pwc-archive/papers-with-abstracts huggingface dataset:
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
Authors: David Rohde, Stephen Bonner, Travis Dunlop, Flavian vasile, Alexandros Karatzoglou
Date: 2018-08-02 00:00:00
Tasks: Product Recommendation, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recommender Systems are becoming ubiquitous in many settings and take many
forms, from product recommendation in e-commerce stores, to query suggestions
in search engines, to friend recommendation in social networks. Current
research directions which are largely based upon supervised learning from
historical data appear to be showing diminishing returns with a lot of
practitioners report a discrepancy between improvements in offline metrics for
supervised learning and the online performance of the newly proposed models.
One possible reason is that we are using the wrong paradigm: when looking at
the long-term cycle of collecting historical performance data, creating a new
version of the recommendation model, A/B testing it and then rolling it out. We
see that there a lot of commonalities with the reinforcement learning (RL)
setup, where the agent observes the environment and acts upon it in order to
change its state towards better states (states with higher rewards). To this
end we introduce RecoGym, an RL environment for recommendation, which is
defined by a model of user traffic patterns on e-commerce and the users
response to recommendations on the publisher websites. We believe that this is
an important step forward for the field of recommendation systems research,
that could open up an avenue of collaboration between the recommender systems
and reinforcement learning communities and lead to better alignment between
offline and online performance metrics.
Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach
Authors: Hammad Haleem, Yong Wang, Abishek Puri, Sahil Wadhwa, Huamin Qu
Date: 2018-08-02 00:00:00

Existing graph layout algorithms are usually not able to optimize all the
aesthetic properties desired in a graph layout. To evaluate how well the
desired visual features are reflected in a graph layout, many readability
metrics have been proposed in the past decades. However, the calculation of
these readability metrics often requires access to the node and edge
coordinates and is usually computationally inefficient, especially for dense
graphs. Importantly, when the node and edge coordinates are not accessible, it
becomes impossible to evaluate the graph layouts quantitatively. In this paper,
we present a novel deep learning-based approach to evaluate the readability of
graph layouts by directly using graph images. A convolutional neural network
architecture is proposed and trained on a benchmark dataset of graph images,
which is composed of synthetically-generated graphs and graphs created by
sampling from real large networks. Multiple representative readability metrics
(including edge crossing, node spread, and group overlap) are considered in the
proposed approach. We quantitatively compare our approach to traditional
methods and qualitatively evaluate our approach using a case study and
visualizing convolutional layers. This work is a first step towards using deep
learning based methods to evaluate images from the visualization field
quantitatively.
OntoSenseNet: A Verb-Centric Ontological Resource for Indian Languages
Authors: Jyoti Jha, Sreekavitha Parupalli, Navjyoti Singh
Date: 2018-08-02 00:00:00

Following approaches for understanding lexical meaning developed by Yaska,
Patanjali and Bhartrihari from Indian linguistic traditions and extending
approaches developed by Leibniz and Brentano in the modern times, a framework
of formal ontology of language was developed. This framework proposes that
meaning of words are in-formed by intrinsic and extrinsic ontological
structures. The paper aims to capture such intrinsic and extrinsic meanings of
words for two major Indian languages, namely, Hindi and Telugu. Parts-of-speech
have been rendered into sense-types and sense-classes. Using them we have
developed a gold- standard annotated lexical resource to support semantic
understanding of a language. The resource has collection of Hindi and Telugu
lexicons, which has been manually annotated by native speakers of the languages
following our annotation guidelines. Further, the resource was utilised to
derive adverbial sense-class distribution of verbs and karaka-verb sense- type
distribution. Different corpora (news, novels) were compared using verb
sense-types distribution. Word Embedding was used as an aid for the enrichment
of the resource. This is a work in progress that aims at lexical coverage of
language extensively.
Online Temporal Calibration for Monocular Visual-Inertial Systems
Authors: Tong Qin, Shaojie Shen
Date: 2018-08-02 00:00:00
Tasks: Autonomous Driving, Robot Navigation, Sensor Fusion, State Estimation, Time Offset Calibration

Accurate state estimation is a fundamental module for various intelligent
applications, such as robot navigation, autonomous driving, virtual and
augmented reality. Visual and inertial fusion is a popular technology for 6-DOF
state estimation in recent years. Time instants at which different sensors'
measurements are recorded are of crucial importance to the system's robustness
and accuracy. In practice, timestamps of each sensor typically suffer from
triggering and transmission delays, leading to temporal misalignment (time
offsets) among different sensors. Such temporal offset dramatically influences
the performance of sensor fusion. To this end, we propose an online approach
for calibrating temporal offset between visual and inertial measurements. Our
approach achieves temporal offset calibration by jointly optimizing time
offset, camera and IMU states, as well as feature locations in a SLAM system.
Furthermore, the approach is a general model, which can be easily employed in
several feature-based optimization frameworks. Simulation and experimental
results demonstrate the high accuracy of our calibration approach even compared
with other state-of-art offline tools. The VIO comparison against other methods
proves that the online temporal calibration significantly benefits
visual-inertial systems. The source code of temporal calibration is integrated
into our public project, VINS-Mono.
Linguistic Search Optimization for Deep Learning Based LVCSR
Authors: Zhehuai Chen
Date: 2018-08-02 00:00:00
Tasks: Deep Learning, speech-recognition, Speech Recognition

