pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Online Multitask Learning of Behavioral Sentence Embeddings
Authors: Shao-Yen Tseng, Brian Baucom, Panayiotis Georgiou
Date: 2018-07-18 00:00:00
Tasks: Domain Adaptation, Sentence, Sentence Embeddings

Unsupervised learning has been an attractive method for easily deriving
meaningful data representations from vast amounts of unlabeled data. These
representations, or embeddings, often yield superior results in many tasks,
whether used directly or as features in subsequent training stages. However,
the quality of the embeddings is highly dependent on the assumed knowledge in
the unlabeled data and how the system extracts information without supervision.
Domain portability is also very limited in unsupervised learning, often
requiring re-training on other in-domain corpora to achieve robustness. In this
work we present a multitask paradigm for unsupervised contextual learning of
behavioral interactions which addresses unsupervised domain adaption. We
introduce an online multitask objective into unsupervised learning and show
that sentence embeddings generated through this process increases performance
of affective tasks.
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Authors: Christos Kyrkou, George Plastiras, Stylianos Venieris, Theocharis Theocharides, Christos-Savvas Bouganis
Date: 2018-07-18 00:00:00
Tasks: Object Detection In Aerial Images, One-Shot Object Detection, Real-Time Object Detection, vehicle detection

Unmanned Aerial Vehicles (drones) are emerging as a promising technology for
both environmental and infrastructure monitoring, with broad use in a plethora
of applications. Many such applications require the use of computer vision
algorithms in order to analyse the information captured from an on-board
camera. Such applications include detecting vehicles for emergency response and
traffic monitoring. This paper therefore, explores the trade-offs involved in
the development of a single-shot object detector based on deep convolutional
neural networks (CNNs) that can enable UAVs to perform vehicle detection under
a resource constrained environment such as in a UAV. The paper presents a
holistic approach for designing such systems; the data collection and training
stages, the CNN architecture, and the optimizations necessary to efficiently
map such a CNN on a lightweight embedded processing platform suitable for
deployment on UAVs. Through the analysis we propose a CNN architecture that is
capable of detecting vehicles from aerial UAV images and can operate between
5-18 frames-per-second for a variety of platforms with an overall accuracy of
~95%. Overall, the proposed architecture is suitable for UAV applications,
utilizing low-power embedded processors that can be deployed on commercial
UAVs.
Deep Content-User Embedding Model for Music Recommendation
Authors: Jongpil Lee, Kyungyun Lee, Jiyoung Park, Jang-Yeon Park, Juhan Nam
Date: 2018-07-18 00:00:00
Tasks: Collaborative Filtering, model, Music Auto-Tagging, Music Recommendation, Recommendation Systems

Recently deep learning based recommendation systems have been actively
explored to solve the cold-start problem using a hybrid approach. However, the
majority of previous studies proposed a hybrid model where collaborative
filtering and content-based filtering modules are independently trained. The
end-to-end approach that takes different modality data as input and jointly
trains the model can provide better optimization but it has not been fully
explored yet. In this work, we propose deep content-user embedding model, a
simple and intuitive architecture that combines the user-item interaction and
music audio content. We evaluate the model on music recommendation and music
auto-tagging tasks. The results show that the proposed model significantly
outperforms the previous work. We also discuss various directions to improve
the proposed model further.
An Attention-Based Approach for Single Image Super Resolution
Authors: Yuan Liu, Yuancheng Wang, Nan Li, Xu Cheng, Yifeng Zhang, Yongming Huang, Guojun Lu
Date: 2018-07-18 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

The main challenge of single image super resolution (SISR) is the recovery of
high frequency details such as tiny textures. However, most of the
state-of-the-art methods lack specific modules to identify high frequency
areas, causing the output image to be blurred. We propose an attention-based
approach to give a discrimination between texture areas and smooth areas. After
the positions of high frequency details are located, high frequency
compensation is carried out. This approach can incorporate with previously
proposed SISR networks. By providing high frequency enhancement, better
performance and visual effect are achieved. We also propose our own SISR
network composed of DenseRes blocks. The block provides an effective way to
combine the low level features and high level features. Extensive benchmark
evaluation shows that our proposed method achieves significant improvement over
the state-of-the-art works in SISR.
Planning and Synthesis Under Assumptions
Authors: Benjamin Aminof, Giuseppe De Giacomo, Aniello Murano, Sasha Rubin
Date: 2018-07-18 00:00:00
Tasks: Fairness

