pwc-archive/papers-with-abstracts huggingface dataset:
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution
Authors: Lei Zhang, Zhiqiang Lang, Peng Wang, Wei Wei, Shengcai Liao, Ling Shao, Yanning Zhang
Date: 2019-03-24 00:00:00
Tasks: Spectral Super-Resolution, Super-Resolution

Spectral super-resolution (SSR) aims at generating a hyperspectral image
(HSI) from a given RGB image. Recently, a promising direction for SSR is to
learn a complicated mapping function from the RGB image to the HSI counterpart
using a deep convolutional neural network. This essentially involves mapping
the RGB context within a size-specific receptive field centered at each pixel
to its spectrum in the HSI. The focus thereon is to appropriately determine the
receptive field size and establish the mapping function from RGB context to the
corresponding spectrum. Due to their differences in category or spatial
position, pixels in HSIs often require different-sized receptive fields and
distinct mapping functions. However, few efforts have been invested to
explicitly exploit this prior.
  To address this problem, we propose a pixel-aware deep function-mixture
network for SSR, which is composed of a new class of modules, termed
function-mixture (FM) blocks. Each FM block is equipped with some basis
functions, i.e., parallel subnets of different-sized receptive fields. Besides,
it incorporates an extra subnet as a mixing function to generate pixel-wise
weights, and then linearly mixes the outputs of all basis functions with those
generated weights. This enables us to pixel-wisely determine the receptive
field size and the mapping function. Moreover, we stack several such FM blocks
to further increase the flexibility of the network in learning the pixel-wise
mapping. To encourage feature reuse, intermediate features generated by the FM
blocks are fused in late stage, which proves to be effective for boosting the
SSR performance. Experimental results on three benchmark HSI datasets
demonstrate the superiority of the proposed method.
Algorithms and Improved bounds for online learning under finite hypothesis class
Authors: Ankit Sharma, Late C. A. Murthy
Date: 2019-03-24 00:00:00

Online learning is the process of answering a sequence of questions based on
the correct answers to the previous questions. It is studied in many research
areas such as game theory, information theory and machine learning. There are
two main components of online learning framework. First, the learning algorithm
also known as the learner and second, the hypothesis class which is essentially
a set of functions which learner uses to predict answers to the questions.
Sometimes, this class contains some functions which have the capability to
provide correct answers to the entire sequence of questions. This case is
called realizable case. And when hypothesis class does not contain such
functions is called unrealizable case. The goal of the learner, in both the
cases, is to make as few mistakes as that could have been made by most powerful
functions in hypothesis class over the entire sequence of questions.
Performance of the learners is analysed by theoretical bounds on the number of
mistakes made by them. This paper proposes three algorithms to improve the
mistakes bound in the unrealizable case. Proposed algorithms perform highly
better than the existing ones in the long run when most of the input sequences
presented to the learner are likely to be realizable.
Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors
Authors: Kun Wang, Jun He, Lei Zhang
Date: 2019-03-24 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Unlike images or videos data which can be easily labeled by human being,
sensor data annotation is a time-consuming process. However, traditional
methods of human activity recognition require a large amount of such strictly
labeled data for training classifiers. In this paper, we present an
attention-based convolutional neural network for human recognition from weakly
labeled data. The proposed attention model can focus on labeled activity among
a long sequence of sensor data, and while filter out a large amount of
background noise signals. In experiment on the weakly labeled dataset, we show
that our attention model outperforms classical deep learning methods in
accuracy. Besides, we determine the specific locations of the labeled activity
in a long sequence of weakly labeled data by converting the compatibility score
which is generated from attention model to compatibility density. Our method
greatly facilitates the process of sensor data annotation, and makes data
collection more easy.
AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations
Authors: Guo-Jun Qi, Liheng Zhang, Chang Wen Chen, Qi Tian
Date: 2019-03-23 00:00:00
Proceeding: ICCV 2019 10
Tasks: Decoder

