pwc-archive/papers-with-abstracts huggingface dataset:
Cross-domain Recommendation via Deep Domain Adaptation
Authors: Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, Taiji Suzuki
Date: 2018-03-08 00:00:00
Tasks: Collaborative Filtering, Denoising, Domain Adaptation, Multi-class Classification

The behavior of users in certain services could be a clue that can be used to
infer their preferences and may be used to make recommendations for other
services they have never used. However, the cross-domain relationships between
items and user consumption patterns are not simple, especially when there are
few or no common users and items across domains. To address this problem, we
propose a content-based cross-domain recommendation method for cold-start users
that does not require user- and item- overlap. We formulate recommendation as
extreme multi-class classification where labels (items) corresponding to the
users are predicted. With this formulation, the problem is reduced to a domain
adaptation setting, in which a classifier trained in the source domain is
adapted to the target domain. For this, we construct a neural network that
combines an architecture for domain adaptation, Domain Separation Network, with
a denoising autoencoder for item representation. We assess the performance of
our approach in experiments on a pair of data sets collected from movie and
news services of Yahoo! JAPAN and show that our approach outperforms several
baseline methods including a cross-domain collaborative filtering method.
A novel model-based heuristic for energy optimal motion planning for automated driving
Authors: Zlatan Ajanovic, Michael Stolz, Martin Horn
Date: 2017-12-11 00:00:00
Tasks: Motion Planning, Optimal Motion Planning

Predictive motion planning is the key to achieve energy-efficient driving,
which is one of the main benefits of automated driving. Researchers have been
studying the planning of velocity trajectories, a simpler form of motion
planning, for over a decade now and many different methods are available.
Dynamic programming has shown to be the most common choice due to its numerical
background and ability to include nonlinear constraints and models. Although
planning of an optimal trajectory is done in a systematic way, dynamic
programming does not use any knowledge about the considered problem to guide
the exploration and therefore explores all possible trajectories.
  A* is a search algorithm which enables using knowledge about the problem to
guide the exploration to the most promising solutions first. Knowledge has to
be represented in a form of a heuristic function, which gives an optimistic
estimate of cost for transitioning to the final state, which is not a
straightforward task. This paper presents a novel heuristics incorporating air
drag and auxiliary power as well as operational costs of the vehicle, besides
kinetic and potential energy and rolling resistance known in the literature.
Furthermore, optimal cruising velocity, which depends on vehicle aerodynamic
properties and auxiliary power, is derived. Results are compared for different
variants of heuristic functions and dynamic programming as well.
An FPGA-based Massively Parallel Neuromorphic Cortex Simulator
Authors: Runchun Wang, Chetan Singh Thakur, Andre van Schaik
Date: 2018-03-08 00:00:00

This paper presents a massively parallel and scalable neuromorphic cortex
simulator designed for simulating large and structurally connected spiking
neural networks, such as complex models of various areas of the cortex. The
main novelty of this work is the abstraction of a neuromorphic architecture
into clusters represented by minicolumns and hypercolumns, analogously to the
fundamental structural units observed in neurobiology. Without this approach,
simulating large-scale fully connected networks needs prohibitively large
memory to store look-up tables for point-to-point connections. Instead, we use
a novel architecture, based on the structural connectivity in the neocortex,
such that all the required parameters and connections can be stored in on-chip
memory. The cortex simulator can be easily reconfigured for simulating
different neural networks without any change in hardware structure by
programming the memory. A hierarchical communication scheme allows one neuron
to have a fan-out of up to 200k neurons. As a proof-of-concept, an
implementation on one Altera Stratix V FPGA was able to simulate 20 million to
2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified
the system by emulating a simplified auditory cortex (with 100 million
neurons). This cortex simulator achieved a low power dissipation of 1.62 {\mu}W
per neuron. With the advent of commercially available FPGA boards, our system
offers an accessible and scalable tool for the design, real-time simulation,
and analysis of large-scale spiking neural networks.
Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network
Authors: Timothy de Moor, Alejandro Rodriguez-Ruiz, Albert Gubern Mérida, Ritse Mann, Jonas Teuwen
Date: 2018-02-19 00:00:00
Tasks: Lesion Detection, Lesion Segmentation, Sensitivity