In Reasoning about Action and Planning, one synthesizes the agent plan by taking advantage of the assumption on how the environment works (that is, one exploits the environment's effects, its fairness, its trajectory constraints). In this paper we study this form of synthesis in detail. We consider assumptions as constraints on the possible strategies that the environment can have in order to respond to the agent's actions. Such constraints may be given in the form of a planning domain (or action theory), as linear-time formulas over infinite or finite runs, or as a combination of the two. We argue though that not all assumption specifications are meaningful: they need to be consistent, which means that there must exist an environment strategy fulfilling the assumption in spite of the agent actions. For such assumptions, we study how to do synthesis/planning for agent goals, ranging from a classical reachability to goal on traces specified in \LTL and \LTLf/\LDLf, characterizing the problem both mathematically and algorithmically.
Visual Affordance and Function Understanding: A Survey
Authors: Mohammed Hassanin, Salman Khan, Murat Tahtali
Date: 2018-07-18 00:00:00
Tasks: Affordance Detection, Scene Understanding, Survey

Nowadays, robots are dominating the manufacturing, entertainment and
healthcare industries. Robot vision aims to equip robots with the ability to
discover information, understand it and interact with the environment. These
capabilities require an agent to effectively understand object affordances and
functionalities in complex visual domains. In this literature survey, we first
focus on Visual affordances and summarize the state of the art as well as open
problems and research gaps. Specifically, we discuss sub-problems such as
affordance detection, categorization, segmentation and high-level reasoning.
Furthermore, we cover functional scene understanding and the prevalent
functional descriptors used in the literature. The survey also provides
necessary background to the problem, sheds light on its significance and
highlights the existing challenges for affordance and functionality learning.
Bag-of-Visual-Words for Signature-Based Multi-Script Document Retrieval
Authors: Ranju Mandal, Partha Pratim Roy, Umapada Pal, Michael Blumenstein
Date: 2018-07-18 00:00:00
Tasks: Retrieval

An end-to-end architecture for multi-script document retrieval using
handwritten signatures is proposed in this paper. The user supplies a query
signature sample and the system exclusively returns a set of documents that
contain the query signature. In the first stage, a component-wise
classification technique separates the potential signature components from all
other components. A bag-of-visual-words powered by SIFT descriptors in a
patch-based framework is proposed to compute the features and a Support Vector
Machine (SVM)-based classifier was used to separate signatures from the
documents. In the second stage, features from the foreground (i.e. signature
strokes) and the background spatial information (i.e. background loops,
reservoirs etc.) were combined to characterize the signature object to match
with the query signature. Finally, three distance measures were used to match a
query signature with the signature present in target documents for retrieval.
The `Tobacco' document database and an Indian script database containing 560
documents of Devanagari (Hindi) and Bangla scripts were used for the
performance evaluation. The proposed system was also tested on noisy documents
and promising results were obtained. A comparative study shows that the
proposed method outperforms the state-of-the-art approaches.
Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration
Authors: Soham De, Anirbit Mukherjee, Enayat Ullah
Date: 2018-07-18 00:00:00
Proceeding: ICLR 2019 5

RMSProp and ADAM continue to be extremely popular algorithms for training
neural nets but their theoretical convergence properties have remained unclear.
Further, recent work has seemed to suggest that these algorithms have worse
generalization properties when compared to carefully tuned stochastic gradient
descent or its momentum variants. In this work, we make progress towards a
deeper understanding of ADAM and RMSProp in two ways. First, we provide proofs
that these adaptive gradient algorithms are guaranteed to reach criticality for
smooth non-convex objectives, and we give bounds on the running time.
  Next we design experiments to empirically study the convergence and
generalization properties of RMSProp and ADAM against Nesterov's Accelerated
Gradient method on a variety of common autoencoder setups and on VGG-9 with
CIFAR-10. Through these experiments we demonstrate the interesting sensitivity
that ADAM has to its momentum parameter $\beta_1$. We show that at very high
values of the momentum parameter ($\beta_1 = 0.99$) ADAM outperforms a
carefully tuned NAG on most of our experiments, in terms of getting lower
training and test losses. On the other hand, NAG can sometimes do better when
ADAM's $\beta_1$ is set to the most commonly used value: $\beta_1 = 0.9$,
indicating the importance of tuning the hyperparameters of ADAM to get better
generalization performance.
  We also report experiments on different autoencoders to demonstrate that NAG
has better abilities in terms of reducing the gradient norms, and it also
produces iterates which exhibit an increasing trend for the minimum eigenvalue
of the Hessian of the loss function at the iterates.
General Value Function Networks
Authors: Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White
Date: 2018-07-18 00:00:00
Tasks: Continuous Control, Decision Making