The learning of Transformation-Equivariant Representations (TERs), which is introduced by Hinton et al. \cite{hinton2011transforming}, has been considered as a principle to reveal visual structures under various transformations. It contains the celebrated Convolutional Neural Networks (CNNs) as a special case that only equivary to the translations. In contrast, we seek to train TERs for a generic class of transformations and train them in an {\em unsupervised} fashion. To this end, we present a novel principled method by Autoencoding Variational Transformations (AVT), compared with the conventional approach to autoencoding data. Formally, given transformed images, the AVT seeks to train the networks by maximizing the mutual information between the transformations and representations. This ensures the resultant TERs of individual images contain the {\em intrinsic} information about their visual structures that would equivary {\em extricably} under various transformations in a generalized {\em nonlinear} case. Technically, we show that the resultant optimization problem can be efficiently solved by maximizing a variational lower-bound of the mutual information. This variational approach introduces a transformation decoder to approximate the intractable posterior of transformations, resulting in an autoencoding architecture with a pair of the representation encoder and the transformation decoder. Experiments demonstrate the proposed AVT model sets a new record for the performances on unsupervised tasks, greatly closing the performance gap to the supervised models.
Measuring the Similarity between Materials with an Emphasis on the Materials Distinctiveness
Authors: Tran-Thai Dang, Tien-Lam Pham, Hiori Kino, Takashi Miyake, Hieu-Chi Dam
Date: 2019-03-23 00:00:00
Tasks: regression

In this study, we establish a basis for selecting similarity measures when
applying machine learning techniques to solve materials science problems. This
selection is considered with an emphasis on the distinctiveness between
materials that reflect their nature well. We perform a case study with a
dataset of rare-earth transition metal crystalline compounds represented using
the Orbital Field Matrix descriptor and the Coulomb Matrix descriptor. We
perform predictions of the formation energies using k-nearest neighbors
regression, ridge regression, and kernel ridge regression. Through detailed
analyses of the yield prediction accuracy, we examine the relationship between
the characteristics of the material representation and similarity measures, and
the complexity of the energy function they can capture. Empirical experiments
and theoretical analysis reveal that similarity measures and kernels that
minimize the loss of materials distinctiveness improve the prediction
performance.
V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation
Authors: Anh Nguyen, Thanh-Toan Do, Ian Reid, Darwin G. Caldwell, Nikos G. Tsagarakis
Date: 2019-03-23 00:00:00
Tasks: Decoder

We propose V2CNet, a new deep learning framework to automatically translate
the demonstration videos to commands that can be directly used in robotic
applications. Our V2CNet has two branches and aims at understanding the
demonstration video in a fine-grained manner. The first branch has the
encoder-decoder architecture to encode the visual features and sequentially
generate the output words as a command, while the second branch uses a Temporal
Convolutional Network (TCN) to learn the fine-grained actions. By jointly
training both branches, the network is able to model the sequential information
of the command, while effectively encodes the fine-grained actions. The
experimental results on our new large-scale dataset show that V2CNet
outperforms recent state-of-the-art methods by a substantial margin, while its
output can be applied in real robotic applications. The source code and trained
models will be made available.
Aligning Users Across Social Networks Using Network Embedding
Authors: Li Liu, William K. Cheung, Xin Li, Lejian Liao
Date: 2016-06-09 00:00:00
Proceeding: IJCAI 2016 6
Tasks: Network Embedding

Li Liu,1 William K. Cheung,2 Xin Li,1⇤ and Lejian Liao1
Cross view link prediction by learning noise-resilient representation consensus
Authors: Xiaokai Wei, Linchuan Xu, Bokai Cao and Philip S. Yu
Date: 2017-04-03 00:00:00
Proceeding: WWW '17 2017 4
Tasks: Attribute, Link Prediction, Prediction, Representation Learning

Link Prediction has been an important task for social and information networks. Existing approaches usually assume the completeness of network structure. However, in many real-world networks, the links and node attributes can usually be partially observable. In this paper, we study the problem of Cross View Link Prediction (CVLP) on partially observable networks, where the focus is to recommend nodes with only links to nodes with only attributes (or vice versa). We aim to bridge the information gap by learning a robust consensus for link-based and attribute-based representations so that nodes become comparable in the latent space. Also, the link-based and attribute-based representations can lend strength to each other via this consensus learning. Moreover, attribute selection is performed jointly with the representation learning to alleviate the effect of noisy high-dimensional attributes. We present two instantiations of this framework with different loss functions and develop an alternating optimization framework to solve the problem. Experimental results on four real-world datasets show the proposed algorithm outperforms the baseline methods significantly for crossview link prediction.
TDBSCAN: Spatiotemporal Density Clustering
Authors: Chen W, Ji M, Wang J
Date: 2014-01-01 00:00:00
Proceeding: International Journal of Online and Biomedical Engineering 2014 1
Tasks: Clustering, Unsupervised Spatial Clustering

