pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Selective Recruitment in Linear-Threshold Brain Networks, Part II: Multi-Layer Dynamics and Top-Down Recruitment
Authors: Erfan Nozari, Jorge Cortés
Date: 2018-09-05 00:00:00

Goal-driven selective attention (GDSA) is a remarkable function that allows the complex dynamical networks of the brain to support coherent perception and cognition. Part I of this two-part paper proposes a new control-theoretic framework, termed hierarchical selective recruitment (HSR), to rigorously explain the emergence of GDSA from the brain's network structure and dynamics. This part completes the development of HSR by deriving conditions on the joint structure of the hierarchical subnetworks that guarantee top-down recruitment of the task-relevant part of each subnetwork by the subnetwork at the layer immediately above, while inhibiting the activity of task-irrelevant subnetworks at all the hierarchical layers. To further verify the merit and applicability of this framework, we carry out a comprehensive case study of selective listening in rodents and show that a small network with HSR-based structure and minimal size can explain the data with remarkable accuracy while satisfying the theoretical requirements of HSR. Our technical approach relies on the theory of switched systems and provides a novel converse Lyapunov theorem for state-dependent switched affine systems that is of independent interest.
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
Authors: Shivang Agarwal, Jean Ogier du Terrail, Frédéric Jurie
Date: 2018-09-10 00:00:00
Tasks: Object, object-detection, Object Detection, Survey

Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.
Unity: A General Platform for Intelligent Agents
Authors: Arthur Juliani, Vincent-Pierre Berges, Ervin Teng, Andrew Cohen, Jonathan Harper, Chris Elion, Chris Goy, Yuan Gao, Hunter Henry, Marwan Mattar, Danny Lange
Date: 2018-09-07 00:00:00
Tasks: Reinforcement Learning, Unity

Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments. However, many of the existing environments provide either unrealistic visuals, inaccurate physics, low task complexity, restricted agent perspective, or a limited capacity for interaction among artificial agents. Furthermore, many platforms lack the ability to flexibly configure the simulation, making the simulated environment a black-box from the perspective of the learning system. In this work, we propose a novel taxonomy of existing simulation platforms and discuss the highest level class of general platforms which enable the development of learning environments that are rich in visual, physical, task, and social complexity. We argue that modern game engines are uniquely suited to act as general platforms and as a case study examine the Unity engine and open source Unity ML-Agents Toolkit. We then survey the research enabled by Unity and the Unity ML-Agents Toolkit, discussing the kinds of research a flexible, interactive and easily configurable general platform can facilitate.
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
Authors: Deqing Wang, HUI ZHANG
Date: 2010-12-13 00:00:00
Tasks: Cross-corpus, Information Retrieval, Multi-class Classification, Retrieval, Text Categorization

Term weighting schemes often dominate the performance of many classifiers,
such as kNN, centroid-based classifier and SVMs. The widely used term weighting
scheme in text categorization, i.e., tf.idf, is originated from information
retrieval (IR) field. The intuition behind idf for text categorization seems
less reasonable than IR. In this paper, we introduce inverse category frequency
(icf) into term weighting scheme and propose two novel approaches, i.e., tf.icf
and icf-based supervised term weighting schemes. The tf.icf adopts icf to
substitute idf factor and favors terms occurring in fewer categories, rather
than fewer documents. And the icf-based approach combines icf and relevance
frequency (rf) to weight terms in a supervised way. Our cross-classifier and
cross-corpus experiments have shown that our proposed approaches are superior
or comparable to six supervised term weighting schemes and three traditional
schemes in terms of macro-F1 and micro-F1.
Multi-view Models for Political Ideology Detection of News Articles
Authors: Vivek Kulkarni, Junting Ye, Steven Skiena, William Yang Wang
Date: 2018-09-10 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Representation Learning

A news article's title, content and link structure often reveal its political
ideology. However, most existing works on automatic political ideology
detection only leverage textual cues. Drawing inspiration from recent advances
in neural inference, we propose a novel attention based multi-view model to
leverage cues from all of the above views to identify the ideology evinced by a
news article. Our model draws on advances in representation learning in natural
language processing and network science to capture cues from both textual
content and the network structure of news articles. We empirically evaluate our
model against a battery of baselines and show that our model outperforms state
of the art by 10 percentage points F1 score.
Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
Authors: Wei Zhan, Liting Sun, Yeping Hu, Jiachen Li, Masayoshi Tomizuka
Date: 2018-09-10 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Reinforcement Learning

