pwc-archive/papers-with-abstracts huggingface dataset:
Music Generation by Deep Learning - Challenges and Directions
Authors: Jean-Pierre Briot, François Pachet
Date: 2017-12-09 00:00:00
Tasks: Deep Learning, Music Generation, Translation

In addition to traditional tasks such as prediction, classification and
translation, deep learning is receiving growing attention as an approach for
music generation, as witnessed by recent research groups such as Magenta at
Google and CTRL (Creator Technology Research Lab) at Spotify. The motivation is
in using the capacity of deep learning architectures and training techniques to
automatically learn musical styles from arbitrary musical corpora and then to
generate samples from the estimated distribution. However, a direct application
of deep learning to generate content rapidly reaches limits as the generated
content tends to mimic the training set without exhibiting true creativity.
Moreover, deep learning architectures do not offer direct ways for controlling
generation (e.g., imposing some tonality or other arbitrary constraints).
Furthermore, deep learning architectures alone are autistic automata which
generate music autonomously without human user interaction, far from the
objective of interactively assisting musicians to compose and refine music.
Issues such as: control, structure, creativity and interactivity are the focus
of our analysis. In this paper, we select some limitations of a direct
application of deep learning to music generation, analyze why the issues are
not fulfilled and how to address them by possible approaches. Various examples
of recent systems are cited as examples of promising directions.
A Streaming Algorithm for Graph Clustering
Authors: Alexandre Hollocou, Julien Maudet, Thomas Bonald, Marc Lelarge
Date: 2017-12-09 00:00:00
Tasks: Clustering, Graph Clustering

We introduce a novel algorithm to perform graph clustering in the edge
streaming setting. In this model, the graph is presented as a sequence of edges
that can be processed strictly once. Our streaming algorithm has an extremely
low memory footprint as it stores only three integers per node and does not
keep any edge in memory. We provide a theoretical justification of the design
of the algorithm based on the modularity function, which is a usual metric to
evaluate the quality of a graph partition. We perform experiments on massive
real-life graphs ranging from one million to more than one billion edges and we
show that this new algorithm runs more than ten times faster than existing
algorithms and leads to similar or better detection scores on the largest
graphs.
Unmixing dynamic PET images with variable specific binding kinetics
Authors: Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Simon Stute, Maria Ribeiro, Clovis Tauber
Date: 2017-07-19 00:00:00

To analyze dynamic positron emission tomography (PET) images, various generic
multivariate data analysis techniques have been considered in the literature,
such as principal component analysis (PCA), independent component analysis
(ICA), factor analysis and nonnegative matrix factorization (NMF).
Nevertheless, these conventional approaches neglect any possible nonlinear
variations in the time activity curves describing the kinetic behavior of
tissues with specific binding, which limits their ability to recover a
reliable, understandable and interpretable description of the data. This paper
proposes an alternative analysis paradigm that accounts for spatial
fluctuations in the exchange rate of the tracer between a free compartment and
a specifically bound ligand compartment. The method relies on the concept of
linear unmixing, usually applied on the hyperspectral domain, which combines
NMF with a sum-to-one constraint that ensures an exhaustive description of the
mixtures. The spatial variability of the signature corresponding to the
specific binding tissue is explicitly modeled through a perturbed component.
The performance of the method is assessed on both synthetic and real data and
is shown to compete favorably when compared to other conventional analysis
methods. The proposed method improved both factor estimation and proportions
extraction for specific binding. Modeling the variability of the specific
binding factor has a strong potential impact for dynamic PET image analysis.
Noise Level Estimation for Overcomplete Dictionary Learning Based on Tight Asymptotic Bounds
Authors: Rui Chen, Changshui Yang, Huizhu Jia, Xiaodong Xie
Date: 2017-12-09 00:00:00
Tasks: Dictionary Learning

