pwc-archive/papers-with-abstracts huggingface dataset:
Functional Object-Oriented Network: Construction & Expansion
Authors: David Paulius, Ahmad Babaeian Jelodar, Yu Sun
Date: 2018-07-05 00:00:00
Tasks: Object, Retrieval

We build upon the functional object-oriented network (FOON), a structured knowledge representation which is constructed from observations of human activities and manipulations. A FOON can be used for representing object-motion affordances. Knowledge retrieval through graph search allows us to obtain novel manipulation sequences using knowledge spanning across many video sources, hence the novelty in our approach. However, we are limited to the sources collected. To further improve the performance of knowledge retrieval as a follow up to our previous work, we discuss generalizing knowledge to be applied to objects which are similar to what we have in FOON without manually annotating new sources of knowledge. We discuss two means of generalization: 1) expanding our network through the use of object similarity to create new functional units from those we already have, and 2) compressing the functional units by object categories rather than specific objects. We discuss experiments which compare the performance of our knowledge retrieval algorithm with both expansion and compression by categories.
Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness
Authors: Priyadarshini Panda, Kaushik Roy
Date: 2018-07-05 00:00:00
Tasks: Adversarial Robustness

We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modeling of random noise with the same loss function used during posterior maximization, improves a model's understanding of the data manifold furthering adversarial robustness. We evaluate our approach's efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis. Our analysis reveals that adversarial robustness, in general, manifests in models with higher variance along the high-ranked principal components. We show that models learnt with our approach perform remarkably well against a wide-range of attacks. Furthermore, combining NoL with state-of-the-art adversarial training extends the robustness of a model, even beyond what it is adversarially trained for, in both white-box and black-box attack scenarios.
Encoding Motion Primitives for Autonomous Vehicles using Virtual Velocity Constraints and Neural Network Scheduling
Authors: Mogens Graf Plessen
Date: 2018-07-05 00:00:00
Tasks: Autonomous Vehicles, GPU, Reinforcement Learning, Scheduling, Trajectory Planning

Within the context of trajectory planning for autonomous vehicles this paper
proposes methods for efficient encoding of motion primitives in neural networks
on top of model-based and gradient-free reinforcement learning. It is
distinguished between 5 core aspects: system model, network architecture,
training algorithm, training tasks selection and hardware/software
implementation. For the system model, a kinematic (3-states-2-controls) and a
dynamic (16-states-2-controls) vehicle model are compared. For the network
architecture, 3 feedforward structures are compared including weighted skip
connections. For the training algorithm, virtual velocity constraints and
network scheduling are proposed. For the training tasks, different feature
vector selections are discussed. For the implementation, aspects of
gradient-free learning using 1 GPU and the handling of perturbation noise
therefore are discussed. The effects of proposed methods are illustrated in
experiments encoding up to 14625 motion primitives. The capabilities of tiny
neural networks with as few as 10 scalar parameters when scheduled on vehicle
velocity are emphasized.
Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts
Authors: Shweta Yadav, Ankit Kumar, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
Date: 2018-07-05 00:00:00

Knowledge about protein-protein interactions is essential in understanding
the biological processes such as metabolic pathways, DNA replication, and
transcription etc. However, a majority of the existing Protein-Protein
Interaction (PPI) systems are dependent primarily on the scientific literature,
which is yet not accessible as a structured database. Thus, efficient
information extraction systems are required for identifying PPI information
from the large collection of biomedical texts. Most of the existing systems
model the PPI extraction task as a classification problem and are tailored to
the handcrafted feature set including domain dependent features. In this paper,
we present a novel method based on deep bidirectional long short-term memory
(B-LSTM) technique that exploits word sequences and dependency path related
information to identify PPI information from text. This model leverages joint
modeling of proteins and relations in a single unified framework, which we name
as Shortest Dependency Path B-LSTM (sdpLSTM) model. We perform experiments on
two popular benchmark PPI datasets, namely AiMed & BioInfer. The evaluation
shows the F1-score values of 86.45% and 77.35% on AiMed and BioInfer,
respectively. Comparisons with the existing systems show that our proposed
approach attains state-of-the-art performance.
Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system
Authors: Guangzhi Tang, Konstantinos P. Michmizos
Date: 2018-07-05 00:00:00
Tasks: Translation