Autonomous vehicles should be able to generate accurate probabilistic
predictions for uncertain behavior of other road users. Moreover, reactive
predictions are necessary in highly interactive driving scenarios to answer
"what if I take this action in the future" for autonomous vehicles. There is no
existing unified framework to homogenize the problem formulation,
representation simplification, and evaluation metric for various prediction
methods, such as probabilistic graphical models (PGM), neural networks (NN) and
inverse reinforcement learning (IRL). In this paper, we formulate a
probabilistic reaction prediction problem, and reveal the relationship between
reaction and situation prediction problems. We employ prototype trajectories
with designated motion patterns other than "intention" to homogenize the
representation so that probabilities corresponding to each trajectory generated
by different methods can be evaluated. We also discuss the reasons why
"intention" is not suitable to serve as a motion indicator in highly
interactive scenarios. We propose to use Brier score as the baseline metric for
evaluation. In order to reveal the fatality of the consequences when the
predictions are adopted by decision-making and planning, we propose a
fatality-aware metric, which is a weighted Brier score based on the criticality
of the trajectory pairs of the interacting entities. Conservatism and
non-defensiveness are defined from the weighted Brier score to indicate the
consequences caused by inaccurate predictions. Modified methods based on PGM,
NN and IRL are provided to generate probabilistic reaction predictions in an
exemplar scenario of nudging from a highway ramp. The results are evaluated by
the baseline and proposed metrics to construct a mini benchmark. Analysis on
the properties of each method is also provided by comparing the baseline and
proposed metric scores.
Universal Multi-Party Poisoning Attacks
Authors: Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed
Date: 2018-09-10 00:00:00

In this work, we demonstrate universal multi-party poisoning attacks that adapt and apply to any multi-party learning process with arbitrary interaction pattern between the parties. More generally, we introduce and study $(k,p)$-poisoning attacks in which an adversary controls $k\in[m]$ of the parties, and for each corrupted party $P_i$, the adversary submits some poisoned data $\mathcal{T}'_i$ on behalf of $P_i$ that is still ``$(1-p)$-close'' to the correct data $\mathcal{T}_i$ (e.g., $1-p$ fraction of $\mathcal{T}'_i$ is still honestly generated). We prove that for any ``bad'' property $B$ of the final trained hypothesis $h$ (e.g., $h$ failing on a particular test example or having ``large'' risk) that has an arbitrarily small constant probability of happening without the attack, there always is a $(k,p)$-poisoning attack that increases the probability of $B$ from $\mu$ to by $\mu^{1-p \cdot k/m} = \mu + \Omega(p \cdot k/m)$. Our attack only uses clean labels, and it is online. More generally, we prove that for any bounded function $f(x_1,\dots,x_n) \in [0,1]$ defined over an $n$-step random process $\mathbf{X} = (x_1,\dots,x_n)$, an adversary who can override each of the $n$ blocks with even dependent probability $p$ can increase the expected output by at least $\Omega(p \cdot \mathrm{Var}[f(\mathbf{x})])$.
ViZDoom Competitions: Playing Doom from Pixels
Authors: Marek Wydmuch, Michał Kempka, Wojciech Jaśkowski
Date: 2018-09-10 00:00:00
Tasks: Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents the first two editions of Visual Doom AI Competition,
held in 2016 and 2017. The challenge was to create bots that compete in a
multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots
had to make their decisions based solely on visual information, i.e., a raw
screen buffer. To play well, the bots needed to understand their surroundings,
navigate, explore, and handle the opponents at the same time. These aspects,
together with the competitive multi-agent aspect of the game, make the
competition a unique platform for evaluating the state of the art reinforcement
learning algorithms. The paper discusses the rules, solutions, results, and
statistics that give insight into the agents' behaviors. Best-performing agents
are described in more detail. The results of the competition lead to the
conclusion that, although reinforcement learning can produce capable Doom bots,
they still are not yet able to successfully compete against humans in this
game. The paper also revisits the ViZDoom environment, which is a flexible,
easy to use, and efficient 3D platform for research for vision-based
reinforcement learning, based on a well-recognized first-person perspective
game Doom.
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models
Authors: Xiu Yang, Guzel Tartakovsky, Alexandre Tartakovsky
Date: 2018-09-10 00:00:00
Tasks: Active Learning, GPR