State construction is important for learning in partially observable environments. A general purpose strategy for state construction is to learn the state update using a Recurrent Neural Network (RNN), which updates the internal state using the current internal state and the most recent observation. This internal state provides a summary of the observed sequence, to facilitate accurate predictions and decision-making. At the same time, specifying and training RNNs is notoriously tricky, particularly as the common strategy to approximate gradients back in time, called truncated Back-prop Through Time (BPTT), can be sensitive to the truncation window. Further, domain-expertise--which can usually help constrain the function class and so improve trainability--can be difficult to incorporate into complex recurrent units used within RNNs. In this work, we explore how to use multi-step predictions to constrain the RNN and incorporate prior knowledge. In particular, we revisit the idea of using predictions to construct state and ask: does constraining (parts of) the state to consist of predictions about the future improve RNN trainability? We formulate a novel RNN architecture, called a General Value Function Network (GVFN), where each internal state component corresponds to a prediction about the future represented as a value function. We first provide an objective for optimizing GVFNs, and derive several algorithms to optimize this objective. We then show that GVFNs are more robust to the truncation level, in many cases only requiring one-step gradient updates.
On Evaluation of Embodied Navigation Agents
Authors: Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, Amir R. Zamir
Date: 2018-07-18 00:00:00
Tasks: Benchmarking

Skillful mobile operation in three-dimensional environments is a primary
topic of study in Artificial Intelligence. The past two years have seen a surge
of creative work on navigation. This creative output has produced a plethora of
sometimes incompatible task definitions and evaluation protocols. To coordinate
ongoing and future research in this area, we have convened a working group to
study empirical methodology in navigation research. The present document
summarizes the consensus recommendations of this working group. We discuss
different problem statements and the role of generalization, present evaluation
measures, and provide standard scenarios that can be used for benchmarking.
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
Authors: Zhen Li, Deqing Zou, Shouhuai Xu, Hai Jin, Yawei Zhu, Zhaoxuan Chen
Date: 2018-07-18 00:00:00
Tasks: Deep Learning, Vulnerability Detection

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily basis. This calls for machine learning methods for vulnerability detection. Deep learning is attractive for this purpose because it alleviates the requirement to manually define features. Despite the tremendous success of deep learning in other application domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities in C/C++ programs with source code. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the pertinent software products.
Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding
Authors: Tong Chen, Wenjia Niu, Yingxiao Xiang, Xiaoxuan Bai, Jiqiang Liu, Zhen Han, Gang Li
Date: 2018-07-18 00:00:00

As adversarial attacks pose a serious threat to the security of AI system in
practice, such attacks have been extensively studied in the context of computer
vision applications. However, few attentions have been paid to the adversarial
research on automatic path finding. In this paper, we show dominant adversarial
examples are effective when targeting A3C path finding, and design a Common
Dominant Adversarial Examples Generation Method (CDG) to generate dominant
adversarial examples against any given map. In addition, we propose Gradient
Band-based Adversarial Training, which trained with a single randomly choose
dominant adversarial example without taking any modification, to realize the
"1:N" attack immunity for generalized dominant adversarial examples. Extensive
experimental results show that, the lowest generation precision for CDG
algorithm is 91.91%, and the lowest immune precision for Gradient Band-based
Adversarial Training is 93.89%, which can prove that our method can realize the
generalized attack immunity of A3C path finding with a high confidence.
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
Authors: Haozhe Jia, Yang song, Donghao Zhang, Heng Huang, Dagan Feng, Michael Fulham, Yong Xia, Weidong Cai
Date: 2018-07-18 00:00:00
Tasks: Decoder, General Classification, Segmentation

Advanced deep learning methods have been developed to conduct prostate MR
volume segmentation in either a 2D or 3D fully convolutional manner. However,
2D methods tend to have limited segmentation performance, since large amounts
of spatial information of prostate volumes are discarded during the
slice-by-slice segmentation process; and 3D methods also have room for
improvement, since they use isotropic kernels to perform 3D convolutions
whereas most prostate MR volumes have anisotropic spatial resolution. Besides,
the fully convolutional structural methods achieve good performance for
localization issues but neglect the per-voxel classification for segmentation
tasks. In this paper, we propose a 3D Global Convolutional Adversarial Network
(3D GCA-Net) to address efficient prostate MR volume segmentation. We first
design a 3D ResNet encoder to extract 3D features from prostate scans, and then
develop the decoder, which is composed of a multi-scale 3D global convolutional
block and a 3D boundary refinement block, to address the classification and
localization issues simultaneously for volumetric segmentation. Additionally,
we combine the encoder-decoder segmentation network with an adversarial network
in the training phrase to enforce the contiguity of long-range spatial
predictions. Throughout the proposed model, we use anisotropic convolutional
processing for better feature learning on prostate MR scans. We evaluated our
3D GCA-Net model on two public prostate MR datasets and achieved
state-of-the-art performances.
Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis
Authors: Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai
Date: 2018-07-18 00:00:00
Tasks: Acoustic Modelling, Decoder, Speech Synthesis

