pwc-archive/papers-with-abstracts huggingface dataset:
Multi-scale Alignment and Contextual History for Attention Mechanism in Sequence-to-sequence Model
Authors: Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Date: 2018-07-22 00:00:00
Tasks: Decoder, Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

A sequence-to-sequence model is a neural network module for mapping two
sequences of different lengths. The sequence-to-sequence model has three core
modules: encoder, decoder, and attention. Attention is the bridge that connects
the encoder and decoder modules and improves model performance in many tasks.
In this paper, we propose two ideas to improve sequence-to-sequence model
performance by enhancing the attention module. First, we maintain the history
of the location and the expected context from several previous time-steps.
Second, we apply multiscale convolution from several previous attention vectors
to the current decoder state. We utilized our proposed framework for
sequence-to-sequence speech recognition and text-to-speech systems. The results
reveal that our proposed extension could improve performance significantly
compared to a standard attention baseline.
FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping
Authors: Mireia Ruiz Maymo, Ali Shafti, A. Aldo Faisal
Date: 2018-07-22 00:00:00
Tasks: Robotic Grasping

Wearable and Assistive robotics for human grasp support are broadly either
tele-operated robotic arms or act through orthotic control of a paralyzed
user's hand. Such devices require correct orientation for successful and
efficient grasping. In many human-robot assistive settings, the end-user is
required to explicitly control the many degrees of freedom making effective or
efficient control problematic. Here we are demonstrating the off-loading of
low-level control of assistive robotics and active orthotics, through automatic
end-effector orientation control for grasping. This paper describes a compact
algorithm implementing fast computer vision techniques to obtain the
orientation of the target object to be grasped, by segmenting the images
acquired with a camera positioned on top of the end-effector of the robotic
device. The rotation needed that optimises grasping is directly computed from
the object's orientation. The algorithm has been evaluated in 6 different scene
backgrounds and end-effector approaches to 26 different objects. 94.8% of the
objects were detected in all backgrounds. Grasping of the object was achieved
in 91.1% of the cases and has been evaluated with a robot simulator confirming
the performance of the algorithm.
RGBiD-SLAM for Accurate Real-time Localisation and 3D Mapping
Authors: Daniel Gutierrez-Gomez, Jose J. Guerrero
Date: 2018-07-22 00:00:00
Tasks: CPU, GPU

In this paper we present a complete SLAM system for RGB-D cameras, namely
RGB-iD SLAM. The presented approach is a dense direct SLAM method with the main
characteristic of working with the depth maps in inverse depth parametrisation
for the routines of dense alignment or keyframe fusion. The system consists in
2 CPU threads working in parallel, which share the use of the GPU for dense
alignment and keyframe fusion routines. The first thread is a front-end
operating at frame rate, which processes every incoming frame from the RGB-D
sensor to compute the incremental odometry and integrate it in a keyframe which
is changed periodically following a covisibility-based strategy. The second
thread is a back-end which receives keyframes from the front-end. This thread
is in charge of segmenting the keyframes based on their structure, describing
them using Bags of Words, trying to find potential loop closures with previous
keyframes, and in such case perform pose-graph optimisation for trajectory
correction. In addition, our system allows is able to compute the odometry both
with unregistered and registered depth maps, allowing to use customised
calibrations of the RGB-D sensor. As a consequence in the paper we also propose
a detailed calibration pipeline to compute customised calibrations for
particular RGB-D cameras. The experiments with our approach in the TUM RGB-D
benchmark datasets show results superior in accuracy to the state-of-the-art in
many of the sequences. The code has been made available on-line for research
purposes https://github.com/dangut/RGBiD-SLAM.
Deep learning at the shallow end: Malware classification for non-domain experts
Authors: Quan Le, Oisín Boydell, Brian Mac Namee, Mark Scanlon
Date: 2018-07-22 00:00:00
Tasks: Classification, General Classification, Malware Classification, Malware Detection

Current malware detection and classification approaches generally rely on
time consuming and knowledge intensive processes to extract patterns
(signatures) and behaviors from malware, which are then used for
identification. Moreover, these signatures are often limited to local,
contiguous sequences within the data whilst ignoring their context in relation
to each other and throughout the malware file as a whole. We present a Deep
Learning based malware classification approach that requires no expert domain
knowledge and is based on a purely data driven approach for complex pattern and
feature identification.
Macro-Micro Adversarial Network for Human Parsing
Authors: Yawei Luo, Zhedong Zheng, Liang Zheng, Tao Guan, Junqing Yu, Yi Yang
Date: 2018-07-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Human Parsing, Human Part Segmentation, Semantic Segmentation