In this work, we propose a new Gaussian process regression (GPR) method: physics information aided Kriging (PhIK). In the standard data-driven Kriging, the unknown function of interest is usually treated as a Gaussian process with assumed stationary covariance with hyperparameters estimated from data. In PhIK, we compute the mean and covariance function from realizations of available stochastic models, e.g., from realizations of governing stochastic partial differential equations solutions. Such constructed Gaussian process generally is non-stationary, and does not assume a specific form of the covariance function. Our approach avoids the optimization step in data-driven GPR methods to identify the hyperparameters. More importantly, we prove that the physical constraints in the form of a deterministic linear operator are guaranteed in the resulting prediction. We also provide an error estimate in preserving the physical constraints when errors are included in the stochastic model realizations. To reduce the computational cost of obtaining stochastic model realizations, we propose a multilevel Monte Carlo estimate of the mean and covariance functions. Further, we present an active learning algorithm that guides the selection of additional observation locations. The efficiency and accuracy of PhIK are demonstrated for reconstructing a partially known modified Branin function, studying a three-dimensional heat transfer problem and learning a conservative tracer distribution from sparse concentration measurements.
Deep Single-View 3D Object Reconstruction with Visual Hull Embedding
Authors: Hanqing Wang, Jiaolong Yang, Wei Liang, Xin Tong
Date: 2018-09-10 00:00:00
Tasks: 3D Object Reconstruction, Object, Object Reconstruction, Pose Estimation

3D object reconstruction is a fundamental task of many robotics and AI
problems. With the aid of deep convolutional neural networks (CNNs), 3D object
reconstruction has witnessed a significant progress in recent years. However,
possibly due to the prohibitively high dimension of the 3D object space, the
results from deep CNNs are often prone to missing some shape details. In this
paper, we present an approach which aims to preserve more shape details and
improve the reconstruction quality. The key idea of our method is to leverage
object mask and pose estimation from CNNs to assist the 3D shape learning by
constructing a probabilistic single-view visual hull inside of the network. Our
method works by first predicting a coarse shape as well as the object pose and
silhouette using CNNs, followed by a novel 3D refinement CNN which refines the
coarse shapes using the constructed probabilistic visual hulls. Experiment on
both synthetic data and real images show that embedding a single-view visual
hull for shape refinement can significantly improve the reconstruction quality
by recovering more shapes details and improving shape consistency with the
input image.
Explicit Utilization of General Knowledge in Machine Reading Comprehension
Authors: Chao Wang, Hui Jiang
Date: 2018-09-10 00:00:00
Proceeding: ACL 2019 7
Tasks: General Knowledge, Machine Reading Comprehension, Question Answering, Reading Comprehension

To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. When only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise.
Expert-augmented actor-critic for ViZDoom and Montezumas Revenge
Authors: Michał Garmulewicz, Henryk Michalewski, Piotr Miłoś
Date: 2018-09-10 00:00:00

We propose an expert-augmented actor-critic algorithm, which we evaluate on
two environments with sparse rewards: Montezumas Revenge and a demanding maze
from the ViZDoom suite. In the case of Montezumas Revenge, an agent trained
with our method achieves very good results consistently scoring above 27,000
points (in many experiments beating the first world). With an appropriate
choice of hyperparameters, our algorithm surpasses the performance of the
expert data. In a number of experiments, we have observed an unreported bug in
Montezumas Revenge which allowed the agent to score more than 800,000 points.
Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration
Authors: Jun Zhang
Date: 2018-09-10 00:00:00
Tasks: Anatomical Landmark Detection, Image Registration, Medical Image Analysis

Deformable image registration is a fundamental task in medical image
analysis, aiming to establish a dense and non-linear correspondence between a
pair of images. Previous deep-learning studies usually employ supervised neural
networks to directly learn the spatial transformation from one image to
another, requiring task-specific ground-truth registration for model training.
Due to the difficulty in collecting precise ground-truth registration,
implementation of these supervised methods is practically challenging. Although
several unsupervised networks have been recently developed, these methods
usually ignore the inherent inverse-consistent property (essential for
diffeomorphic mapping) of transformations between a pair of images. Also,
existing approaches usually encourage the to-be-estimated transformation to be
locally smooth via a smoothness constraint only, which could not completely
avoid folding in the resulting transformation. To this end, we propose an
Inverse-Consistent deep Network (ICNet) for unsupervised deformable image
registration. Specifically, we develop an inverse-consistent constraint to
encourage that a pair of images are symmetrically deformed toward one another,
until both warped images are matched. Besides using the conventional smoothness
constraint, we also propose an anti-folding constraint to further avoid folding
in the transformation. The proposed method does not require any supervision
information, while encouraging the diffeomoprhic property of the transformation
via the proposed inverse-consistent and anti-folding constraints. We evaluate
our method on T1-weighted brain magnetic resonance imaging (MRI) scans for
tissue segmentation and anatomical landmark detection, with results
demonstrating the superior performance of our ICNet over several
state-of-the-art approaches for deformable image registration. Our code will be
made publicly available.
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
Authors: Ji Wang, Jian-Guo Zhang, Weidong Bao, Xiaomin Zhu, Bokai Cao, Philip S. Yu
Date: 2018-09-10 00:00:00
Tasks: Privacy Preserving

