pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
Authors: Sonali Parbhoo, Mario Wieser, Volker Roth
Date: 2018-11-26 00:00:00
Tasks: Causal Inference

Estimating the causal effects of an intervention in the presence of
confounding is a frequently occurring problem in applications such as medicine.
The task is challenging since there may be multiple confounding factors, some
of which may be missing, and inferences must be made from high-dimensional,
noisy measurements. In this paper, we propose a decision-theoretic approach to
estimate the causal effects of interventions where a subset of the covariates
is unavailable for some patients during testing. Our approach uses the
information bottleneck principle to perform a discrete, low-dimensional
sufficient reduction of the covariate data to estimate a distribution over
confounders. In doing so, we can estimate the causal effect of an intervention
where only partial covariate information is available. Our results on a causal
inference benchmark and a real application for treating sepsis show that our
method achieves state-of-the-art performance, without sacrificing
interpretability.
Matchable Image Retrieval by Learning from Surface Reconstruction
Authors: Tianwei Shen, Zixin Luo, Lei Zhou, Runze Zhang, Siyu Zhu, Tian Fang, Long Quan
Date: 2018-11-26 00:00:00
Tasks: 3D Reconstruction, Image Retrieval, Retrieval, Surface Reconstruction, Triplet

Convolutional Neural Networks (CNNs) have achieved superior performance on
object image retrieval, while Bag-of-Words (BoW) models with handcrafted local
features still dominate the retrieval of overlapping images in 3D
reconstruction. In this paper, we narrow down this gap by presenting an
efficient CNN-based method to retrieve images with overlaps, which we refer to
as the matchable image retrieval problem. Different from previous methods that
generates training data based on sparse reconstruction, we create a large-scale
image database with rich 3D geometrics and exploit information from surface
reconstruction to obtain fine-grained training data. We propose a batched
triplet-based loss function combined with mesh re-projection to effectively
learn the CNN representation. The proposed method significantly accelerates the
image retrieval process in 3D reconstruction and outperforms the
state-of-the-art CNN-based and BoW methods for matchable image retrieval. The
code and data are available at https://github.com/hlzz/mirror.
Universal Semi-Supervised Semantic Segmentation
Authors: Tarun Kalluri, Girish Varma, Manmohan Chandraker, C. V. Jawahar
Date: 2018-11-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation, Unsupervised Domain Adaptation

In recent years, the need for semantic segmentation has arisen across several different applications and environments. However, the expense and redundancy of annotation often limits the quantity of labels available for training in any domain, while deployment is easier if a single model works well across domains. In this paper, we pose the novel problem of universal semi-supervised semantic segmentation and propose a solution framework, to meet the dual needs of lower annotation and deployment costs. In contrast to counterpoints such as fine tuning, joint training or unsupervised domain adaptation, universal semi-supervised segmentation ensures that across all domains: (i) a single model is deployed, (ii) unlabeled data is used, (iii) performance is improved, (iv) only a few labels are needed and (v) label spaces may differ. To address this, we minimize supervised as well as within and cross-domain unsupervised losses, introducing a novel feature alignment objective based on pixel-aware entropy regularization for the latter. We demonstrate quantitative advantages over other approaches on several combinations of segmentation datasets across different geographies (Germany, England, India) and environments (outdoors, indoors), as well as qualitative insights on the aligned representations.
A Differential Topological View of Challenges in Learning with Feedforward Neural Networks
Authors: Hao Shen
Date: 2018-11-26 00:00:00
Tasks: Representation Learning

Among many unsolved puzzles in theories of Deep Neural Networks (DNNs), there
are three most fundamental challenges that highly demand solutions, namely,
expressibility, optimisability, and generalisability. Although there have been
significant progresses in seeking answers using various theories, e.g.
information bottleneck theory, sparse representation, statistical inference,
Riemannian geometry, etc., so far there is no single theory that is able to
provide solutions to all these challenges. In this work, we propose to engage
the theory of differential topology to address the three problems. By modelling
the dataset of interest as a smooth manifold, DNNs can be considered as
compositions of smooth maps between smooth manifolds. Specifically, our work
offers a differential topological view of loss landscape of DNNs, interplay
between width and depth in expressibility, and regularisations for
generalisability. Finally, in the setting of deep representation learning, we
further apply the quotient topology to investigate the architecture of DNNs,
which enables to capture nuisance factors in data with respect to a specific
learning task.
Multi-hierarchical Independent Correlation Filters for Visual Tracking
Authors: Shuai Bai, Zhiqun He, Ting-Bing Xu, Zheng Zhu, Yuan Dong, Hongliang Bai
Date: 2018-11-26 00:00:00
Tasks: Motion Estimation, Object Tracking, Visual Object Tracking, Visual Tracking

