pwc-archive/papers-with-abstracts huggingface dataset:
Intelligent Autonomous Things on the Battlefield
Authors: Alexander Kott, Ethan Stump
Date: 2019-02-26 00:00:00
Tasks: BIG-bench Machine Learning

Numerous, artificially intelligent, networked things will populate the
battlefield of the future, operating in close collaboration with human
warfighters, and fighting as teams in highly adversarial environments. This
chapter explores the characteristics, capabilities and intelli-gence required
of such a network of intelligent things and humans - Internet of Battle Things
(IOBT). The IOBT will experience unique challenges that are not yet well
addressed by the current generation of AI and machine learning.
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
Authors: Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman
Date: 2019-02-26 00:00:00
Tasks: Gaussian Processes, State Space Models, Variational Inference

Banded matrices can be used as precision matrices in several models including
linear state-space models, some Gaussian processes, and Gaussian Markov random
fields. The aim of the paper is to make modern inference methods (such as
variational inference or gradient-based sampling) available for Gaussian models
with banded precision. We show that this can efficiently be achieved by
equipping an automatic differentiation framework, such as TensorFlow or
PyTorch, with some linear algebra operators dedicated to banded matrices. This
paper studies the algorithmic aspects of the required operators, details their
reverse-mode derivatives, and show that their complexity is linear in the
number of observations.
Entity Recognition at First Sight: Improving NER with Eye Movement Information
Authors: Nora Hollenstein, Ce Zhang
Date: 2019-02-26 00:00:00
Proceeding: NAACL 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Previous research shows that eye-tracking data contains information about the
lexical and syntactic properties of text, which can be used to improve natural
language processing models. In this work, we leverage eye movement features
from three corpora with recorded gaze information to augment a state-of-the-art
neural model for named entity recognition (NER) with gaze embeddings. These
corpora were manually annotated with named entity labels. Moreover, we show how
gaze features, generalized on word type level, eliminate the need for recorded
eye-tracking data at test time. The gaze-augmented models for NER using
token-level and type-level features outperform the baselines. We present the
benefits of eye-tracking features by evaluating the NER models on both
individual datasets as well as in cross-domain settings.
MRS-VPR: a multi-resolution sampling based global visual place recognition method
Authors: Peng Yin, Rangaprasad Arun Srivatsan, Yin Chen, Xueqian Li, Hongda Zhang, Lingyun Xu, Lu Li, Zhenzhong Jia, Jianmin Ji, Yuqing He
Date: 2019-02-26 00:00:00
Tasks: Loop Closure Detection, Visual Navigation, Visual Place Recognition

Place recognition and loop closure detection are challenging for long-term
visual navigation tasks. SeqSLAM is considered to be one of the most successful
approaches to achieving long-term localization under varying environmental
conditions and changing viewpoints. It depends on a brute-force, time-consuming
sequential matching method. We propose MRS-VPR, a multi-resolution,
sampling-based place recognition method, which can significantly improve the
matching efficiency and accuracy in sequential matching. The novelty of this
method lies in the coarse-to-fine searching pipeline and a particle
filter-based global sampling scheme, that can balance the matching efficiency
and accuracy in the long-term navigation task. Moreover, our model works much
better than SeqSLAM when the testing sequence has a much smaller scale than the
reference sequence. Our experiments demonstrate that the proposed method is
efficient in locating short temporary trajectories within long-term reference
ones without losing accuracy compared to SeqSLAM.
A Multi-Domain Feature Learning Method for Visual Place Recognition
Authors: Peng Yin, Lingyun Xu, Xueqian Li, Chen Yin, Yingli Li, Rangaprasad Arun Srivatsan, Lu Li, Jianmin Ji, Yuqing He
Date: 2019-02-26 00:00:00
Tasks: Attribute, Visual Place Recognition

Visual Place Recognition (VPR) is an important component in both computer
vision and robotics applications, thanks to its ability to determine whether a
place has been visited and where specifically. A major challenge in VPR is to
handle changes of environmental conditions including weather, season and
illumination. Most VPR methods try to improve the place recognition performance
by ignoring the environmental factors, leading to decreased accuracy decreases
when environmental conditions change significantly, such as day versus night.
To this end, we propose an end-to-end conditional visual place recognition
method. Specifically, we introduce the multi-domain feature learning method
(MDFL) to capture multiple attribute-descriptions for a given place, and then
use a feature detaching module to separate the environmental condition-related
features from those that are not. The only label required within this feature
learning pipeline is the environmental condition. Evaluation of the proposed
method is conducted on the multi-season \textit{NORDLAND} dataset, and the
multi-weather \textit{GTAV} dataset. Experimental results show that our method
improves the feature robustness against variant environmental conditions.
TCDCaps: Visual Tracking via Cascaded Dense Capsules
Authors: Ding Ma, Xiangqian Wu
Date: 2019-02-26 00:00:00
Tasks: Visual Tracking