Computer-aided detection or decision support systems aim to improve breast
cancer screening programs by helping radiologists to evaluate digital
mammography (DM) exams. Commonly such methods proceed in two steps: selection
of candidate regions for malignancy, and later classification as either
malignant or not. In this study, we present a candidate detection method based
on deep learning to automatically detect and additionally segment soft tissue
lesions in DM. A database of DM exams (mostly bilateral and two views) was
collected from our institutional archive. In total, 7196 DM exams (28294 DM
images) acquired with systems from three different vendors (General Electric,
Siemens, Hologic) were collected, of which 2883 contained malignant lesions
verified with histopathology. Data was randomly split on an exam level into
training (50\%), validation (10\%) and testing (40\%) of deep neural network
with u-net architecture. The u-net classifies the image but also provides
lesion segmentation. Free receiver operating characteristic (FROC) analysis was
used to evaluate the model, on an image and on an exam level. On an image
level, a maximum sensitivity of 0.94 at 7.93 false positives (FP) per image was
achieved. Similarly, per exam a maximum sensitivity of 0.98 at 7.81 FP per
image was achieved. In conclusion, the method could be used as a candidate
selection model with high accuracy and with the additional information of
lesion segmentation.
On Using Backpropagation for Speech Texture Generation and Voice Conversion
Authors: Jan Chorowski, Ron J. Weiss, Rif A. Saurous, Samy Bengio
Date: 2017-12-22 00:00:00
Tasks: Image Generation, speech-recognition, Speech Recognition, Style Transfer, Texture Synthesis, Voice Conversion

Inspired by recent work on neural network image generation which rely on
backpropagation towards the network inputs, we present a proof-of-concept
system for speech texture synthesis and voice conversion based on two
mechanisms: approximate inversion of the representation learned by a speech
recognition neural network, and on matching statistics of neuron activations
between different source and target utterances. Similar to image texture
synthesis and neural style transfer, the system works by optimizing a cost
function with respect to the input waveform samples. To this end we use a
differentiable mel-filterbank feature extraction pipeline and train a
convolutional CTC speech recognition network. Our system is able to extract
speaker characteristics from very limited amounts of target speaker data, as
little as a few seconds, and can be used to generate realistic speech babble or
reconstruct an utterance in a different voice.
IcoRating: A Deep-Learning System for Scam ICO Identification
Authors: Shuqing Bian, Zhenpeng Deng, Fei Li, Will Monroe, Peng Shi, Zijun Sun, Wei Wu, Sikuang Wang, William Yang Wang, Arianna Yuan, Tianwei Zhang, Jiwei Li
Date: 2018-03-08 00:00:00
Tasks: Deep Learning

Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP,
NEO) have been rapidly gaining ground in use, value, and understanding among
the public, bringing astonishing profits to investors. Unlike other money and
banking systems, most digital tokens do not require central authorities. Being
decentralized poses significant challenges for credit rating. Most ICOs are
currently not subject to government regulations, which makes a reliable credit
rating system for ICO projects necessary and urgent.
  In this paper, we introduce IcoRating, the first learning--based
cryptocurrency rating system. We exploit natural-language processing techniques
to analyze various aspects of 2,251 digital currencies to date, such as white
paper content, founding teams, Github repositories, websites, etc. Supervised
learning models are used to correlate the life span and the price change of
cryptocurrencies with these features. For the best setting, the proposed system
is able to identify scam ICO projects with 0.83 precision.
  We hope this work will help investors identify scam ICOs and attract more
efforts in automatically evaluating and analyzing ICO projects.
Learning Effective Binary Visual Representations with Deep Networks
Authors: Jianxin Wu, Jian-Hao Luo
Date: 2018-03-08 00:00:00
Tasks: General Classification, Image Retrieval, object-detection, Object Detection, Retrieval