This paper proposes a forward attention method for the sequenceto- sequence
acoustic modeling of speech synthesis. This method is motivated by the nature
of the monotonic alignment from phone sequences to acoustic sequences. Only the
alignment paths that satisfy the monotonic condition are taken into
consideration at each decoder timestep. The modified attention probabilities at
each timestep are computed recursively using a forward algorithm. A transition
agent for forward attention is further proposed, which helps the attention
mechanism to make decisions whether to move forward or stay at each decoder
timestep. Experimental results show that the proposed forward attention method
achieves faster convergence speed and higher stability than the baseline
attention method. Besides, the method of forward attention with transition
agent can also help improve the naturalness of synthetic speech and control the
speed of synthetic speech effectively.
Generating Levels That Teach Mechanics
Authors: Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros, Andy Nealen, Julian Togelius
Date: 2018-07-18 00:00:00

The automatic generation of game tutorials is a challenging AI problem. While
it is possible to generate annotations and instructions that explain to the
player how the game is played, this paper focuses on generating a gameplay
experience that introduces the player to a game mechanic. It evolves small
levels for the Mario AI Framework that can only be beaten by an agent that
knows how to perform specific actions in the game. It uses variations of a
perfect A* agent that are limited in various ways, such as not being able to
jump high or see enemies, to test how failing to do certain actions can stop
the player from beating the level.
Motivating the Rules of the Game for Adversarial Example Research
Authors: Justin Gilmer, Ryan P. Adams, Ian Goodfellow, David Andersen, George E. Dahl
Date: 2018-07-18 00:00:00

Advances in machine learning have led to broad deployment of systems with
impressive performance on important problems. Nonetheless, these systems can be
induced to make errors on data that are surprisingly similar to examples the
learned system handles correctly. The existence of these errors raises a
variety of questions about out-of-sample generalization and whether bad actors
might use such examples to abuse deployed systems. As a result of these
security concerns, there has been a flurry of recent papers proposing
algorithms to defend against such malicious perturbations of correctly handled
examples. It is unclear how such misclassifications represent a different kind
of security problem than other errors, or even other attacker-produced examples
that have no specific relationship to an uncorrupted input. In this paper, we
argue that adversarial example defense papers have, to date, mostly considered
abstract, toy games that do not relate to any specific security concern.
Furthermore, defense papers have not yet precisely described all the abilities
and limitations of attackers that would be relevant in practical security.
Towards this end, we establish a taxonomy of motivations, constraints, and
abilities for more plausible adversaries. Finally, we provide a series of
recommendations outlining a path forward for future work to more clearly
articulate the threat model and perform more meaningful evaluation.
The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
Authors: Felipe Campelo, Lucas S. Batista, Claus Aranha
Date: 2018-07-18 00:00:00
Tasks: Evolutionary Algorithms

Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D)
represent a widely used class of population-based metaheuristics for the
solution of multicriteria optimization problems. We introduce the MOEADr
package, which offers many of these variants as instantiations of a
component-oriented framework. This approach contributes for easier
reproducibility of existing MOEA/D variants from the literature, as well as for
faster development and testing of new composite algorithms. The package offers
an standardized, modular implementation of MOEA/D based on this framework,
which was designed aiming at providing researchers and practitioners with a
standard way to discuss and express MOEA/D variants. In this paper we introduce
the design principles behind the MOEADr package, as well as its current
components. Three case studies are provided to illustrate the main aspects of
the package.
Machine Learning Interpretability: A Science rather than a tool
Authors: Abdul Karim, Avinash Mishra, MA Hakim Newton, Abdul Sattar
Date: 2018-07-18 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual

The term "interpretability" is oftenly used by machine learning researchers
each with their own intuitive understanding of it. There is no universal well
agreed upon definition of interpretability in machine learning. As any type of
science discipline is mainly driven by the set of formulated questions rather
than by different tools in that discipline, e.g. astrophysics is the discipline
that learns the composition of stars, not as the discipline that use the
spectroscopes. Similarly, we propose that machine learning interpretability
should be a discipline that answers specific questions related to
interpretability. These questions can be of statistical, causal and
counterfactual nature. Therefore, there is a need to look into the
interpretability problem of machine learning in the context of questions that
need to be addressed rather than different tools. We discuss about a
hypothetical interpretability framework driven by a question based scientific
approach rather than some specific machine learning model. Using a question
based notion of interpretability, we can step towards understanding the science
of machine learning rather than its engineering. This notion will also help us
understanding any specific problem more in depth rather than relying solely on
machine learning methods.