In human parsing, the pixel-wise classification loss has drawbacks in its
low-level local inconsistency and high-level semantic inconsistency. The
introduction of the adversarial network tackles the two problems using a single
discriminator. However, the two types of parsing inconsistency are generated by
distinct mechanisms, so it is difficult for a single discriminator to solve
them both. To address the two kinds of inconsistencies, this paper proposes the
Macro-Micro Adversarial Net (MMAN). It has two discriminators. One
discriminator, Macro D, acts on the low-resolution label map and penalizes
semantic inconsistency, e.g., misplaced body parts. The other discriminator,
Micro D, focuses on multiple patches of the high-resolution label map to
address the local inconsistency, e.g., blur and hole. Compared with traditional
adversarial networks, MMAN not only enforces local and semantic consistency
explicitly, but also avoids the poor convergence problem of adversarial
networks when handling high resolution images. In our experiment, we validate
that the two discriminators are complementary to each other in improving the
human parsing accuracy. The proposed framework is capable of producing
competitive parsing performance compared with the state-of-the-art methods,
i.e., mIoU=46.81% and 59.91% on LIP and PASCAL-Person-Part, respectively. On a
relatively small dataset PPSS, our pre-trained model demonstrates impressive
generalization ability. The code is publicly available at
https://github.com/RoyalVane/MMAN.
Deep Discriminative Model for Video Classification
Authors: Mohammad Tavakolian, Abdenour Hadid
Date: 2018-07-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Classification, General Classification, model, Scene Classification, Temporal Action Localization, Unsupervised Pre-training, Video Classification

This paper presents a new deep learning approach for video-based scene
classification. We design a Heterogeneous Deep Discriminative Model (HDDM)
whose parameters are initialized by performing an unsupervised pre-training in
a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBM). In
order to avoid the redundancy of adjacent frames, we extract spatiotemporal
variation patterns within frames and represent them sparsely using Sparse Cubic
Symmetrical Pattern (SCSP). Then, a pre-initialized HDDM is separately trained
using the videos of each class to learn class-specific models. According to the
minimum reconstruction error from the learnt class-specific models, a weighted
voting strategy is employed for the classification. The performance of the
proposed method is extensively evaluated on two action recognition datasets;
UCF101 and Hollywood II, and three dynamic texture and dynamic scene datasets;
DynTex, YUPENN, and Maryland. The experimental results and comparisons against
state-of-the-art methods demonstrate that the proposed method consistently
achieves superior performance on all datasets.
Understanding hand-object manipulation by modeling the contextual relationship between actions, grasp types and object attributes
Authors: Minjie Cai, Kris Kitani, Yoichi Sato
Date: 2018-07-22 00:00:00
Tasks: Object

This paper proposes a novel method for understanding daily hand-object
manipulation by developing computer vision-based techniques. Specifically, we
focus on recognizing hand grasp types, object attributes and manipulation
actions within an unified framework by exploring their contextual
relationships. Our hypothesis is that it is necessary to jointly model hands,
objects and actions in order to accurately recognize multiple tasks that are
correlated to each other in hand-object manipulation. In the proposed model, we
explore various semantic relationships between actions, grasp types and object
attributes, and show how the context can be used to boost the recognition of
each component. We also explore the spatial relationship between the hand and
object in order to detect the manipulated object from hand in cluttered
environment. Experiment results on all three recognition tasks show that our
proposed method outperforms traditional appearance-based methods which are not
designed to take into account contextual relationships involved in hand-object
manipulation. The visualization and generalizability study of the learned
context further supports our hypothesis.
NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images
Authors: Malik Aqeel Anwar, Arijit Raychowdhury
Date: 2018-07-22 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in
an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable
reward function is designed keeping in mind the cost and weight constraints for
micro drone with minimum number of sensing modalities. Collection of small
number of expert data and knowledge based data aggregation is integrated into
the RL process to aid convergence. Experimentation is carried out on a Parrot
AR drone in different indoor arenas and the results are compared with other
baseline technologies. We demonstrate how the drone successfully avoids
obstacles and navigates across different arenas.
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Authors: Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha
Date: 2018-07-22 00:00:00

Learning nonlinear dynamics from diffusion data is a challenging problem
since the individuals observed may be different at different time points,
generally following an aggregate behaviour. Existing work cannot handle the
tasks well since they model such dynamics either directly on observations or
enforce the availability of complete longitudinal individual-level
trajectories. However, in most of the practical applications, these
requirements are unrealistic: the evolving dynamics may be too complex to be
modeled directly on observations, and individual-level trajectories may not be
available due to technical limitations, experimental costs and/or privacy
issues. To address these challenges, we formulate a model of diffusion dynamics
as the {\em hidden stochastic process} via the introduction of hidden variables
for flexibility, and learn the hidden dynamics directly on {\em aggregate
observations} without any requirement for individual-level trajectories. We
propose a dynamic generative model with Wasserstein distance for LEarninG dEep
hidden Nonlinear Dynamics (LEGEND) and prove its theoretical guarantees as
well. Experiments on a range of synthetic and real-world datasets illustrate
that LEGEND has very strong performance compared to state-of-the-art baselines.
Rapid Autonomous Car Control based on Spatial and Temporal Visual Cues
Authors: Surya Dantuluri
Date: 2018-07-22 00:00:00