Although traditionally binary visual representations are mainly designed to
reduce computational and storage costs in the image retrieval research, this
paper argues that binary visual representations can be applied to large scale
recognition and detection problems in addition to hashing in retrieval.
Furthermore, the binary nature may make it generalize better than its
real-valued counterparts. Existing binary hashing methods are either two-stage
or hinging on loss term regularization or saturated functions, hence converge
slowly and only emit soft binary values. This paper proposes Approximately
Binary Clamping (ABC), which is non-saturating, end-to-end trainable, with fast
convergence and can output true binary visual representations. ABC achieves
comparable accuracy in ImageNet classification as its real-valued counterpart,
and even generalizes better in object detection. On benchmark image retrieval
datasets, ABC also outperforms existing hashing methods.
High-dimensional classification by sparse logistic regression
Authors: Felix Abramovich, Vadim Grinshtein
Date: 2017-06-26 00:00:00
Tasks: Binary Classification, Classification, feature selection, General Classification, Model Selection, regression, Vocal Bursts Intensity Prediction

We consider high-dimensional binary classification by sparse logistic
regression. We propose a model/feature selection procedure based on penalized
maximum likelihood with a complexity penalty on the model size and derive the
non-asymptotic bounds for the resulting misclassification excess risk. The
bounds can be reduced under the additional low-noise condition. The proposed
complexity penalty is remarkably related to the VC-dimension of a set of sparse
linear classifiers. Implementation of any complexity penalty-based criterion,
however, requires a combinatorial search over all possible models. To find a
model selection procedure computationally feasible for high-dimensional data,
we extend the Slope estimator for logistic regression and show that under an
additional weighted restricted eigenvalue condition it is rate-optimal in the
minimax sense.
Object cosegmentation using deep Siamese network
Authors: Prerana Mukherjee, Brejesh lall, Snehith Lattupally
Date: 2018-03-07 00:00:00
Tasks: Object, Object Proposal Generation, Semantic Segmentation

Object cosegmentation addresses the problem of discovering similar objects
from multiple images and segmenting them as foreground simultaneously. In this
paper, we propose a novel end-to-end pipeline to segment the similar objects
simultaneously from relevant set of images using supervised learning via
deep-learning framework. We experiment with multiple set of object proposal
generation techniques and perform extensive numerical evaluations by training
the Siamese network with generated object proposals. Similar objects proposals
for the test images are retrieved using the ANNOY (Approximate Nearest
Neighbor) library and deep semantic segmentation is performed on them. Finally,
we form a collage from the segmented similar objects based on the relative
importance of the objects.
Search Engine Guided Non-Parametric Neural Machine Translation
Authors: Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O. K. Li
Date: 2017-05-20 00:00:00
Tasks: fr-en, Machine Translation, NMT, Retrieval, Sentence, Translation

In this paper, we extend an attention-based neural machine translation (NMT)
model by allowing it to access an entire training set of parallel sentence
pairs even after training. The proposed approach consists of two stages. In the
first stage--retrieval stage--, an off-the-shelf, black-box search engine is
used to retrieve a small subset of sentence pairs from a training set given a
source sentence. These pairs are further filtered based on a fuzzy matching
score based on edit distance. In the second stage--translation stage--, a novel
translation model, called translation memory enhanced NMT (TM-NMT), seamlessly
uses both the source sentence and a set of retrieved sentence pairs to perform
the translation. Empirical evaluation on three language pairs (En-Fr, En-De,
and En-Es) shows that the proposed approach significantly outperforms the
baseline approach and the improvement is more significant when more relevant
sentence pairs were retrieved.
How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks
Authors: Linli Xu, Liang Jiang, Chuan Qin, Zhe Wang, Dongfang Du
Date: 2018-03-08 00:00:00
Tasks: Decoder