Recent advances in deep learning based large vocabulary con- tinuous speech
recognition (LVCSR) invoke growing demands in large scale speech transcription.
The inference process of a speech recognizer is to find a sequence of labels
whose corresponding acoustic and language models best match the input feature
[1]. The main computation includes two stages: acoustic model (AM) inference
and linguistic search (weighted finite-state transducer, WFST). Large
computational overheads of both stages hamper the wide application of LVCSR.
Benefit from stronger classifiers, deep learning, and more powerful computing
devices, we propose general ideas and some initial trials to solve these
fundamental problems.
Memristor-based Synaptic Sampling Machines
Authors: Irina Dolzhikova, Khaled Salama, Vipin Kizheppatt, Alex Pappachen James
Date: 2018-08-02 00:00:00
Tasks: Edge-computing

Synaptic Sampling Machine (SSM) is a type of neural network model that
considers biological unreliability of the synapses. We propose the circuit
design of the SSM neural network which is realized through the memristive-CMOS
crossbar structure with the synaptic sampling cell (SSC) being used as a basic
stochastic unit. The increase in the edge computing devices in the Internet of
things era, drives the need for hardware acceleration for data processing and
computing. The computational considerations of the processing speed and
possibility for the real-time realization pushes the synaptic sampling
algorithm that demonstrated promising results on software for hardware
implementation.
Double Supervised Network with Attention Mechanism for Scene Text Recognition
Authors: Yuting Gao, Zheng Huang, Yuchen Dai, Cheng Xu, Kai Chen, Jie Tuo
Date: 2018-08-02 00:00:00
Tasks: Scene Text Recognition

In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition. It incorporates one text attention module during feature extraction which enforces the model to focus on text regions and the whole framework is supervised by two branches. One supervision branch comes from context-level modelling and another comes from one extra supervision enhancement branch which aims at tackling inexplicit semantic information at character level. These two supervisions can benefit each other and yield better performance. The proposed approach can recognize text in arbitrary length and does not need any predefined lexicon. Our method outperforms the current state-of-the-art methods on three text recognition benchmarks: IIIT5K, ICDAR2013 and SVT reaching accuracy 88.6%, 92.3% and 84.1% respectively which suggests the effectiveness of the proposed method.
Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits
Authors: Zhaofei Yu, Yonghong Tian, Tiejun Huang, Jian. K. Liu
Date: 2018-08-02 00:00:00
Tasks: All, Bayesian Inference

Experimental observations of neuroscience suggest that the brain is working a
probabilistic way when computing information with uncertainty. This processing
could be modeled as Bayesian inference. However, it remains unclear how
Bayesian inference could be implemented at the level of neuronal circuits of
the brain. In this study, we propose a novel general-purpose neural
implementation of probabilistic inference based on a ubiquitous network of
cortical microcircuits, termed winner-take-all (WTA) circuit. We show that each
WTA circuit could encode the distribution of states defined on a variable. By
connecting multiple WTA circuits together, the joint distribution can be
represented for arbitrary probabilistic graphical models. Moreover, we prove
that the neural dynamics of WTA circuit is able to implement one of the most
powerful inference methods in probabilistic graphical models, mean-field
inference. We show that the synaptic drive of each spiking neuron in the WTA
circuit encodes the marginal probability of the variable in each state, and the
firing probability (or firing rate) of each neuron is proportional to the
marginal probability. Theoretical analysis and experimental results demonstrate
that the WTA circuits can get comparable inference result as mean-field
approximation. Taken together, our results suggest that the WTA circuit could
be seen as the minimal inference unit of neuronal circuits.
PCN: Point Completion Network
Authors: Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, Martial Hebert
Date: 2018-08-02 00:00:00
Tasks: Decoder, Point Cloud Completion

Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.
On the achievability of blind source separation for high-dimensional nonlinear source mixtures
Authors: Takuya Isomura, Taro Toyoizumi
Date: 2018-08-02 00:00:00
Tasks: blind source separation