Trajectory data generated from personal or vehicle use of GPS devices can be utilized for travel analysis and traffic information service, whereas trip segmentation is a key step toward the semantic labelling of the trajectories. Two issues are difficult to deal with by the traditional density-based algorithms, i. e. multiple stops at the same spatial location with different visit times and non-consecutive point sequence for stop definition due to signal drifting. This article aims to develop a modified density-based clustering algorithm, named T-DBSCAN, by considering the time-sequential characteristics of the GPS points along a trajectory. Two new premises (i.e. state continuity within a single stop and temporal disjuncture among stops) were proposed as a theoretical basis for regulating the trajectory point selection in clustering. An empirical test was performed using a GPS-based personal travel dataset collected in the city of Shanghai to compare T-DBSCAN against DBSCAN. The results indicated that T-DBSCAN effectively improved both accuracy and computational speed in trajectory segmentation.
A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data
Authors: C. Titus Brown, Adina Howe, Qingpeng Zhang, Alexis B. Pyrkosz, Timothy H. Brom
Date: 2012-03-21 00:00:00

Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified
single-cell genomes, and metagenomes has enabled investigation of a wide range
of organisms and ecosystems. However, sampling variation in short-read data
sets and high sequencing error rates of modern sequencers present many new
computational challenges in data interpretation. These challenges have led to
the development of new classes of mapping tools and {\em de novo} assemblers.
These algorithms are challenged by the continued improvement in sequencing
throughput. We here describe digital normalization, a single-pass computational
algorithm that systematizes coverage in shotgun sequencing data sets, thereby
decreasing sampling variation, discarding redundant data, and removing the
majority of errors. Digital normalization substantially reduces the size of
shotgun data sets and decreases the memory and time requirements for {\em de
novo} sequence assembly, all without significantly impacting content of the
generated contigs. We apply digital normalization to the assembly of microbial
genomic data, amplified single-cell genomic data, and transcriptomic data. Our
implementation is freely available for use and modification.
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Authors: Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca
Date: 2019-03-26 00:00:00
Tasks: GPU, Image Captioning, Neural Architecture Search, object-detection, Object Detection, Style Transfer, Transfer Learning

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN) to predict network accuracies for biasing the search toward a promising region. To amortize the network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed design and reduces the number of epochs in evaluating a network by transfer learning guided with the tree structure in MCTS. In 12 GPU days and 1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency. Particularly, we also evaluate AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more sample efficient than Random Search and Regularized Evolution in finding the global optimum. Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection.
An Alternating Manifold Proximal Gradient Method for Sparse PCA and Sparse CCA
Authors: Shixiang Chen, Shiqian Ma, Lingzhou Xue, Hui Zou
Date: 2019-03-27 00:00:00

Sparse principal component analysis (PCA) and sparse canonical correlation
analysis (CCA) are two essential techniques from high-dimensional statistics
and machine learning for analyzing large-scale data. Both problems can be
formulated as an optimization problem with nonsmooth objective and nonconvex
constraints. Since non-smoothness and nonconvexity bring numerical
difficulties, most algorithms suggested in the literature either solve some
relaxations or are heuristic and lack convergence guarantees. In this paper, we
propose a new alternating manifold proximal gradient method to solve these two
high-dimensional problems and provide a unified convergence analysis. Numerical
experiment results are reported to demonstrate the advantages of our algorithm.
Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysis
Authors: Noé Tits, Fengna Wang, Kevin El Haddad, Vincent Pagel, Thierry Dutoit
Date: 2019-03-27 00:00:00
Tasks: Emotional Speech Synthesis, Expressive Speech Synthesis, Learning Network Representations, Speech Emotion Recognition, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

The field of Text-to-Speech has experienced huge improvements last years
benefiting from deep learning techniques. Producing realistic speech becomes
possible now. As a consequence, the research on the control of the
expressiveness, allowing to generate speech in different styles or manners, has
attracted increasing attention lately. Systems able to control style have been
developed and show impressive results. However the control parameters often
consist of latent variables and remain complex to interpret. In this paper, we
analyze and compare different latent spaces and obtain an interpretation of
their influence on expressive speech. This will enable the possibility to build
controllable speech synthesis systems with an understandable behaviour.
GAN-based Pose-aware Regulation for Video-based Person Re-identification
Authors: Alessandro Borgia, Yang Hua, Elyor Kodirov, Neil M. Robertson
Date: 2019-03-27 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