With the recent advances of neural models and natural language processing,
automatic generation of classical Chinese poetry has drawn significant
attention due to its artistic and cultural value. Previous works mainly focus
on generating poetry given keywords or other text information, while visual
inspirations for poetry have been rarely explored. Generating poetry from
images is much more challenging than generating poetry from text, since images
contain very rich visual information which cannot be described completely using
several keywords, and a good poem should convey the image accurately. In this
paper, we propose a memory based neural model which exploits images to generate
poems. Specifically, an Encoder-Decoder model with a topic memory network is
proposed to generate classical Chinese poetry from images. To the best of our
knowledge, this is the first work attempting to generate classical Chinese
poetry from images with neural networks. A comprehensive experimental
investigation with both human evaluation and quantitative analysis demonstrates
that the proposed model can generate poems which convey images accurately.
Learning Robust and Adaptive Real-World Continuous Control Using Simulation and Transfer Learning
Authors: M Ferguson, K. H. Law
Date: 2018-02-13 00:00:00
Tasks: continuous-control, Continuous Control, Navigate, Reinforcement Learning, Transfer Learning

We use model-free reinforcement learning, extensive simulation, and transfer
learning to develop a continuous control algorithm that has good zero-shot
performance in a real physical environment. We train a simulated agent to act
optimally across a set of similar environments, each with dynamics drawn from a
prior distribution. We propose that the agent is able to adjust its actions
almost immediately, based on small set of observations. This robust and
adaptive behavior is enabled by using a policy gradient algorithm with an Long
Short Term Memory (LSTM) function approximation. Finally, we train an agent to
navigate a two-dimensional environment with uncertain dynamics and noisy
observations. We demonstrate that this agent has good zero-shot performance in
a real physical environment. Our preliminary results indicate that the agent is
able to infer the environmental dynamics after only a few timesteps, and adjust
its actions accordingly.
Rethinking Feature Distribution for Loss Functions in Image Classification
Authors: Weitao Wan, Yuanyi Zhong, Tianpeng Li, Jiansheng Chen
Date: 2018-03-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Classification, General Classification, image-classification, Image Classification

We propose a large-margin Gaussian Mixture (L-GM) loss for deep neural
networks in classification tasks. Different from the softmax cross-entropy
loss, our proposal is established on the assumption that the deep features of
the training set follow a Gaussian Mixture distribution. By involving a
classification margin and a likelihood regularization, the L-GM loss
facilitates both a high classification performance and an accurate modeling of
the training feature distribution. As such, the L-GM loss is superior to the
softmax loss and its major variants in the sense that besides classification,
it can be readily used to distinguish abnormal inputs, such as the adversarial
examples, based on their features' likelihood to the training feature
distribution. Extensive experiments on various recognition benchmarks like
MNIST, CIFAR, ImageNet and LFW, as well as on adversarial examples demonstrate
the effectiveness of our proposal.
Learning to Detect Multiple Photographic Defects
Authors: Ning Yu, Xiaohui Shen, Zhe Lin, Radomir Mech, Connelly Barnes
Date: 2016-12-06 00:00:00
Tasks: Defect Detection, Multi-Task Learning

In this paper, we introduce the problem of simultaneously detecting multiple
photographic defects. We aim at detecting the existence, severity, and
potential locations of common photographic defects related to color, noise,
blur and composition. The automatic detection of such defects could be used to
provide users with suggestions for how to improve photos without the need to
laboriously try various correction methods. Defect detection could also help
users select photos of higher quality while filtering out those with severe
defects in photo curation and summarization.
  To investigate this problem, we collected a large-scale dataset of user
annotations on seven common photographic defects, which allows us to evaluate
algorithms by measuring their consistency with human judgments. Our new dataset
enables us to formulate the problem as a multi-task learning problem and train
a multi-column deep convolutional neural network (CNN) to simultaneously
predict the severity of all the defects. Unlike some existing single-defect
estimation methods that rely on low-level statistics and may fail in many cases
on natural photographs, our model is able to understand image contents and
quality at a higher level. As a result, in our experiments, we show that our
model has predictions with much higher consistency with human judgments than
low-level methods as well as several baseline CNN models. Our model also
performs better than an average human from our user study.
A framework with updateable joint images re-ranking for Person Re-identification
Authors: Mingyue Yuan, Dong Yin, Jingwen Ding, Yuhao Luo, Zhipeng Zhou, Chengfeng Zhu, Rui Zhang
Date: 2018-03-08 00:00:00
Tasks: Person Re-Identification, Re-Ranking