For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that a cascade of linear PCA and ICA can solve a nonlinear BSS problem accurately -- when the sensory inputs are generated from hidden sources via nonlinear mappings with sufficient dimensionality. Our proposed theorem, termed the asymptotic linearization theorem, theoretically guarantees that applying linear PCA to the inputs can reliably extract a subspace spanned by the linear projections from every hidden source as the major components -- and thus projecting the inputs onto their major eigenspace can effectively recover a linear transformation of the hidden sources. Then, subsequent application of linear ICA can separate all the true independent hidden sources accurately. Zero-element-wise-error nonlinear BSS is asymptotically attained when the source dimensionality is large and the input dimensionality is sufficiently larger than the source dimensionality. Our proposed theorem is validated analytically and numerically. Moreover, the same computation can be performed by using Hebbian-like plasticity rules, implying the biological plausibility of this nonlinear BSS strategy. Our results highlight the utility of linear PCA and ICA for accurately and reliably recovering nonlinearly mixed sources -- and further suggest the importance of employing sensors with sufficient dimensionality to identify true hidden sources of real-world data.
Deep Learning for Radio Resource Allocation in Multi-Cell Networks
Authors: K. I. Ahmed, H. Tabassum, E. Hossain
Date: 2018-08-02 00:00:00
Tasks: Deep Learning, Management

Increased complexity and heterogeneity of emerging 5G and beyond 5G (B5G)
wireless networks will require a paradigm shift from traditional resource
allocation mechanisms. Deep learning (DL) is a powerful tool where a
multi-layer neural network can be trained to model a resource management
algorithm using network data.Therefore, resource allocation decisions can be
obtained without intensive online computations which would be required
otherwise for the solution of resource allocation problems. In this context,
this article focuses on the application of DL to obtain solutions for the radio
resource allocation problems in multi-cell networks. Starting with a brief
overview of a deep neural network (DNN) as a DL model, relevant DNN
architectures and the data training procedure, we provide an overview of
existing state-of-the-art applying DL in the context of radio resource
allocation. A qualitative comparison is provided in terms of their objectives,
inputs/outputs, learning and data training methods. Then, we present a
supervised DL model to solve the sub-band and power allocation problem in a
multi-cell network. Using the data generated by a genetic algorithm, we first
train the model and then test the accuracy of the proposed model in predicting
the resource allocation solutions. Simulation results show that the trained DL
model is able to provide the desired optimal solution 86.3% of time.
Investigating accuracy of pitch-accent annotations in neural network-based speech synthesis and denoising effects
Authors: Hieu-Thi Luong, Xin Wang, Junichi Yamagishi, Nobuyuki Nishizawa
Date: 2018-08-02 00:00:00
Tasks: Denoising, Speech Synthesis

We investigated the impact of noisy linguistic features on the performance of
a Japanese speech synthesis system based on neural network that uses WaveNet
vocoder. We compared an ideal system that uses manually corrected linguistic
features including phoneme and prosodic information in training and test sets
against a few other systems that use corrupted linguistic features. Both
subjective and objective results demonstrate that corrupted linguistic
features, especially those in the test set, affected the ideal system's
performance significantly in a statistical sense due to a mismatched condition
between the training and test sets. Interestingly, while an utterance-level
Turing test showed that listeners had a difficult time differentiating
synthetic speech from natural speech, it further indicated that adding noise to
the linguistic features in the training set can partially reduce the effect of
the mismatch, regularize the model, and help the system perform better when
linguistic features of the test set are noisy.
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
Authors: Qixian Zhou, Xiaodan Liang, Ke Gong, Liang Lin
Date: 2018-08-02 00:00:00
Tasks: Human Parsing, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Beyond the existing single-person and multiple-person human parsing tasks in
static images, this paper makes the first attempt to investigate a more
realistic video instance-level human parsing that simultaneously segments out
each person instance and parses each instance into more fine-grained parts
(e.g., head, leg, dress). We introduce a novel Adaptive Temporal Encoding
Network (ATEN) that alternatively performs temporal encoding among key frames
and flow-guided feature propagation from other consecutive frames between two
key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the
instance-level parsing result for each key frame, which integrates both the
global human parsing and instance-level human segmentation into a unified
model. To balance between accuracy and efficiency, the flow-guided feature
propagation is used to directly parse consecutive frames according to their
identified temporal consistency with key frames. On the other hand, ATEN
leverages the convolution gated recurrent units (convGRU) to exploit temporal
changes over a series of key frames, which are further used to facilitate the
frame-level instance-level parsing. By alternatively performing direct feature
propagation between consistent frames and temporal encoding network among key
frames, our ATEN achieves a good balance between frame-level accuracy and time
efficiency, which is a common crucial problem in video object segmentation
research. To demonstrate the superiority of our ATEN, extensive experiments are
conducted on the most popular video segmentation benchmark (DAVIS) and a newly
collected Video Instance-level Parsing (VIP) dataset, which is the first video
instance-level human parsing dataset comprised of 404 sequences and over 20k
frames with instance-level and pixel-wise annotations.
Object Localization and Size Estimation from RGB-D Images
Authors: ShreeRanjani SrirangamSridharan, Oytun Ulutan, Shehzad Noor Taus Priyo, Swati Rallapalli, Mudhakar Srivatsa
Date: 2018-08-02 00:00:00
Tasks: Object, Object Localization