It is true that the "best" neural network is not necessarily the one with the
most "brain-like" behavior. Understanding biological intelligence, however, is
a fundamental goal for several distinct disciplines. Translating our
understanding of intelligence to machines is a fundamental problem in robotics.
Propelled by new advancements in Neuroscience, we developed a spiking neural
network (SNN) that draws from mounting experimental evidence that a number of
individual neurons is associated with spatial navigation. By following the
brain's structure, our model assumes no initial all-to-all connectivity, which
could inhibit its translation to a neuromorphic hardware, and learns an
uncharted territory by mapping its identified components into a limited number
of neural representations, through spike-timing dependent plasticity (STDP). In
our ongoing effort to employ a bioinspired SNN-controlled robot to real-world
spatial mapping applications, we demonstrate here how an SNN may robustly
control an autonomous robot in mapping and exploring an unknown environment,
while compensating for its own intrinsic hardware imperfections, such as
partial or total loss of visual input.
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images
Authors: Jun Zhang, Ashirbani Saha, Brian J. Soher, Maciej A. Mazurowski
Date: 2018-07-05 00:00:00
Tasks: Segmentation

Objective: To develop an automatic image normalization algorithm for
intensity correction of images from breast dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI) acquired by different MRI scanners with various
imaging parameters, using only image information. Methods: DCE-MR images of 460
subjects with breast cancer acquired by different scanners were used in this
study. Each subject had one T1-weighted pre-contrast image and three
T1-weighted post-contrast images available. Our normalization algorithm
operated under the assumption that the same type of tissue in different
patients should be represented by the same voxel value. We used four
tissue/material types as the anchors for the normalization: 1) air, 2) fat
tissue, 3) dense tissue, and 4) heart. The algorithm proceeded in the following
two steps: First, a state-of-the-art deep learning-based algorithm was applied
to perform tissue segmentation accurately and efficiently. Then, based on the
segmentation results, a subject-specific piecewise linear mapping function was
applied between the anchor points to normalize the same type of tissue in
different patients into the same intensity ranges. We evaluated the algorithm
with 300 subjects used for training and the rest used for testing. Results: The
application of our algorithm to images with different scanning parameters
resulted in highly improved consistency in pixel values and extracted radiomics
features. Conclusion: The proposed image normalization strategy based on tissue
segmentation can perform intensity correction fully automatically, without the
knowledge of the scanner parameters. Significance: We have thoroughly tested
our algorithm and showed that it successfully normalizes the intensity of
DCE-MR images. We made our software publicly available for others to apply in
their analyses.
Scalable Recommender Systems through Recursive Evidence Chains
Authors: Elias Tragas, Calvin Luo, Maxime Gazeau, Kevin Luk, David Duvenaud
Date: 2018-07-05 00:00:00
Tasks: Matrix Completion, Recommendation Systems

Recommender systems can be formulated as a matrix completion problem,
predicting ratings from user and item parameter vectors. Optimizing these
parameters by subsampling data becomes difficult as the number of users and
items grows. We develop a novel approach to generate all latent variables on
demand from the ratings matrix itself and a fixed pool of parameters. We
estimate missing ratings using chains of evidence that link them to a small set
of prototypical users and items. Our model automatically addresses the
cold-start and online learning problems by combining information across both
users and items. We investigate the scaling behavior of this model, and
demonstrate competitive results with respect to current matrix factorization
techniques in terms of accuracy and convergence speed.
Spatiotemporal KSVD Dictionary Learning for Online Multi-target Tracking
Authors: Huynh Manh, Gita Alaghband
Date: 2018-07-05 00:00:00
Tasks: Dictionary Learning, Human Detection

In this paper, we present a new spatial discriminative KSVD dictionary
algorithm (STKSVD) for learning target appearance in online multi-target
tracking. Different from other classification/recognition tasks (e.g. face,
image recognition), learning target's appearance in online multi-target
tracking is impacted by factors such as posture/articulation changes, partial
occlusion by background scene or other targets, background changes (human
detection bounding box covers human parts and part of the scene), etc. However,
we observe that these variations occur gradually relative to spatial and
temporal dynamics. We characterize the spatial and temporal information between
target's samples through a new STKSVD appearance learning algorithm to better
discriminate sparse code, linear classifier parameters and minimize
reconstruction error in a single optimization system. Our appearance learning
algorithm and tracking framework employ two different methods of calculating
appearance similarity score in each stage of a two-stage association: a linear
classifier in the first stage, and minimum residual errors in the second stage.
The results tested using 2DMOT2015 dataset and its public Aggregated Channel
features (ACF) human detection for all comparisons show that our method
outperforms the existing related learning methods.
Detecting Visual Relationships Using Box Attention
Authors: Alexander Kolesnikov, Alina Kuznetsova, Christoph H. Lampert, Vittorio Ferrari
Date: 2018-07-05 00:00:00
Tasks: object-detection, Object Detection