Person re-identification plays an important role in realistic video
surveillance with increasing demand for public safety. In this paper, we
propose a novel framework with rules of updating images for person
re-identification in real-world surveillance system. First, Image Pool is
generated by using mean-shift tracking method to automatically select video
frame fragments of the target person. Second, features extracted from Image
Pool by convolutional network work together to re-rank original ranking list of
the main image and matching results will be generated. In addition, updating
rules are designed for replacing images in Image Pool when a new image
satiating with our updating critical formula in video system. These rules fall
into two categories: if the new image is from the same camera as the previous
updated image, it will replace one of assist images; otherwise, it will replace
the main image directly. Experiments are conduced on Market-1501, iLIDS-VID and
PRID-2011 and our ITSD datasets to validate that our framework outperforms on
rank-1 accuracy and mAP for person re-identification. Furthermore, the update
ability of our framework provides consistently remarkable accuracy rate in
real-world surveillance system.
General Latent Feature Models for Heterogeneous Datasets
Authors: Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani
Date: 2017-06-12 00:00:00

Latent feature modeling allows capturing the latent structure responsible for
generating the observed properties of a set of objects. It is often used to
make predictions either for new values of interest or missing information in
the original data, as well as to perform data exploratory analysis. However,
although there is an extensive literature on latent feature models for
homogeneous datasets, where all the attributes that describe each object are of
the same (continuous or discrete) nature, there is a lack of work on latent
feature modeling for heterogeneous databases. In this paper, we introduce a
general Bayesian nonparametric latent feature model suitable for heterogeneous
datasets, where the attributes describing each object can be either discrete,
continuous or mixed variables. The proposed model presents several important
properties. First, it accounts for heterogeneous data while keeping the
properties of conjugate models, which allow us to infer the model in linear
time with respect to the number of objects and attributes. Second, its Bayesian
nonparametric nature allows us to automatically infer the model complexity from
the data, i.e., the number of features necessary to capture the latent
structure in the data. Third, the latent features in the model are
binary-valued variables, easing the interpretability of the obtained latent
features in data exploratory analysis. We show the flexibility of the proposed
model by solving both prediction and data analysis tasks on several real-world
datasets. Moreover, a software package of the GLFM is publicly available for
other researcher to use and improve it.
A Multi-Objective Deep Reinforcement Learning Framework
Authors: Thanh Thi Nguyen, Ngoc Duy Nguyen, Peter Vamplew, Saeid Nahavandi, Richard Dazeley, Chee Peng Lim
Date: 2018-03-08 00:00:00
Tasks: Deep Reinforcement Learning, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental results on two benchmark problems (two-objective deep sea treasure environment and three-objective Mountain Car problem) indicate that the proposed framework is able to find the Pareto-optimal solutions effectively. The proposed framework is generic and highly modularized, which allows the integration of different deep reinforcement learning algorithms in different complex problem domains. This therefore overcomes many disadvantages involved with standard multi-objective reinforcement learning methods in the current literature. The proposed framework acts as a testbed platform that accelerates the development of MODRL for solving increasingly complicated multi-objective problems.
Some Approximation Bounds for Deep Networks
Authors: Brendan McCane, Lech Szymanski
Date: 2018-03-08 00:00:00

In this paper we introduce new bounds on the approximation of functions in
deep networks and in doing so introduce some new deep network architectures for
function approximation. These results give some theoretical insight into the
success of autoencoders and ResNets.