For visual tracking, most of the traditional correlation filters (CF) based
methods suffer from the bottleneck of feature redundancy and lack of motion
information. In this paper, we design a novel tracking framework, called
multi-hierarchical independent correlation filters (MHIT). The framework
consists of motion estimation module, hierarchical features selection,
independent CF online learning, and adaptive multi-branch CF fusion.
Specifically, the motion estimation module is introduced to capture motion
information, which effectively alleviates the object partial occlusion in the
temporal video. The multi-hierarchical deep features of CNN representing
different semantic information can be fully excavated to track multi-scale
objects. To better overcome the deep feature redundancy, each hierarchical
features are independently fed into a single branch to implement the online
learning of parameters. Finally, an adaptive weight scheme is integrated into
the framework to fuse these independent multi-branch CFs for the better and
more robust visual object tracking. Extensive experiments on OTB and VOT
datasets show that the proposed MHIT tracker can significantly improve the
tracking performance. Especially, it obtains a 20.1% relative performance gain
compared to the top trackers on the VOT2017 challenge, and also achieves new
state-of-the-art performance on the VOT2018 challenge.
A Rule-based Kurdish Text Transliteration System
Authors: Sina Ahmadi
Date: 2018-11-26 00:00:00
Tasks: Transliteration

In this article, we present a rule-based approach for transliterating two
mostly used orthographies in Sorani Kurdish. Our work consists of detecting a
character in a word by removing the possible ambiguities and mapping it into
the target orthography. We describe different challenges in Kurdish text mining
and propose novel ideas concerning the transliteration task for Sorani Kurdish.
Our transliteration system, named Wergor, achieves 82.79% overall precision and
more than 99% in detecting the double-usage characters. We also present a
manually transliterated corpus for Kurdish.
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Authors: Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant
Date: 2018-11-26 00:00:00
Tasks: Two-sample testing

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
Authors: Francois-Xavier Briol, Chris. J. Oates, Mark Girolami, Michael A. Osborne, Dino Sejdinovic
Date: 2018-11-26 00:00:00

This article is the rejoinder for the paper "Probabilistic Integration: A
Role in Statistical Computation?" to appear in Statistical Science with
discussion. We would first like to thank the reviewers and many of our
colleagues who helped shape this paper, the editor for selecting our paper for
discussion, and of course all of the discussants for their thoughtful,
insightful and constructive comments. In this rejoinder, we respond to some of
the points raised by the discussants and comment further on the fundamental
questions underlying the paper: (i) Should Bayesian ideas be used in numerical
analysis?, and (ii) If so, what role should such approaches have in statistical
computation?
PNS: Population-Guided Novelty Search for Reinforcement Learning in Hard Exploration Environments
Authors: Qihao Liu, Yujia Wang, Xiaofeng Liu
Date: 2018-11-26 00:00:00
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) has made remarkable achievements, but it still suffers from inadequate exploration strategies, sparse reward signals, and deceptive reward functions. To alleviate these problems, a Population-guided Novelty Search (PNS) parallel learning method is proposed in this paper. In PNS, the population is divided into multiple sub-populations, each of which has one chief agent and several exploring agents. The chief agent evaluates the policies learned by exploring agents and shares the optimal policy with all sub-populations. The exploring agents learn their policies in collaboration with the guidance of the optimal policy and, simultaneously, upload their policies to the chief agent. To balance exploration and exploitation, the Novelty Search (NS) is employed in every chief agent to encourage policies with high novelty while maximizing per-episode performance. We apply PNS to the twin delayed deep deterministic (TD3) policy gradient algorithm. The effectiveness of PNS to promote exploration and improve performance in continuous control domains is demonstrated in the experimental section. Notably, PNS-TD3 achieves rewards that far exceed the SOTA methods in environments with sparse or delayed reward signals. We also demonstrate that PNS enables robotic agents to learn control policies directly from pixels for sparse-reward manipulation in both simulated and real-world settings.
Region Based Extensive Response Index Pattern for Facial Expression Recognition
Authors: Monu Verma, Santosh. K. Vipparthi, Girdhari Singh
Date: 2018-11-26 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

This paper presents a novel descriptor named Region based Extensive Response
Index Pattern (RETRaIN) for facial expression recognition. The RETRaIN encodes
the relation among the reference and neighboring pixels of facial active
regions. These relations are computed by using directional compass mask on an
input image and extract the high edge responses in foremost directions. Further
extreme edge index positions are selected and encoded into six-bit compact code
to reduce feature dimensionality and distinguish between the uniform and
non-uniform patterns in the facial features. The performance of the proposed
descriptor is tested and evaluated on three benchmark datasets Extended Cohn
Kanade, JAFFE, and MUG. The RETRaIN achieves superior recognition accuracy in
comparison to state-of-the-art techniques.
Generalised Differential Privacy for Text Document Processing
Authors: Natasha Fernandes, Mark Dras, Annabelle McIver
Date: 2018-11-26 00:00:00
Tasks: Authorship Attribution, BIG-bench Machine Learning, General Classification, Semantic Similarity, Semantic Textual Similarity