We propose a new model for detecting visual relationships, such as "person riding motorcycle" or "bottle on table". This task is an important step towards comprehensive structured image understanding, going beyond detecting individual objects. Our main novelty is a Box Attention mechanism that allows to model pairwise interactions between objects using standard object detection pipelines. The resulting model is conceptually clean, expressive and relies on well-justified training and prediction procedures. Moreover, unlike previously proposed approaches, our model does not introduce any additional complex components or hyperparameters on top of those already required by the underlying detection model. We conduct an experimental evaluation on three challenging datasets, V-COCO, Visual Relationships and Open Images, demonstrating strong quantitative and qualitative results.
Face Recognition Using Map Discriminant on YCbCr Color Space
Authors: I Gede Pasek Suta Wijaya
Date: 2018-07-05 00:00:00
Tasks: Blocking, Face Recognition

This paper presents face recognition using maximum a posteriori (MAP)
discriminant on YCbCr color space. The YCbCr color space is considered in order
to cover the skin information of face image on the recognition process. The
proposed method is employed to improve the recognition rate and equal error
rate (EER) of the gray scale based face recognition. In this case, the face
features vector consisting of small part of dominant frequency elements which
is extracted by non-blocking DCT is implemented as dimensional reduction of the
raw face images. The matching process between the query face features and the
trained face features is performed using maximum a posteriori (MAP)
discriminant. From the experimental results on data from four face databases
containing 2268 images with 196 classes show that the face recognition YCbCr
color space provide better recognition rate and lesser EER than those of gray
scale based face recognition which improve the first rank of grayscale based
method result by about 4%. However, it requires three times more computation
time than that of grayscale based method.
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning
Authors: Seyma Yucer, Yusuf Sinan Akgul
Date: 2018-07-05 00:00:00
Tasks: 3D Action Recognition, Action Recognition, General Classification, Metric Learning, Temporal Action Localization

This paper proposes a new 3D Human Action Recognition system as a two-phase
system: (1) Deep Metric Learning Module which learns a similarity metric
between two 3D joint sequences using Siamese-LSTM networks; (2) A Multiclass
Classification Module that uses the output of the first module to produce the
final recognition output. This model has several advantages: the first module
is trained with a larger set of data because it uses many combinations of
sequence pairs.Our deep metric learning module can also be trained
independently of the datasets, which makes our system modular and
generalizable. We tested the proposed system on standard and newly introduced
datasets that showed us that initial results are promising. We will continue
developing this system by adding more sophisticated LSTM blocks and by
cross-training between different datasets.
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
Authors: Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi
Date: 2018-07-05 00:00:00
Tasks: Representation Learning, Variational Inference

We present a representation learning algorithm that learns a low-dimensional
latent dynamical system from high-dimensional \textit{sequential} raw data,
e.g., video. The framework builds upon recent advances in amortized inference
methods that use both an inference network and a refinement procedure to output
samples from a variational distribution given an observation sequence, and
takes advantage of the duality between control and inference to approximately
solve the intractable inference problem using the path integral control
approach. The learned dynamical model can be used to predict and plan the
future states; we also present the efficient planning method that exploits the
learned low-dimensional latent dynamics. Numerical experiments show that the
proposed path-integral control based variational inference method leads to
tighter lower bounds in statistical model learning of sequential data. The
supplementary video: https://youtu.be/xCp35crUoLQ
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
Authors: Trefor W. Evans, Prasanth B. Nair
Date: 2018-07-05 00:00:00
Proceeding: ICML 2018
Tasks: Bayesian Inference, Gaussian Processes

We introduce a kernel approximation strategy that enables computation of the
Gaussian process log marginal likelihood and all hyperparameter derivatives in
$\mathcal{O}(p)$ time. Our GRIEF kernel consists of $p$ eigenfunctions found
using a Nystrom approximation from a dense Cartesian product grid of inducing
points. By exploiting algebraic properties of Kronecker and Khatri-Rao tensor
products, computational complexity of the training procedure can be practically
independent of the number of inducing points. This allows us to use arbitrarily
many inducing points to achieve a globally accurate kernel approximation, even
in high-dimensional problems. The fast likelihood evaluation enables type-I or
II Bayesian inference on large-scale datasets. We benchmark our algorithms on
real-world problems with up to two-million training points and $10^{33}$
inducing points.
Learning a Representation Map for Robot Navigation using Deep Variational Autoencoder
Authors: Kaixin Hu, Peter O'Connor
Date: 2018-07-05 00:00:00
Tasks: Robot Navigation