Video-based person re-identification deals with the inherent difficulty of
matching unregulated sequences with different length and with incomplete target
pose/viewpoint structure. Common approaches operate either by reducing the
problem to the still images case, facing a significant information loss, or by
exploiting inter-sequence temporal dependencies as in Siamese Recurrent Neural
Networks or in gait analysis. However, in all cases, the inter-sequences
pose/viewpoint misalignment is not considered, and the existing spatial
approaches are mostly limited to the still images context. To this end, we
propose a novel approach that can exploit more effectively the rich video
information, by accounting for the role that the changing pose/viewpoint factor
plays in the sequences matching process. Specifically, our approach consists of
two components. The first one attempts to complement the original
pose-incomplete information carried by the sequences with synthetic
GAN-generated images, and fuse their feature vectors into a more discriminative
viewpoint-insensitive embedding, namely Weighted Fusion (WF). Another one
performs an explicit pose-based alignment of sequence pairs to promote coherent
feature matching, namely Weighted-Pose Regulation (WPR). Extensive experiments
on two large video-based benchmark datasets show that our approach outperforms
considerably existing methods.
Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery
Authors: Ries Uittenbogaard, Clint Sebastian, Julien Vijverberg, Bas Boom, Dariu M. Gavrila, Peter H. N. de With
Date: 2019-03-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Benchmarking, Object, Segmentation, Semantic Segmentation

The current paradigm in privacy protection in street-view images is to detect
and blur sensitive information. In this paper, we propose a framework that is
an alternative to blurring, which automatically removes and inpaints moving
objects (e.g. pedestrians, vehicles) in street-view imagery. We propose a novel
moving object segmentation algorithm exploiting consistencies in depth across
multiple street-view images that are later combined with the results of a
segmentation network. The detected moving objects are removed and inpainted
with information from other views, to obtain a realistic output image such that
the moving object is not visible anymore. We evaluate our results on a dataset
of 1000 images to obtain a peak noise-to-signal ratio (PSNR) and L1 loss of
27.2 dB and 2.5%, respectively. To ensure the subjective quality, To assess
overall quality, we also report the results of a survey conducted on 35
professionals, asked to visually inspect the images whether object removal and
inpainting had taken place. The inpainting dataset will be made publicly
available for scientific benchmarking purposes at
https://research.cyclomedia.com
Autoregressive Policies for Continuous Control Deep Reinforcement Learning
Authors: Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra
Date: 2019-03-27 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning algorithms rely on exploration to discover new
behaviors, which is typically achieved by following a stochastic policy. In
continuous control tasks, policies with a Gaussian distribution have been
widely adopted. Gaussian exploration however does not result in smooth
trajectories that generally correspond to safe and rewarding behaviors in
practical tasks. In addition, Gaussian policies do not result in an effective
exploration of an environment and become increasingly inefficient as the action
rate increases. This contributes to a low sample efficiency often observed in
learning continuous control tasks. We introduce a family of stationary
autoregressive (AR) stochastic processes to facilitate exploration in
continuous control domains. We show that proposed processes possess two
desirable features: subsequent process observations are temporally coherent
with continuously adjustable degree of coherence, and the process stationary
distribution is standard normal. We derive an autoregressive policy (ARP) that
implements such processes maintaining the standard agent-environment interface.
We show how ARPs can be easily used with the existing off-the-shelf learning
algorithms. Empirically we demonstrate that using ARPs results in improved
exploration and sample efficiency in both simulated and real world domains,
and, furthermore, provides smooth exploration trajectories that enable safe
operation of robotic hardware.
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Authors: Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych
Date: 2019-03-27 00:00:00
Proceeding: NAACL 2019 6
Tasks: Adversarial Attack, Sentence

Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual input perturbations demonstrate. We then investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82\%. We then explore three shielding methods---visual character embeddings, adversarial training, and rule-based recovery---which substantially improve the robustness of the models. However, the shielding methods still fall behind performances achieved in non-attack scenarios, which demonstrates the difficulty of dealing with visual attacks.