CNN) techniques have been investigated to address
specific enhancement tasks. In this paper, by exploiting the advantages of
these two types of mechanisms within a complementary propagation perspective,
we propose a unified framework, named deep prior ensemble (DPE), for solving
various image enhancement tasks. Specifically, we first establish the basic
propagation scheme based on the fundamental image modeling cues and then
introduce residual CNNs to help predicting the propagation direction at each
stage. By designing prior projections to perform feedback control, we
theoretically prove that even with experience-inspired CNNs, DPE is definitely
converged and the output will always satisfy our fundamental task constraints.
The main advantage against conventional optimization-based MAP approaches is
that our descent directions are learned from collected training data, thus are
much more robust to unwanted local minimums. While, compared with existing CNN
type networks, which are often designed in heuristic manners without
theoretical guarantees, DPE is able to gain advantages from rich task cues
investigated on the bases of domain knowledges. Therefore, DPE actually
provides a generic ensemble methodology to integrate both knowledge and
data-based cues for different image enhancement tasks. More importantly, our
theoretical investigations verify that the feedforward propagations of DPE are
properly controlled toward our desired solution. Experimental results
demonstrate that the proposed DPE outperforms state-of-the-arts on a variety of
image enhancement tasks in terms of both quantitative measure and visual
perception quality.
Glioma Segmentation with Cascaded Unet
Authors: Dmitry Lachinov, Evgeny Vasiliev, Vadim Turlapov
Date: 2018-10-09 00:00:00
Tasks: Brain Tumor Segmentation, object-detection, Object Detection, Segmentation, Tumor Segmentation

MRI analysis takes central position in brain tumor diagnosis and treatment,
thus it's precise evaluation is crucially important. However, it's 3D nature
imposes several challenges, so the analysis is often performed on 2D
projections that reduces the complexity, but increases bias. On the other hand,
time consuming 3D evaluation, like, segmentation, is able to provide precise
estimation of a number of valuable spatial characteristics, giving us
understanding about the course of the disease.\newline Recent studies, focusing
on the segmentation task, report superior performance of Deep Learning methods
compared to classical computer vision algorithms. But still, it remains a
challenging problem. In this paper we present deep cascaded approach for
automatic brain tumor segmentation. Similar to recent methods for object
detection, our implementation is based on neural networks; we propose
modifications to the 3D UNet architecture and augmentation strategy to
efficiently handle multimodal MRI input, besides this we introduce approach to
enhance segmentation quality with context obtained from models of the same
topology operating on downscaled data. We evaluate presented approach on BraTS
2018 dataset and discuss results.
The combination of context information to enhance simple question answering
Authors: Zhaohui Chao, Lin Li
Date: 2018-10-09 00:00:00
Tasks: Fact Selection, Knowledge Base Question Answering, Question Answering

With the rapid development of knowledge base,question answering based on
knowledge base has been a hot research issue. In this paper, we focus on
answering singlerelation factoid questions based on knowledge base. We build a
question answering system and study the effect of context information on fact
selection, such as entity's notable type,outdegree. Experimental results show
that context information can improve the result of simple question answering.
Learning Noun Cases Using Sequential Neural Networks
Authors: Sina Ahmadi
Date: 2018-10-09 00:00:00
Tasks: Sentence

Morphological declension, which aims to inflect nouns to indicate number,
case and gender, is an important task in natural language processing (NLP).
This research proposal seeks to address the degree to which Recurrent Neural
Networks (RNNs) are efficient in learning to decline noun cases. Given the
challenge of data sparsity in processing morphologically rich languages and
also, the flexibility of sentence structures in such languages, we believe that
modeling morphological dependencies can improve the performance of neural
network models. It is suggested to carry out various experiments to understand
the interpretable features that may lead to a better generalization of the
learned models on cross-lingual tasks.
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
Authors: Parnia Bahar, Christopher Brix, Hermann Ney
Date: 2018-10-09 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, NMT, Translation, Vocal Bursts Valence Prediction

This work investigates an alternative model for neural machine translation
(NMT) and proposes a novel architecture, where we employ a multi-dimensional
long short-term memory (MDLSTM) for translation modeling. In the
state-of-the-art methods, source and target sentences are treated as
one-dimensional sequences over time, while we view translation as a
two-dimensional (2D) mapping using an MDLSTM layer to define the correspondence
between source and target words. We extend beyond the current sequence to
sequence backbone NMT models to a 2D structure in which the source and target
sentences are aligned with each other in a 2D grid. Our proposed topology shows
consistent improvements over attention-based sequence to sequence model on two
WMT 2017 tasks, German$\leftrightarrow$English.
Mathematics as information compression via the matching and unification of patterns
Authors: J. Gerard Wolff
Date: 2018-08-05 00:00:00