In this letter, we address the problem of estimating Gaussian noise level
from the trained dictionaries in update stage. We first provide rigorous
statistical analysis on the eigenvalue distributions of a sample covariance
matrix. Then we propose an interval-bounded estimator for noise variance in
high dimensional setting. To this end, an effective estimation method for noise
level is devised based on the boundness and asymptotic behavior of noise
eigenvalue spectrum. The estimation performance of our method has been
guaranteed both theoretically and empirically. The analysis and experiment
results have demonstrated that the proposed algorithm can reliably infer true
noise levels, and outperforms the relevant existing methods.
A Deep Recurrent Framework for Cleaning Motion Capture Data
Authors: Utkarsh Mall, G. Roshan Lal, Siddhartha Chaudhuri, Parag Chaudhuri
Date: 2017-12-09 00:00:00

We present a deep, bidirectional, recurrent framework for cleaning noisy and
incomplete motion capture data. It exploits temporal coherence and joint
correlations to infer adaptive filters for each joint in each frame. A single
model can be trained to denoise a heterogeneous mix of action types, under
substantial amounts of noise. A signal that has both noise and gaps is
preprocessed with a second bidirectional network that synthesizes missing
frames from surrounding context. The approach handles a wide variety of noise
types and long gaps, does not rely on knowledge of the noise distribution, and
operates in a streaming setting. We validate our approach through extensive
evaluations on noise both in joint angles and in joint positions, and show that
it improves upon various alternatives.
Data-driven generation of spatio-temporal routines in human mobility
Authors: Luca Pappalardo, Filippo Simini
Date: 2016-07-16 00:00:00

The generation of realistic spatio-temporal trajectories of human mobility is
of fundamental importance in a wide range of applications, such as the
developing of protocols for mobile ad-hoc networks or what-if analysis in urban
ecosystems. Current generative algorithms fail in accurately reproducing the
individuals' recurrent schedules and at the same time in accounting for the
possibility that individuals may break the routine during periods of variable
duration. In this article we present DITRAS (DIary-based TRAjectory Simulator),
a framework to simulate the spatio-temporal patterns of human mobility. DITRAS
operates in two steps: the generation of a mobility diary and the translation
of the mobility diary into a mobility trajectory. We propose a data-driven
algorithm which constructs a diary generator from real data, capturing the
tendency of individuals to follow or break their routine. We also propose a
trajectory generator based on the concept of preferential exploration and
preferential return. We instantiate DITRAS with the proposed diary and
trajectory generators and compare the resulting algorithm with real data and
synthetic data produced by other generative algorithms, built by instantiating
DITRAS with several combinations of diary and trajectory generators. We show
that the proposed algorithm reproduces the statistical properties of real
trajectories in the most accurate way, making a step forward the understanding
of the origin of the spatio-temporal patterns of human mobility.
Measuring Territorial Control in Civil Wars Using Hidden Markov Models: A Data Informatics-Based Approach
Authors: Therese Anders, Hong Xu, Cheng Cheng, T. K. Satish Kumar
Date: 2017-11-18 00:00:00

Territorial control is a key aspect shaping the dynamics of civil war.
Despite its importance, we lack data on territorial control that are
fine-grained enough to account for subnational spatio-temporal variation and
that cover a large set of conflicts. To resolve this issue, we propose a
theoretical model of the relationship between territorial control and tactical
choice in civil war and outline how Hidden Markov Models (HMMs) are suitable to
capture theoretical intuitions and estimate levels of territorial control. We
discuss challenges of using HMMs in this application and mitigation strategies
for future work.
Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization
Authors: Adam McCarthy, Blanca Rodriguez, Ana Minchole
Date: 2017-12-09 00:00:00
Tasks: Bayesian Optimization, Variational Inference

Performing inference over simulators is generally intractable as their
runtime means we cannot compute a marginal likelihood. We develop a
likelihood-free inference method to infer parameters for a cardiac simulator,
which replicates electrical flow through the heart to the body surface. We
improve the fit of a state-of-the-art simulator to an electrocardiogram (ECG)
recorded from a real patient.
Peephole: Predicting Network Performance Before Training
Authors: Boyang Deng, Junjie Yan, Dahua Lin
Date: 2017-12-09 00:00:00