The critical challenge in tracking-by-detection framework is how to avoid drift problem during online learning, where the robust features for a variety of appearance changes are difficult to be learned and a reasonable intersection over union (IoU) threshold that defines the true/false positives is hard to set. This paper presents the TCDCaps method to address the problems above via a cascaded dense capsule architecture. To get robust features, we extend original capsules with dense-connected routing, which are referred as DCaps. Depending on the preservation of part-whole relationships in the Capsule Networks, our dense-connected capsules can capture a variety of appearance variations. In addition, to handle the issue of IoU threshold, a cascaded DCaps model (CDCaps) is proposed to improve the quality of candidates, it consists of sequential DCaps trained with increasing IoU thresholds so as to sequentially improve the quality of candidates. Extensive experiments on 3 popular benchmarks demonstrate the robustness of the proposed TCDCaps.
Graph Neural Processes: Towards Bayesian Graph Neural Networks
Authors: Andrew Carr, David Wingate
Date: 2019-02-26 00:00:00
Tasks: Imputation

We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and latent neural processes. A Graph Neural Process is defined as a Conditional Neural Process that operates on arbitrary graph data. It takes features of sparsely observed context points as input, and outputs a distribution over target points. We demonstrate graph neural processes in edge imputation and discuss benefits and drawbacks of the method for other application areas. One major benefit of GNPs is the ability to quantify uncertainty in deep learning on graph structures. An additional benefit of this method is the ability to extend graph neural networks to inputs of dynamic sized graphs.
A framework for information extraction from tables in biomedical literature
Authors: Nikola Milosevic, Cassie Gregson, Robert Hernandez, Goran Nenadic
Date: 2019-02-26 00:00:00
Tasks: Table Detection

The scientific literature is growing exponentially, and professionals are no
more able to cope with the current amount of publications. Text mining provided
in the past methods to retrieve and extract information from text; however,
most of these approaches ignored tables and figures. The research done in
mining table data still does not have an integrated approach for mining that
would consider all complexities and challenges of a table. Our research is
examining the methods for extracting numerical (number of patients, age, gender
distribution) and textual (adverse reactions) information from tables in the
clinical literature. We present a requirement analysis template and an integral
methodology for information extraction from tables in clinical domain that
contains 7 steps: (1) table detection, (2) functional processing, (3)
structural processing, (4) semantic tagging, (5) pragmatic processing, (6) cell
selection and (7) syntactic processing and extraction. Our approach performed
with the F-measure ranged between 82 and 92%, depending on the variable, task
and its complexity.
STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection
Authors: William McNally, Alexander Wong, John McPhee
Date: 2019-02-26 00:00:00
Tasks: Action Recognition, Multimodal Activity Recognition, Pose Estimation, Skeleton Based Action Recognition, Temporal Action Localization

While depth cameras and inertial sensors have been frequently leveraged for
human action recognition, these sensing modalities are impractical in many
scenarios where cost or environmental constraints prohibit their use. As such,
there has been recent interest on human action recognition using low-cost,
readily-available RGB cameras via deep convolutional neural networks. However,
many of the deep convolutional neural networks proposed for action recognition
thus far have relied heavily on learning global appearance cues directly from
imaging data, resulting in highly complex network architectures that are
computationally expensive and difficult to train. Motivated to reduce network
complexity and achieve higher performance, we introduce the concept of
spatio-temporal activation reprojection (STAR). More specifically, we reproject
the spatio-temporal activations generated by human pose estimation layers in
space and time using a stack of 3D convolutions. Experimental results on
UTD-MHAD and J-HMDB demonstrate that an end-to-end architecture based on the
proposed STAR framework (which we nickname STAR-Net) is proficient in
single-environment and small-scale applications. On UTD-MHAD, STAR-Net
outperforms several methods using richer data modalities such as depth and
inertial sensors.
Anomalous Situation Detection in Complex Scenes
Authors: Michalis Voutouris, Giovanni Sachi, Hina Afridi
Date: 2019-02-26 00:00:00