We present a novel approach to modern car control utilizing a combination of
Deep Convolutional Neural Networks and Long Short-Term Memory Systems: Both of
which are a subsection of Hierarchical Representations Learning, more commonly
known as Deep Learning. Using Deep Convolutional Neural Networks and Long
Short-Term Memory Systems (DCNN/LSTM), we propose an end-to-end approach to
accurately predict steering angles and throttle values. We use this algorithm
on our latest robot, El Toro Grande 1 (ETG) which is equipped with a variety of
sensors in order to localize itself in its environment. Using previous training
data and the data that it collects during circuit and drag races, it predicts
throttle and steering angles in order to stay on path and avoid colliding into
other robots. This allows ETG to theoretically race on any track with
sufficient training data.
German Dialect Identification Using Classifier Ensembles
Authors: Alina Maria Ciobanu, Shervin Malmasi, Liviu P. Dinu
Date: 2018-07-22 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialect Identification

In this paper we present the GDI_classification entry to the second German
Dialect Identification (GDI) shared task organized within the scope of the
VarDial Evaluation Campaign 2018. We present a system based on SVM classifier
ensembles trained on characters and words. The system was trained on a
collection of speech transcripts of five Swiss-German dialects provided by the
organizers. The transcripts included in the dataset contained speakers from
Basel, Bern, Lucerne, and Zurich. Our entry in the challenge reached 62.03%
F1-score and was ranked third out of eight teams.
Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach
Authors: Luke Burks, Ian Loefgren, Nisar Ahmed
Date: 2018-07-22 00:00:00
Tasks: Clustering, Decision Making, State Space Models

This work develops novel strategies for optimal planning with semantic observations using continuous state partially observable markov decision processes (CPOMDPs). Two major innovations are presented in relation to Gaussian mixture (GM) CPOMDP policy approximation methods. While existing methods have many desirable theoretical properties, they are unable to efficiently represent and reason over hybrid continuous-discrete probabilistic models. The first major innovation is the derivation of closed-form variational Bayes GM approximations of Point-Based Value Iteration Bellman policy backups, using softmax models of continuous-discrete semantic observation probabilities. A key benefit of this approach is that dynamic decision-making tasks can be performed with complex non-Gaussian uncertainties, while also exploiting continuous dynamic state space models (thus avoiding cumbersome and costly discretization). The second major innovation is a new clustering-based technique for mixture condensation that scales well to very large GM policy functions and belief functions. Simulation results for a target search and interception task with semantic observations show that the GM policies resulting from these innovations are more effective than those produced by other state of the art policy approximations, but require significantly less modeling overhead and online runtime cost. Additional results show the robustness of this approach to model errors and scaling to higher dimensions.
Tree-structured multi-stage principal component analysis (TMPCA): theory and applications
Authors: Yuanhang Su, Ruiyuan Lin, C. -C. Jay Kuo
Date: 2018-07-22 00:00:00
Tasks: Dimensionality Reduction, General Classification, text-classification, Text Classification

A PCA based sequence-to-vector (seq2vec) dimension reduction method for the
text classification problem, called the tree-structured multi-stage principal
component analysis (TMPCA) is presented in this paper. Theoretical analysis and
applicability of TMPCA are demonstrated as an extension to our previous work
(Su, Huang & Kuo). Unlike conventional word-to-vector embedding methods, the
TMPCA method conducts dimension reduction at the sequence level without labeled
training data. Furthermore, it can preserve the sequential structure of input
sequences. We show that TMPCA is computationally efficient and able to
facilitate sequence-based text classification tasks by preserving strong mutual
information between its input and output mathematically. It is also
demonstrated by experimental results that a dense (fully connected) network
trained on the TMPCA preprocessed data achieves better performance than
state-of-the-art fastText and other neural-network-based solutions.
Asynchronous Advantage Actor-Critic Agent for Starcraft II
Authors: Basel Alghanem, Keerthana P G
Date: 2018-07-22 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II, Transfer Learning

Deep reinforcement learning, and especially the Asynchronous Advantage
Actor-Critic algorithm, has been successfully used to achieve super-human
performance in a variety of video games. Starcraft II is a new challenge for
the reinforcement learning community with the release of pysc2 learning
environment proposed by Google Deepmind and Blizzard Entertainment. Despite
being a target for several AI developers, few have achieved human level
performance. In this project we explain the complexities of this environment
and discuss the results from our experiments on the environment. We have
compared various architectures and have proved that transfer learning can be an
effective paradigm in reinforcement learning research for complex scenarios
requiring skill transfer.
Sign-Perturbed Sums: A New System Identification Approach for Constructing Exact Non-Asymptotic Confidence Regions in Linear Regression Models
Authors: Balázs Cs. Csáji, Marco C. Campi, Erik Weyer
Date: 2018-07-22 00:00:00