The quest for performant networks has been a significant force that drives
the advancements of deep learning in recent years. While rewarding, improving
network design has never been an easy journey. The large design space combined
with the tremendous cost required for network training poses a major obstacle
to this endeavor. In this work, we propose a new approach to this problem,
namely, predicting the performance of a network before training, based on its
architecture. Specifically, we develop a unified way to encode individual
layers into vectors and bring them together to form an integrated description
via LSTM. Taking advantage of the recurrent network's strong expressive power,
this method can reliably predict the performances of various network
architectures. Our empirical studies showed that it not only achieved accurate
predictions but also produced consistent rankings across datasets -- a key
desideratum in performance prediction.
DeepIso: A Deep Learning Model for Peptide Feature Detection
Authors: Fatema Tuz Zohora, Ngoc Hieu Tran, Xianglilan Zhang, Lei Xin, Baozhen Shan, Ming Li
Date: 2017-12-09 00:00:00
Tasks: Deep Learning, Drug Design, Drug Discovery, Specificity

Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based
proteomics is a well-established research field with major applications such as
identification of disease biomarkers, drug discovery, drug design and
development. In proteomics, protein identification and quantification is a
fundamental task, which is done by first enzymatically digesting it into
peptides, and then analyzing peptides by LC-MS/MS instruments. The peptide
feature detection and quantification from an LC-MS map is the first step in
typical analysis workflows. In this paper we propose a novel deep learning
based model, DeepIso, that uses Convolutional Neural Networks (CNNs) to scan an
LC-MS map to detect peptide features and estimate their abundance. Existing
tools are often designed with limited engineered features based on domain
knowledge, and depend on pretrained parameters which are hardly updated despite
huge amount of new coming proteomic data. Our proposed model, on the other
hand, is capable of learning multiple levels of representation of high
dimensional data through its many layers of neurons and continuously evolving
with newly acquired data. To evaluate our proposed model, we use an antibody
dataset including a heavy and a light chain, each digested by Asp-N,
Chymotrypsin, Trypsin, thus giving six LC-MS maps for the experiment. Our model
achieves 93.21% sensitivity with specificity of 99.44% on this dataset. Our
results demonstrate that novel deep learning tools are desirable to advance the
state-of-the-art in protein identification and quantification.
Happiness Pursuit: Personality Learning in a Society of Agents
Authors: Rafał Muszyński, Jun Wang
Date: 2017-11-29 00:00:00

Modeling personality is a challenging problem with applications spanning
computer games, virtual assistants, online shopping and education. Many
techniques have been tried, ranging from neural networks to computational
cognitive architectures. However, most approaches rely on examples with
hand-crafted features and scenarios. Here, we approach learning a personality
by training agents using a Deep Q-Network (DQN) model on rewards based on
psychoanalysis, against hand-coded AI in the game of Pong. As a result, we
obtain 4 agents, each with its own personality. Then, we define happiness of an
agent, which can be seen as a measure of alignment with agent's objective
function, and study it when agents play both against hand-coded AI, and against
each other. We find that the agents that achieve higher happiness during
testing against hand-coded AI, have lower happiness when competing against each
other. This suggests that higher happiness in testing is a sign of overfitting
in learning to interact with hand-coded AI, and leads to worse performance
against agents with different personalities.
Integrating both Visual and Audio Cues for Enhanced Video Caption
Authors: Wangli Hao, Zhao-Xiang Zhang, He Guan, Guibo Zhu
Date: 2017-11-22 00:00:00
Tasks: Descriptive, Sentence, Video Description