This paper describes a novel perspective on the foundations of mathematics:
how mathematics may be seen to be largely about 'information compression via
the matching and unification of patterns' (ICMUP). ICMUP is itself a novel
approach to information compression, couched in terms of non-mathematical
primitives, as is necessary in any investigation of the foundations of
mathematics. This new perspective on the foundations of mathematics has grown
out of an extensive programme of research developing the "SP Theory of
Intelligence" and its realisation in the "SP Computer Model", a system in which
a generalised version of ICMUP -- the powerful concept of SP-multiple-alignment
-- plays a central role. These ideas may be seen to be part of a "Big Picture"
comprising six areas of interest, with information compression as a unifying
theme. The paper describes the close relation between mathematics and
information compression, and describes examples showing how variants of ICMUP
may be seen in widely-used structures and operations in mathematics. Examples
are also given to show how the mathematics-related disciplines of logic and
computing may be understood as ICMUP. There are many potential benefits and
applications of these ideas.
Collective evolution of weights in wide neural networks
Authors: Dmitry Yarotsky
Date: 2018-10-09 00:00:00

We derive a nonlinear integro-differential transport equation describing
collective evolution of weights under gradient descent in large-width
neural-network-like models. We characterize stationary points of the evolution
and analyze several scenarios where the transport equation can be solved
approximately. We test our general method in the special case of linear
free-knot splines, and find good agreement between theory and experiment in
observations of global optima, stability of stationary points, and convergence
rates.
Deterministic Variational Inference for Robust Bayesian Neural Networks
Authors: Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
Date: 2018-10-09 00:00:00
Proceeding: ICLR 2019 5
Tasks: Variational Inference

Bayesian neural networks (BNNs) hold great promise as a flexible and
principled solution to deal with uncertainty when learning from finite data.
Among approaches to realize probabilistic inference in deep neural networks,
variational Bayes (VB) is theoretically grounded, generally applicable, and
computationally efficient. With wide recognition of potential advantages, why
is it that variational Bayes has seen very limited practical use for BNNs in
real applications? We argue that variational inference in neural networks is
fragile: successful implementations require careful initialization and tuning
of prior variances, as well as controlling the variance of Monte Carlo gradient
estimates. We provide two innovations that aim to turn VB into a robust
inference tool for Bayesian neural networks: first, we introduce a novel
deterministic method to approximate moments in neural networks, eliminating
gradient variance; second, we introduce a hierarchical prior for parameters and
a novel Empirical Bayes procedure for automatically selecting prior variances.
Combining these two innovations, the resulting method is highly efficient and
robust. On the application of heteroscedastic regression we demonstrate good
predictive performance over alternative approaches.
3D model silhouette-based tracking in depth images for puppet suit dynamic video-mapping
Authors: Guillaume Caron, Mounya Belghiti, Anthony Dessaux
Date: 2018-10-09 00:00:00
Tasks: Visual Tracking

Video-mapping is the process of coherent video-projection of images,
animations or movies on static objects or buildings for shows. This paper
focuses on the dynamic video-mapping of the suit of a puppet being moved by its
puppeteer on the theater stage. This may allow changing the costume dynamically
and simulate light interaction and more.
  Contrary to common video-mapping, the image warping cannot be done once,
offline, before the show. It must be done in real-time, and considering a
non-flat projection surface, so that the video-projected suit always maps
perfectly the puppet, automatically.
  Hence, we propose a new visual tracking method of articulated object, for the
puppet tracking, exploiting the silhouette of a 3D model of it, in the depth
images of a Kinect v2. Then, considering the precise calibration between the
latter and the video-projector, that we propose, coherent dynamic video-mapping
is made possible as the presented results show.
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
Authors: Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze
Date: 2018-10-09 00:00:00
Proceeding: ICLR 2019 5
Tasks: Information Extraction, Information Retrieval, Language Modelling, Retrieval, Topic Models, Word Embeddings

We address two challenges of probabilistic topic modelling in order to better
estimate the probability of a word in a given context, i.e., P(word|context):
(1) No Language Structure in Context: Probabilistic topic models ignore word
order by summarizing a given context as a "bag-of-word" and consequently the
semantics of words in the context is lost. The LSTM-LM learns a vector-space
representation of each word by accounting for word order in local collocation
patterns and models complex characteristics of language (e.g., syntax and
semantics), while the TM simultaneously learns a latent representation from the
entire document and discovers the underlying thematic structure. We unite two
complementary paradigms of learning the meaning of word occurrences by
combining a TM (e.g., DocNADE) and a LM in a unified probabilistic framework,
named as ctx-DocNADE. (2) Limited Context and/or Smaller training corpus of
documents: In settings with a small number of word occurrences (i.e., lack of
context) in short text or data sparsity in a corpus of few documents, the
application of TMs is challenging. We address this challenge by incorporating
external knowledge into neural autoregressive topic models via a language
modelling approach: we use word embeddings as input of a LSTM-LM with the aim
to improve the word-topic mapping on a smaller and/or short-text corpus. The
proposed DocNADE extension is named as ctx-DocNADEe.
  We present novel neural autoregressive topic model variants coupled with
neural LMs and embeddings priors that consistently outperform state-of-the-art
generative TMs in terms of generalization (perplexity), interpretability (topic
coherence) and applicability (retrieval and classification) over 6 long-text
and 8 short-text datasets from diverse domains.
Convolutional Neural Networks In Convolution
Authors: Xiaobo Huang
Date: 2018-10-09 00:00:00
Tasks: General Classification