In this paper we investigate a robust method to identify anomalies in complex
scenes. This task is performed by evaluating the collective behavior by
extracting the local binary patterns (LBP) and Laplacian of Gaussian (LoG)
features. We fuse both features together which are exploited to train an MLP
neural network during the training stage, and the anomaly is identified on the
test samples. Considering the challenge of tracking individuals in dense
crowded scenes due to multiple occlusions and clutter, in this paper we extract
LBP and LoG features and use them as an approximate representation of the
anomalous situation. These features well match the appearance of anomaly and
their consistency, and accuracy is higher both in regular and irregular areas
compared to other descriptors. In this paper, these features are exploited as
input prior to train the neural network. The MLP neural network is subsequently
explored to consider these features that can detect the anomalous situation.
The experimental tests are conducted on a set of benchmark video sequences
commonly used for anomaly situation detection.
The Termination Critic
Authors: Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup
Date: 2019-02-26 00:00:00
Tasks: Reinforcement Learning

In this work, we consider the problem of autonomously discovering behavioral
abstractions, or options, for reinforcement learning agents. We propose an
algorithm that focuses on the termination condition, as opposed to -- as is
common -- the policy. The termination condition is usually trained to optimize
a control objective: an option ought to terminate if another has better value.
We offer a different, information-theoretic perspective, and propose that
terminations should focus instead on the compressibility of the option's
encoding -- arguably a key reason for using abstractions. To achieve this
algorithmically, we leverage the classical options framework, and learn the
option transition model as a "critic" for the termination condition. Using this
model, we derive gradients that optimize the desired criteria. We show that the
resulting options are non-trivial, intuitively meaningful, and useful for
learning and planning.
Fully Distributed Bayesian Optimization with Stochastic Policies
Authors: Javier Garcia-Barcos, Ruben Martinez-Cantin
Date: 2019-02-26 00:00:00
Tasks: Bayesian Optimization, Thompson Sampling

Bayesian optimization has become a popular method for high-throughput computing, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory. In these applications, distributed and scalable architectures are a necessity. However, Bayesian optimization is mostly sequential. Even parallel variants require certain computations between samples, limiting the parallelization bandwidth. Thompson sampling has been previously applied for distributed Bayesian optimization. But, when compared with other acquisition functions in the sequential setting, Thompson sampling is known to perform suboptimally. In this paper, we present a new method for fully distributed Bayesian optimization, which can be combined with any acquisition function. Our approach considers Bayesian optimization as a partially observable Markov decision process. In this context, stochastic policies, such as the Boltzmann policy, have some interesting properties which can also be studied for Bayesian optimization. Furthermore, the Boltzmann policy trivially allows a distributed Bayesian optimization implementation with high level of parallelism and scalability. We present results in several benchmarks and applications that shows the performance of our method.
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings
Authors: Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg
Date: 2019-02-26 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Agents are systems that optimize an objective function in an environment. Together, the goal and the environment induce secondary objectives, incentives. Modeling the agent-environment interaction using causal influence diagrams, we can answer two fundamental questions about an agent's incentives directly from the graph: (1) which nodes can the agent have an incentivize to observe, and (2) which nodes can the agent have an incentivize to control? The answers tell us which information and influence points need extra protection. For example, we may want a classifier for job applications to not use the ethnicity of the candidate, and a reinforcement learning agent not to take direct control of its reward mechanism. Different algorithms and training paradigms can lead to different causal influence diagrams, so our method can be used to identify algorithms with problematic incentives and help in designing algorithms with better incentives.
Design of intentional backdoors in sequential models
Authors: Zhaoyuan Yang, Naresh Iyer, Johan Reimann, Nurali Virani
Date: 2019-02-26 00:00:00
Tasks: Decision Making, Reinforcement Learning, Sequential Decision Making