The aim of this work is to use Variational Autoencoder (VAE) to learn a
representation of an indoor environment that can be used for robot navigation.
We use images extracted from a video, in which a camera takes a tour around a
house, for training the VAE model with a 4 dimensional latent space. After the
model is trained, each real frame has a corresponding representation point on
manifold in the latent space, and each representation point has corresponding
reconstructed image. For the navigation problem, we map the starting image and
destination image to the latent space, then optimize a path on the learned
manifold connecting the two points, and finally map the path back through
decoder to a sequence of images. The ideal sequence of images should correspond
to a route that is spatially continuous - i.e. neighbor images in the route
should correspond to neighbor locations in physical space. Such a route could
be used for navigation with computer vision techniques, i.e. a robot could
follow the image sequence from starting location to destination in the
environment step by step. We implement this algorithm, but find in our
experimental results that the resulting route is not satisfactory. The route
consist of several discontinuous image frames along the ideal routes, so that
the route could not be followed by a robot with computer vision techniques in
practice. In our evaluation, we propose two reasons for our failure to
automatically find continuous routes: (1) The VAE tends to capture global
structures, but discard the details; (2) the Euclidean similarity metric used
for measuring continuity between house images is sub-optimal. For further work,
we propose: trying other generative models like VAE-GANs which may be better at
reconstructing the details to learn the representation map, and adjusting the
similarity metric in the path selecting algorithm.
A Review of Different Word Embeddings for Sentiment Classification using Deep Learning
Authors: Debadri Dutta
Date: 2018-07-05 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

The web is loaded with textual content, and Natural Language Processing is a
standout amongst the most vital fields in Machine Learning. But when data is
huge simple Machine Learning algorithms are not able to handle it and that is
when Deep Learning comes into play which based on Neural Networks. However
since neural networks cannot process raw text, we have to change over them
through some diverse strategies of word embedding. This paper demonstrates
those distinctive word embedding strategies implemented on an Amazon Review
Dataset, which has two sentiments to be classified: Happy and Unhappy based on
numerous customer reviews. Moreover we demonstrate the distinction in accuracy
with a discourse about which word embedding to apply when.
Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media
Authors: Sudha Subramani, Manjula O'Connor
Date: 2018-07-05 00:00:00
Tasks: Sentiment Analysis

Domestic Violence (DV) is considered as big social issue and there exists a
strong relationship between DV and health impacts of the public. Existing
research studies have focused on social media to track and analyse real world
events like emerging trends, natural disasters, user sentiment analysis,
political opinions, and health care. However there is less attention given on
social welfare issues like DV and its impact on public health. Recently, the
victims of DV turned to social media platforms to express their feelings in the
form of posts and seek the social and emotional support, for sympathetic
encouragement, to show compassion and empathy among public. But, it is
difficult to mine the actionable knowledge from large conversational datasets
from social media due to the characteristics of high dimensions, short, noisy,
huge volume, high velocity, and so on. Hence, this paper will propose a novel
framework to model and discover the various themes related to DV from the
public domain. The proposed framework would possibly provide unprecedentedly
valuable information to the public health researchers, national family health
organizations, government and public with data enrichment and consolidation to
improve the social welfare of the community. Thus provides actionable knowledge
by monitoring and analysing continuous and rich user generated content.
TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces
Authors: Yash Patel, Lluis Gomez, Raul Gomez, Marçal Rusiñol, Dimosthenis Karatzas, C. V. Jawahar
Date: 2018-07-04 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Retrieval, Self-Supervised Learning

The immense success of deep learning based methods in computer vision heavily
relies on large scale training datasets. These richly annotated datasets help
the network learn discriminative visual features. Collecting and annotating
such datasets requires a tremendous amount of human effort and annotations are
limited to popular set of classes. As an alternative, learning visual features
by designing auxiliary tasks which make use of freely available
self-supervision has become increasingly popular in the computer vision
community.
  In this paper, we put forward an idea to take advantage of multi-modal
context to provide self-supervision for the training of computer vision
algorithms. We show that adequate visual features can be learned efficiently by
training a CNN to predict the semantic textual context in which a particular
image is more probable to appear as an illustration. More specifically we use
popular text embedding techniques to provide the self-supervision for the
training of deep CNN.
  Our experiments demonstrate state-of-the-art performance in image
classification, object detection, and multi-modal retrieval compared to recent
self-supervised or naturally-supervised approaches.