We address the problem of how to "obfuscate" texts by removing stylistic
clues which can identify authorship, whilst preserving (as much as possible)
the content of the text. In this paper we combine ideas from "generalised
differential privacy" and machine learning techniques for text processing to
model privacy for text documents. We define a privacy mechanism that operates
at the level of text documents represented as "bags-of-words" - these
representations are typical in machine learning and contain sufficient
information to carry out many kinds of classification tasks including topic
identification and authorship attribution (of the original documents). We show
that our mechanism satisfies privacy with respect to a metric for semantic
similarity, thereby providing a balance between utility, defined by the
semantic content of texts, with the obfuscation of stylistic clues. We
demonstrate our implementation on a "fan fiction" dataset, confirming that it
is indeed possible to disguise writing style effectively whilst preserving
enough information and variation for accurate content classification tasks.
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization
Authors: Zengyi Qin, Jinglu Wang, Yan Lu
Date: 2018-11-26 00:00:00
Tasks: 2D Object Detection, 3D Object Detection, Depth Estimation, Monocular 3D Object Detection, Monocular 3D Object Localization, object-detection, Object Detection, Object Localization, Scene Understanding

Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a single RGB image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object detection from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension. MonoGRNet is a single, unified network composed of four task-specific subnetworks, responsible for 2D object detection, instance depth estimation (IDE), 3D localization and local corner regression. Unlike the pixel-level depth estimation that needs per-pixel annotations, we propose a novel IDE method that directly predicts the depth of the targeting 3D bounding box's center using sparse supervision. The 3D localization is further achieved by estimating the position in the horizontal and vertical dimensions. Finally, MonoGRNet is jointly learned by optimizing the locations and poses of the 3D bounding boxes in the global context. We demonstrate that MonoGRNet achieves state-of-the-art performance on challenging datasets.
Brain-inspired robust delineation operator
Authors: Nicola Strisciuglio, George Azzopardi, Nicolai Petkov
Date: 2018-11-26 00:00:00

In this paper we present a novel filter, based on the existing COSFIRE
filter, for the delineation of patterns of interest. It includes a mechanism of
push-pull inhibition that improves robustness to noise in terms of spurious
texture. Push-pull inhibition is a phenomenon that is observed in neurons in
area V1 of the visual cortex, which suppresses the response of certain simple
cells for stimuli of preferred orientation but of non-preferred contrast. This
type of inhibition allows for sharper detection of the patterns of interest and
improves the quality of delineation especially in images with spurious texture.
  We performed experiments on images from different applications, namely the
detection of rose stems for automatic gardening, the delineation of cracks in
pavements and road surfaces, and the segmentation of blood vessels in retinal
images. Push-pull inhibition helped to improve results considerably in all
applications.
Learning Latent Beliefs and Performing Epistemic Reasoning for Efficient and Meaningful Dialog Management
Authors: Aishwarya Chhabra, Pratik Saini, Amit Sangroya, C. Anantaram
Date: 2018-11-26 00:00:00
Tasks: Dialogue Management, Epistemic Reasoning, Management

Many dialogue management frameworks allow the system designer to directly define belief rules to implement an efficient dialog policy. Because these rules are directly defined, the components are said to be hand-crafted. As dialogues become more complex, the number of states, transitions, and policy decisions becomes very large. To facilitate the dialog policy design process, we propose an approach to automatically learn belief rules using a supervised machine learning approach. We validate our ideas in Student-Advisor conversation domain, where we extract latent beliefs like student is curious, confused and neutral, etc. Further, we also perform epistemic reasoning that helps to tailor the dialog according to student's emotional state and hence improve the overall effectiveness of the dialog system. Our latent belief identification approach shows an accuracy of 87% and this results in efficient and meaningful dialog management.
Augmenting Robot Knowledge Consultants with Distributed Short Term Memory
Authors: Tom Williams, Ravenna Thielstrom, Evan Krause, Bradley Oosterveld, Matthias Scheutz
Date: 2018-11-26 00:00:00
Tasks: Referring Expression, Referring expression generation