Recent work has demonstrated robust mechanisms by which attacks can be
orchestrated on machine learning models. In contrast to adversarial examples,
backdoor or trojan attacks embed surgically modified samples with targeted
labels in the model training process to cause the targeted model to learn to
misclassify chosen samples in the presence of specific triggers, while keeping
the model performance stable across other nominal samples. However, current
published research on trojan attacks mainly focuses on classification problems,
which ignores sequential dependency between inputs. In this paper, we propose
methods to discreetly introduce and exploit novel backdoor attacks within a
sequential decision-making agent, such as a reinforcement learning agent, by
training multiple benign and malicious policies within a single long short-term
memory (LSTM) network. We demonstrate the effectiveness as well as the damaging
impact of such attacks through initial outcomes generated from our approach,
employed on grid-world environments. We also provide evidence as well as
intuition on how the trojan trigger and malicious policy is activated.
Challenges with network size and unintentional triggers are identified and
analogies with adversarial examples are also discussed. In the end, we propose
potential approaches to defend against or serve as early detection for such
attacks. Results of our work can also be extended to many applications of LSTM
and recurrent networks.
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
Authors: Runsheng Zhang, Yaping Huang, Mengyang Pu, Jian Zhang, Qingji Guan, Qi Zou, Haibin Ling
Date: 2019-02-26 00:00:00
Tasks: Object Discovery, Saliency Detection, Unsupervised Saliency Detection

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly.
An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection
Authors: Stefan Hinterstoisser, Olivier Pauly, Hauke Heibel, Martina Marek, Martin Bokeloh
Date: 2019-02-26 00:00:00
Tasks: object-detection, Object Detection

Deep learning methods typically require vast amounts of training data to
reach their full potential. While some publicly available datasets exists,
domain specific data always needs to be collected and manually labeled, an
expensive, time consuming and error prone process. Training with synthetic data
is therefore very lucrative, as dataset creation and labeling comes for free.
We propose a novel method for creating purely synthetic training data for
object detection. We leverage a large dataset of 3D background models and
densely render them using full domain randomization. This yields background
images with realistic shapes and texture on top of which we render the objects
of interest. During training, the data generation process follows a curriculum
strategy guaranteeing that all foreground models are presented to the network
equally under all possible poses and conditions with increasing complexity. As
a result, we entirely control the underlying statistics and we create optimal
training samples at every stage of training. Using a set of 64 retail objects,
we demonstrate that our simple approach enables the training of detectors that
outperform models trained with real data on a challenging evaluation dataset.
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Authors: Christian Gumbsch, Martin V. Butz, Georg Martius
Date: 2019-02-26 00:00:00

Voluntary behavior of humans appears to be composed of small, elementary building blocks or behavioral primitives. While this modular organization seems crucial for the learning of complex motor skills and the flexible adaption of behavior to new circumstances, the problem of learning meaningful, compositional abstractions from sensorimotor experiences remains an open challenge. Here, we introduce a computational learning architecture, termed surprise-based behavioral modularization into event-predictive structures (SUBMODES), that explores behavior and identifies the underlying behavioral units completely from scratch. The SUBMODES architecture bootstraps sensorimotor exploration using a self-organizing neural controller. While exploring the behavioral capabilities of its own body, the system learns modular structures that predict the sensorimotor dynamics and generate the associated behavior. In line with recent theories of event perception, the system uses unexpected prediction error signals, i.e., surprise, to detect transitions between successive behavioral primitives. We show that, when applied to two robotic systems with completely different body kinematics, the system manages to learn a variety of complex and realistic behavioral primitives. Moreover, after initial self-exploration the system can use its learned predictive models progressively more effectively for invoking model predictive planning and goal-directed control in different tasks and environments.
Fused Lasso for Feature Selection using Structural Information
Authors: Lu Bai, Lixin Cui, Yue Wang, Philip S. Yu, Edwin R. Hancock
Date: 2019-02-26 00:00:00
Tasks: feature selection, Time Series Analysis

Feature selection has been proven a powerful preprocessing step for high-dimensional data analysis. However, most state-of-the-art methods tend to overlook the structural correlation information between pairwise samples, which may encapsulate useful information for refining the performance of feature selection. Moreover, they usually consider candidate feature relevancy equivalent to selected feature relevancy, and some less relevant features may be misinterpreted as salient features. To overcome these issues, we propose a new feature selection method using structural correlation between pairwise samples. Our idea is based on converting the original vectorial features into structure-based feature graph representations to incorporate structural relationship between samples, and defining a new evaluation measure to compute the joint significance of pairwise feature combinations in relation to the target feature graph. Furthermore, we formulate the corresponding feature subset selection problem into a least square regression model associated with a fused lasso regularizer to simultaneously maximize the joint relevancy and minimize the redundancy of the selected features. To effectively solve the optimization problem, an iterative algorithm is developed to identify the most discriminative features. Experiments demonstrate the effectiveness of the proposed approach.