Video caption refers to generating a descriptive sentence for a specific
short video clip automatically, which has achieved remarkable success recently.
However, most of the existing methods focus more on visual information while
ignoring the synchronized audio cues. We propose three multimodal deep fusion
strategies to maximize the benefits of visual-audio resonance information. The
first one explores the impact on cross-modalities feature fusion from low to
high order. The second establishes the visual-audio short-term dependency by
sharing weights of corresponding front-end networks. The third extends the
temporal dependency to long-term through sharing multimodal memory across
visual and audio modalities. Extensive experiments have validated the
effectiveness of our three cross-modalities fusion strategies on two benchmark
datasets, including Microsoft Research Video to Text (MSRVTT) and Microsoft
Video Description (MSVD). It is worth mentioning that sharing weight can
coordinate visual-audio feature fusion effectively and achieve the state-of-art
performance on both BELU and METEOR metrics. Furthermore, we first propose a
dynamic multimodal feature fusion framework to deal with the part modalities
missing case. Experimental results demonstrate that even in the audio absence
mode, we can still obtain comparable results with the aid of the additional
audio modality inference module.
CMCGAN: A Uniform Framework for Cross-Modal Visual-Audio Mutual Generation
Authors: Wangli Hao, Zhao-Xiang Zhang, He Guan
Date: 2017-11-22 00:00:00
Tasks: Audio Generation, Generative Adversarial Network

Visual and audio modalities are two symbiotic modalities underlying videos,
which contain both common and complementary information. If they can be mined
and fused sufficiently, performances of related video tasks can be
significantly enhanced. However, due to the environmental interference or
sensor fault, sometimes, only one modality exists while the other is abandoned
or missing. By recovering the missing modality from the existing one based on
the common information shared between them and the prior information of the
specific modality, great bonus will be gained for various vision tasks. In this
paper, we propose a Cross-Modal Cycle Generative Adversarial Network (CMCGAN)
to handle cross-modal visual-audio mutual generation. Specifically, CMCGAN is
composed of four kinds of subnetworks: audio-to-visual, visual-to-audio,
audio-to-audio and visual-to-visual subnetworks respectively, which are
organized in a cycle architecture. CMCGAN has several remarkable advantages.
Firstly, CMCGAN unifies visual-audio mutual generation into a common framework
by a joint corresponding adversarial loss. Secondly, through introducing a
latent vector with Gaussian distribution, CMCGAN can handle dimension and
structure asymmetry over visual and audio modalities effectively. Thirdly,
CMCGAN can be trained end-to-end to achieve better convenience. Benefiting from
CMCGAN, we develop a dynamic multimodal classification network to handle the
modality missing problem. Abundant experiments have been conducted and validate
that CMCGAN obtains the state-of-the-art cross-modal visual-audio generation
results. Furthermore, it is shown that the generated modality achieves
comparable effects with those of original modality, which demonstrates the
effectiveness and advantages of our proposed method.
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
Authors: Chihao Zhang, Shihua Zhang
Date: 2017-12-09 00:00:00
Tasks: Bayesian Inference, Data Integration

Matrix decomposition is a popular and fundamental approach in machine
learning and data mining. It has been successfully applied into various fields.
Most matrix decomposition methods focus on decomposing a data matrix from one
single source. However, it is common that data are from different sources with
heterogeneous noise. A few of matrix decomposition methods have been extended
for such multi-view data integration and pattern discovery. While only few
methods were designed to consider the heterogeneity of noise in such multi-view
data for data integration explicitly. To this end, we propose a joint matrix
decomposition framework (BJMD), which models the heterogeneity of noise by
Gaussian distribution in a Bayesian framework. We develop two algorithms to
solve this model: one is a variational Bayesian inference algorithm, which
makes full use of the posterior distribution; and another is a maximum a
posterior algorithm, which is more scalable and can be easily paralleled.
Extensive experiments on synthetic and real-world datasets demonstrate that
BJMD considering the heterogeneity of noise is superior or competitive to the
state-of-the-art methods.
Deep-Person: Learning Discriminative Deep Features for Person Re-Identification
Authors: Xiang Bai, Mingkun Yang, Tengteng Huang, Zhiyong Dou, Rui Yu, Yongchao Xu
Date: 2017-11-29 00:00:00
Tasks: Person Re-Identification, Re-Ranking

Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor. However, the spatial context between these parts is ignored for the independent extractor to each separate part. In this paper, we propose to apply Long Short-Term Memory (LSTM) in an end-to-end way to model the pedestrian, seen as a sequence of body parts from head to foot. Integrating the contextual information strengthens the discriminative ability of local representation. We also leverage the complementary information between local and global feature. Furthermore, we integrate both identification task and ranking task in one network, where a discriminative embedding and a similarity measurement are learned concurrently. This results in a novel three-branch framework named Deep-Person, which learns highly discriminative features for person Re-ID. Experimental results demonstrate that Deep-Person outperforms the state-of-the-art methods by a large margin on three challenging datasets including Market-1501, CUHK03, and DukeMTMC-reID. Specifically, combining with a re-ranking approach, we achieve a 90.84% mAP on Market-1501 under single query setting.
Video Salient Object Detection via Fully Convolutional Networks
Authors: Wenguan Wang, Jianbing Shen, Ling Shao
Date: 2017-02-02 00:00:00
Tasks: Data Augmentation, Object, object-detection, Object Detection, Optical Flow Estimation, RGB Salient Object Detection, Salient Object Detection, Video Salient Object Detection

This paper proposes a deep learning model to efficiently detect salient
regions in videos. It addresses two important issues: (1) deep video saliency
model training with the absence of sufficiently large and pixel-wise annotated
video data, and (2) fast video saliency training and detection. The proposed
deep video saliency network consists of two modules, for capturing the spatial
and temporal saliency information, respectively. The dynamic saliency model,
explicitly incorporating saliency estimates from the static saliency model,
directly produces spatiotemporal saliency inference without time-consuming
optical flow computation. We further propose a novel data augmentation
technique that simulates video training data from existing annotated image
datasets, which enables our network to learn diverse saliency information and
prevents overfitting with the limited number of training videos. Leveraging our
synthetic video data (150K video sequences) and real videos, our deep video
saliency model successfully learns both spatial and temporal saliency cues,
thus producing accurate spatiotemporal saliency estimate. We advance the
state-of-the-art on the DAVIS dataset (MAE of .06) and the FBMS dataset (MAE of
.07), and do so with much improved speed (2fps with all steps).
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery
Authors: Gencer Sumbul, Ramazan Gokberk Cinbis, Selim Aksoy
Date: 2017-12-09 00:00:00
Tasks: Language Modeling, Language Modelling, Object Recognition, Transfer Learning, Zero-Shot Learning

Fine-grained object recognition that aims to identify the type of an object
among a large number of subcategories is an emerging application with the
increasing resolution that exposes new details in image data. Traditional fully
supervised algorithms fail to handle this problem where there is low
between-class variance and high within-class variance for the classes of
interest with small sample sizes. We study an even more extreme scenario named
zero-shot learning (ZSL) in which no training example exists for some of the
classes. ZSL aims to build a recognition model for new unseen categories by
relating them to seen classes that were previously learned. We establish this
relation by learning a compatibility function between image features extracted
via a convolutional neural network and auxiliary information that describes the
semantics of the classes of interest by using training samples from the seen
classes. Then, we show how knowledge transfer can be performed for the unseen
classes by maximizing this function during inference. We introduce a new data
set that contains 40 different types of street trees in 1-ft spatial resolution
aerial data, and evaluate the performance of this model with manually annotated
attributes, a natural language model, and a scientific taxonomy as auxiliary
information. The experiments show that the proposed model achieves 14.3%
recognition accuracy for the classes with no training examples, which is
significantly better than a random guess accuracy of 6.3% for 16 test classes,
and three other ZSL algorithms.