Currently, increasingly deeper neural networks have been applied to improve
their accuracy. In contrast, We propose a novel wider Convolutional Neural
Networks (CNN) architecture, motivated by the Multi-column Deep Neural Networks
and the Network In Network(NIN), aiming for higher accuracy without input data
transmutation. In our architecture, namely "CNN In Convolution"(CNNIC), a small
CNN, instead of the original generalized liner model(GLM) based filters, is
convoluted as kernel on the original image, serving as feature extracting layer
of this networks. And further classifications are then carried out by a global
average pooling layer and a softmax layer. Dropout and orthonormal
initialization are applied to overcome training difficulties including slow
convergence and over-fitting. Persuasive classification performance is
demonstrated on MNIST.
Transfer Metric Learning: Algorithms, Applications and Outlooks
Authors: Yong Luo, Yonggang Wen, Ling-Yu Duan, DaCheng Tao
Date: 2018-10-09 00:00:00
Tasks: Metric Learning, Triplet

Distance metric learning (DML) aims to find an appropriate way to reveal the
underlying data relationship. It is critical in many machine learning, pattern
recognition and data mining algorithms, and usually require large amount of
label information (such as class labels or pair/triplet constraints) to achieve
satisfactory performance. However, the label information may be insufficient in
real-world applications due to the high-labeling cost, and DML may fail in this
case. Transfer metric learning (TML) is able to mitigate this issue for DML in
the domain of interest (target domain) by leveraging knowledge/information from
other related domains (source domains). Although achieved a certain level of
development, TML has limited success in various aspects such as selective
transfer, theoretical understanding, handling complex data, big data and
extreme cases. In this survey, we present a systematic review of the TML
literature. In particular, we group TML into different categories according to
different settings and metric transfer strategies, such as direct metric
approximation, subspace approximation, distance approximation, and distribution
approximation. A summarization and insightful discussion of the various TML
approaches and their applications will be presented. Finally, we indicate some
challenges and provide possible future directions.
Analyzing the Noise Robustness of Deep Neural Networks
Authors: Mengchen Liu, Shixia Liu, Hang Su, Kelei Cao, Jun Zhu
Date: 2018-10-09 00:00:00

Deep neural networks (DNNs) are vulnerable to maliciously generated
adversarial examples. These examples are intentionally designed by making
imperceptible perturbations and often mislead a DNN into making an incorrect
prediction. This phenomenon means that there is significant risk in applying
DNNs to safety-critical applications, such as driverless cars. To address this
issue, we present a visual analytics approach to explain the primary cause of
the wrong predictions introduced by adversarial examples. The key is to analyze
the datapaths of the adversarial examples and compare them with those of the
normal examples. A datapath is a group of critical neurons and their
connections. To this end, we formulate the datapath extraction as a subset
selection problem and approximately solve it based on back-propagation. A
multi-level visualization consisting of a segmented DAG (layer level), an Euler
diagram (feature map level), and a heat map (neuron level), has been designed
to help experts investigate datapaths from the high-level layers to the
detailed neuron activations. Two case studies are conducted that demonstrate
the promise of our approach in support of explaining the working mechanism of
adversarial examples.
Image Segmentation using Unsupervised Watershed Algorithm with an Over-segmentation Reduction Technique
Authors: Ravimal Bandara
Date: 2018-10-09 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is the process of partitioning an image into meaningful
segments. The meaning of the segments is subjective due to the definition of
homogeneity is varied based on the users perspective hence the automation of
the segmentation is challenging. Watershed is a popular segmentation technique
which assumes topographic map in an image, with the brightness of each pixel
representing its height, and finds the lines that run along the tops of ridges.
The results from the algorithm typically suffer from over segmentation due to
the lack of knowledge of the objects being classified. This paper presents an
approach to reduce the over segmentation of watershed algorithm by assuming
that the different adjacent segments of an object have similar color
distribution. The approach demonstrates an improvement over conventional
watershed algorithm.
Continual State Representation Learning for Reinforcement Learning using Generative Replay
Authors: Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Date: 2018-10-09 00:00:00
Tasks: Change Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

We consider the problem of building a state representation model in a
continual fashion. As the environment changes, the aim is to efficiently
compress the sensory state's information without losing past knowledge. The
learned features are then fed to a Reinforcement Learning algorithm to learn a
policy. We propose to use Variational Auto-Encoders for state representation,
and Generative Replay, i.e. the use of generated samples, to maintain past
knowledge. We also provide a general and statistically sound method for
automatic environment change detection. Our method provides efficient state
representation as well as forward transfer, and avoids catastrophic forgetting.
The resulting model is capable of incrementally learning information without
using past data and with a bounded system size.