Depth sensing cameras (e.g., Kinect sensor, Tango phone) can acquire color
and depth images that are registered to a common viewpoint. This opens the
possibility of developing algorithms that exploit the advantages of both
sensing modalities. Traditionally, cues from color images have been used for
object localization (e.g., YOLO). However, the addition of a depth image can be
further used to segment images that might otherwise have identical color
information. Further, the depth image can be used for object size
(height/width) estimation (in real-world measurements units, such as meters) as
opposed to image based segmentation that would only support drawing bounding
boxes around objects of interest. In this paper, we first collect color camera
information along with depth information using a custom Android application on
Tango Phab2 phone. Second, we perform timing and spatial alignment between the
two data sources. Finally, we evaluate several ways of measuring the height of
the object of interest within the captured images under a variety of settings.
Sequence Discriminative Training for Deep Learning based Acoustic Keyword Spotting
Authors: Zhehuai Chen, Yanmin Qian, Kai Yu
Date: 2018-08-02 00:00:00
Tasks: Deep Learning, Keyword Spotting, speech-recognition, Speech Recognition

Speech recognition is a sequence prediction problem. Besides employing
various deep learning approaches for framelevel classification, sequence-level
discriminative training has been proved to be indispensable to achieve the
state-of-the-art performance in large vocabulary continuous speech recognition
(LVCSR). However, keyword spotting (KWS), as one of the most common speech
recognition tasks, almost only benefits from frame-level deep learning due to
the difficulty of getting competing sequence hypotheses. The few studies on
sequence discriminative training for KWS are limited for fixed vocabulary or
LVCSR based methods and have not been compared to the state-of-the-art deep
learning based KWS approaches. In this paper, a sequence discriminative
training framework is proposed for both fixed vocabulary and unrestricted
acoustic KWS. Sequence discriminative training for both sequence-level
generative and discriminative models are systematically investigated. By
introducing word-independent phone lattices or non-keyword blank symbols to
construct competing hypotheses, feasible and efficient sequence discriminative
training approaches are proposed for acoustic KWS. Experiments showed that the
proposed approaches obtained consistent and significant improvement in both
fixed vocabulary and unrestricted KWS tasks, compared to previous frame-level
deep learning based acoustic KWS methods.
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
Authors: Farhad Shakerin, Gopal Gupta
Date: 2018-08-02 00:00:00
Tasks: General Classification, Inductive logic programming

We present a heuristic based algorithm to induce \textit{nonmonotonic} logic
programs that will explain the behavior of XGBoost trained classifiers. We use
the technique based on the LIME approach to locally select the most important
features contributing to the classification decision. Then, in order to explain
the model's global behavior, we propose the LIME-FOLD algorithm ---a
heuristic-based inductive logic programming (ILP) algorithm capable of learning
non-monotonic logic programs---that we apply to a transformed dataset produced
by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm.
Our experiments with UCI standard benchmarks suggest a significant improvement
in terms of classification evaluation metrics. Meanwhile, the number of induced
rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system.
Fusion Subspace Clustering: Full and Incomplete Data
Authors: Daniel L. Pimentel-Alarcón, Usman Mahmood
Date: 2018-08-02 00:00:00
Tasks: Clustering, Low-Rank Matrix Completion, Matrix Completion

Modern inference and learning often hinge on identifying low-dimensional
structures that approximate large scale data. Subspace clustering achieves this
through a union of linear subspaces. However, in contemporary applications data
is increasingly often incomplete, rendering standard (full-data) methods
inapplicable. On the other hand, existing incomplete-data methods present major
drawbacks, like lifting an already high-dimensional problem, or requiring a
super polynomial number of samples. Motivated by this, we introduce a new
subspace clustering algorithm inspired by fusion penalties. The main idea is to
permanently assign each datum to a subspace of its own, and minimize the
distance between the subspaces of all data, so that subspaces of the same
cluster get fused together. Our approach is entirely new to both, full and
missing data, and unlike other methods, it directly allows noise, it requires
no liftings, it allows low, high, and even full-rank data, it approaches
optimal (information-theoretic) sampling rates, and it does not rely on other
methods such as low-rank matrix completion to handle missing data. Furthermore,
our extensive experiments on both real and synthetic data show that our
approach performs comparably to the state-of-the-art with complete data, and
dramatically better if data is missing.