We propose a new system identification method, called Sign-Perturbed Sums
(SPS), for constructing non-asymptotic confidence regions under mild
statistical assumptions. SPS is introduced for linear regression models,
including but not limited to FIR systems, and we show that the SPS confidence
regions have exact confidence probabilities, i.e., they contain the true
parameter with a user-chosen exact probability for any finite data set.
Moreover, we also prove that the SPS regions are star convex with the
Least-Squares (LS) estimate as a star center. The main assumptions of SPS are
that the noise terms are independent and symmetrically distributed about zero,
but they can be nonstationary, and their distributions need not be known. The
paper also proposes a computationally efficient ellipsoidal outer approximation
algorithm for SPS. Finally, SPS is demonstrated through a number of simulation
experiments.
Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks
Authors: Humphrey Sheil, Omer Rana, Ronan Reilly
Date: 2018-07-21 00:00:00
Tasks: Feature Engineering, General Classification

We present a neural network for predicting purchasing intent in an Ecommerce
setting. Our main contribution is to address the significant investment in
feature engineering that is usually associated with state-of-the-art methods
such as Gradient Boosted Machines. We use trainable vector spaces to model
varied, semi-structured input data comprising categoricals, quantities and
unique instances. Multi-layer recurrent neural networks capture both
session-local and dataset-global event dependencies and relationships for user
sessions of any length. An exploration of model design decisions including
parameter sharing and skip connections further increase model accuracy. Results
on benchmark datasets deliver classification accuracy within 98% of
state-of-the-art on one and exceed state-of-the-art on the second without the
need for any domain / dataset-specific feature engineering on both short and
long event sequences.
Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text
Authors: Mingda Zhang, Rebecca Hwa, Adriana Kovashka
Date: 2018-07-21 00:00:00
Tasks: Image Captioning, Specificity

Images and text in advertisements interact in complex, non-literal ways. The
two channels are usually complementary, with each channel telling a different
part of the story. Current approaches, such as image captioning methods, only
examine literal, redundant relationships, where image and text show exactly the
same content. To understand more complex relationships, we first collect a
dataset of advertisement interpretations for whether the image and slogan in
the same visual advertisement form a parallel (conveying the same message
without literally saying the same thing) or non-parallel relationship, with the
help of workers recruited on Amazon Mechanical Turk. We develop a variety of
features that capture the creativity of images and the specificity or ambiguity
of text, as well as methods that analyze the semantics within and across
channels. We show that our method outperforms standard image-text alignment
approaches on predicting the parallel/non-parallel relationship between image
and text.
Towards Neural Theorem Proving at Scale
Authors: Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel
Date: 2018-07-21 00:00:00
Tasks: Automated Theorem Proving, Representation Learning

Neural models combining representation learning and reasoning in an
end-to-end trainable manner are receiving increasing interest. However, their
use is severely limited by their computational complexity, which renders them
unusable on real world datasets. We focus on the Neural Theorem Prover (NTP)
model proposed by Rockt{\"{a}}schel and Riedel (2017), a continuous relaxation
of the Prolog backward chaining algorithm where unification between terms is
replaced by the similarity between their embedding representations. For
answering a given query, this model needs to consider all possible proof paths,
and then aggregate results - this quickly becomes infeasible even for small
Knowledge Bases (KBs). We observe that we can accurately approximate the
inference process in this model by considering only proof paths associated with
the highest proof scores. This enables inference and learning on previously
impracticable KBs.
On Numerical Estimation of Joint Probability Distribution from Lebesgue Integral Quadratures
Authors: Vladislav Gennadievich Malyshkin
Date: 2018-07-21 00:00:00

An important application of Lebesgue integral quadrature arXiv:1807.06007 is developed. Given two random processes, $f(x)$ and $g(x)$, two generalized eigenvalue problems can be formulated and solved. In addition to obtaining two Lebesgue quadratures (for $f$ and $g$) from two eigenproblems, the projections of $f$- and $g$- eigenvectors on each other allow to build a joint distribution estimator, the most general form of which is a density-matrix correlation. Examples of the density-matrix correlation can be a value-correlation $V_{f^{[i]};g^{[j]}}$, similar to a regular correlation concept, and a new one, a probability-correlation $P_{f^{[i]};g^{[j]}}$. If Christoffel function average is used instead of regular average the approach can be extended to an estimation of joint probability of three and more random processes. The theory is implemented numerically; the software is available under the GPLv3 license.