The increasing demand for on-device deep learning services calls for a highly
efficient manner to deploy deep neural networks (DNNs) on mobile devices with
limited capacity. The cloud-based solution is a promising approach to enabling
deep learning applications on mobile devices where the large portions of a DNN
are offloaded to the cloud. However, revealing data to the cloud leads to
potential privacy risk. To benefit from the cloud data center without the
privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN
which partitions the DNN across mobile devices and cloud data centers. A simple
data transformation is performed on the mobile device, while the
resource-hungry training and the complex inference rely on the cloud data
center. To protect the sensitive information, a lightweight privacy-preserving
mechanism consisting of arbitrary data nullification and random noise addition
is introduced, which provides strong privacy guarantee. A rigorous privacy
budget analysis is given. Nonetheless, the private perturbation to the original
data inevitably has a negative impact on the performance of further inference
on the cloud side. To mitigate this influence, we propose a noisy training
method to enhance the cloud-side network robustness to perturbed data. Through
the sophisticated design, ARDEN can not only preserve privacy but also improve
the inference performance. To validate the proposed ARDEN, a series of
experiments based on three image datasets and a real mobile application are
conducted. The experimental results demonstrate the effectiveness of ARDEN.
Finally, we implement ARDEN on a demo system to verify its practicality.
Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations
Authors: Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Menuka Warushavithana, Viraj Gamage, Amal Shehan Perera
Date: 2018-09-10 00:00:00

Case Law has a significant impact on the proceedings of legal cases.
Therefore, the information that can be obtained from previous court cases is
valuable to lawyers and other legal officials when performing their duties.
This paper describes a methodology of applying discourse relations between
sentences when processing text documents related to the legal domain. In this
study, we developed a mechanism to classify the relationships that can be
observed among sentences in transcripts of United States court cases. First, we
defined relationship types that can be observed between sentences in court case
transcripts. Then we classified pairs of sentences according to the
relationship type by combining a machine learning model and a rule-based
approach. The results obtained through our system were evaluated using human
judges. To the best of our knowledge, this is the first study where discourse
relationships between sentences have been used to determine relationships among
sentences in legal court case transcripts.
Monocular Object and Plane SLAM in Structured Environments
Authors: Shichao Yang, Sebastian Scherer
Date: 2018-09-10 00:00:00
Tasks: Camera Localization, Object, Simultaneous Localization and Mapping

In this paper, we present a monocular Simultaneous Localization and Mapping (SLAM) algorithm using high-level object and plane landmarks. The built map is denser, more compact and semantic meaningful compared to feature point based SLAM. We first propose a high order graphical model to jointly infer the 3D object and layout planes from single images considering occlusions and semantic constraints. The extracted objects and planes are further optimized with camera poses in a unified SLAM framework. Objects and planes can provide more semantic constraints such as Manhattan plane and object supporting relationships compared to points. Experiments on various public and collected datasets including ICL NUIM and TUM Mono show that our algorithm can improve camera localization accuracy compared to state-of-the-art SLAM especially when there is no loop closure, and also generate dense maps robustly in many structured environments.
Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations
Authors: Koki Washio, Tsuneaki Kato
Date: 2018-09-10 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentence

Recognizing lexical semantic relations between word pairs is an important
task for many applications of natural language processing. One of the
mainstream approaches to this task is to exploit the lexico-syntactic paths
connecting two target words, which reflect the semantic relations of word
pairs. However, this method requires that the considered words co-occur in a
sentence. This requirement is hardly satisfied because of Zipf's law, which
states that most content words occur very rarely. In this paper, we propose
novel methods with a neural model of $P(path|w_1, w_2)$ to solve this problem.
Our proposed model of $P(path|w_1, w_2)$ can be learned in an unsupervised
manner and can generalize the co-occurrences of word pairs and dependency
paths. This model can be used to augment the path data of word pairs that do
not co-occur in the corpus, and extract features capturing relational
information from word pairs. Our experimental results demonstrate that our
methods improve on previous neural approaches based on dependency paths and
successfully solve the focused problem.