Human-robot communication in situated environments involves a complex
interplay between knowledge representations across a wide variety of
modalities. Crucially, linguistic information must be associated with
representations of objects, locations, people, and goals, which may be
represented in very different ways. In previous work, we developed a Consultant
Framework that facilitates modality-agnostic access to information distributed
across a set of heterogeneously represented knowledge sources. In this work, we
draw inspiration from cognitive science to augment these distributed knowledge
sources with Short Term Memory Buffers to create an STM-augmented algorithm for
referring expression generation. We then discuss the potential performance
benefits of this approach and insights from cognitive science that may inform
future refinements in the design of our approach.
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos
Authors: Itamar Ben-Ari, Ravid Shwartz-Ziv
Date: 2018-11-26 00:00:00
Tasks: Anomaly Detection, Common Sense Reasoning, Decoder

We propose a semi-supervised model for detecting anomalies in videos
inspiredby the Video Pixel Network [van den Oord et al., 2016]. VPN is a
probabilisticgenerative model based on a deep neural network that estimates the
discrete jointdistribution of raw pixels in video frames. Our model extends the
Convolutional-LSTM video encoder part of the VPN with a novel convolutional
based attentionmechanism. We also modify the Pixel-CNN decoder part of the VPN
to a frameinpainting task where a partially masked version of the frame to
predict is given asinput. The frame reconstruction error is used as an anomaly
indicator. We test ourmodel on a modified version of the moving mnist dataset
[Srivastava et al., 2015]. Our model is shown to be effective in detecting
anomalies in videos. This approachcould be a component in applications
requiring visual common sense.
A novel particle swarm optimizer with multi-stage transformation and genetic operation for VLSI routing
Authors: Genggeng Liu, Zhen Zhuang, Wenzhong Guo, Naixue Xiong, Guolong Chen
Date: 2018-11-26 00:00:00

As the basic model for very large scale integration (VLSI) routing, the
Steiner minimal tree (SMT) can be used in various practical problems, such as
wire length optimization, congestion, and time delay estimation. In this paper,
a novel particle swarm optimization (PSO) algorithm based on multi-stage
transformation and genetic operation is presented to construct two types of
SMT, including non-Manhattan SMT and Manhattan SMT. Firstly, in order to be
able to handle two types of SMT problems at the same time, an effective
edge-vertex encoding strategy is proposed. Secondly, a multi-stage
transformation strategy is proposed to both expand the algorithm search space
and ensure the effective convergence. We have tested three types from two to
four stages and various combinations under each type to highlight the best
combination. Thirdly, the genetic operators combined with union-find partition
are designed to construct the discrete particle update formula for discrete
VLSI routing. Moreover, in order to introduce uncertainty and diversity into
the search of PSO algorithm, we propose an improved mutation operation with
edge transformation. Experimental results show that our algorithm from a global
perspective of multilayer structure can achieve the best solution quality among
the existing algorithms. Finally, to our best knowledge, it is the first work
to address both manhattan and non-manhattan routing at the same time.
A Survey on Joint Object Detection and Pose Estimation using Monocular Vision
Authors: Aniruddha V Patil, Pankaj Rabha
Date: 2018-11-26 00:00:00
Tasks: Clustering, object-detection, Object Detection, Pose Estimation

In this survey we present a complete landscape of joint object detection and
pose estimation methods that use monocular vision. Descriptions of traditional
approaches that involve descriptors or models and various estimation methods
have been provided. These descriptors or models include chordiograms,
shape-aware deformable parts model, bag of boundaries, distance transform
templates, natural 3D markers and facet features whereas the estimation methods
include iterative clustering estimation, probabilistic networks and iterative
genetic matching. Hybrid approaches that use handcrafted feature extraction
followed by estimation by deep learning methods have been outlined. We have
investigated and compared, wherever possible, pure deep learning based
approaches (single stage and multi stage) for this problem. Comprehensive
details of the various accuracy measures and metrics have been illustrated. For
the purpose of giving a clear overview, the characteristics of relevant
datasets are discussed. The trends that prevailed from the infancy of this
problem until now have also been highlighted.
Multi-task Learning over Graph Structures
Authors: Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung
Date: 2018-11-26 00:00:00
Tasks: General Classification, Multi-Task Learning, text-classification, Text Classification, Transfer Learning

We present two architectures for multi-task learning with neural sequence
models. Our approach allows the relationships between different tasks to be
learned dynamically, rather than using an ad-hoc pre-defined structure as in
previous work. We adopt the idea from message-passing graph neural networks and
propose a general \textbf{graph multi-task learning} framework in which
different tasks can communicate with each other in an effective and
interpretable way. We conduct extensive experiments in text classification and
sequence labeling to evaluate our approach on multi-task learning and transfer
learning. The empirical results show that our models not only outperform
competitive baselines but also learn interpretable and transferable patterns
across tasks.