pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Twitter User Socioeconomic Attributes with Network and Language Information
Authors: Nikolaos Aletras, Benjamin Paul Chamberlain
Date: 2018-04-11 00:00:00
Tasks: Recommendation Systems

Inferring socioeconomic attributes of social media users such as occupation
and income is an important problem in computational social science. Automated
inference of such characteristics has applications in personalised recommender
systems, targeted computational advertising and online political campaigning.
While previous work has shown that language features can reliably predict
socioeconomic attributes on Twitter, employing information coming from users'
social networks has not yet been explored for such complex user
characteristics. In this paper, we describe a method for predicting the
occupational class and the income of Twitter users given information extracted
from their extended networks by learning a low-dimensional vector
representation of users, i.e. graph embeddings. We use this representation to
train predictive models for occupational class and income. Results on two
publicly available datasets show that our method consistently outperforms the
state-of-the-art methods in both tasks. We also obtain further significant
improvements when we combine graph embeddings with textual features,
demonstrating that social network and language information are complementary.
SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation
Authors: Ye Zhang, Nan Ding, Radu Soricut
Date: 2018-04-11 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Text Generation

Supervised training of abstractive language generation models results in
learning conditional probabilities over language sequences based on the
supervised training signal. When the training signal contains a variety of
writing styles, such models may end up learning an 'average' style that is
directly influenced by the training data make-up and cannot be controlled by
the needs of an application. We describe a family of model architectures
capable of capturing both generic language characteristics via shared model
parameters, as well as particular style characteristics via private model
parameters. Such models are able to generate language according to a specific
learned style, while still taking advantage of their power to model generic
language phenomena. Furthermore, we describe an extension that uses a mixture
of output distributions from all learned styles to perform on-the fly style
adaptation based on the textual input alone. Experimentally, we find that the
proposed models consistently outperform models that encapsulate single-style or
average-style language generation capabilities.
End-to-End Learning of Communications Systems Without a Channel Model
Authors: Fayçal Ait Aoudia, Jakob Hoydis
Date: 2018-04-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The idea of end-to-end learning of communications systems through neural
network -based autoencoders has the shortcoming that it requires a
differentiable channel model. We present in this paper a novel learning
algorithm which alleviates this problem. The algorithm iterates between
supervised training of the receiver and reinforcement learning -based training
of the transmitter. We demonstrate that this approach works as well as fully
supervised methods on additive white Gaussian noise (AWGN) and Rayleigh
block-fading (RBF) channels. Surprisingly, while our method converges slower on
AWGN channels than supervised training, it converges faster on RBF channels.
Our results are a first step towards learning of communications systems over
any type of channel without prior assumptions.
Detail-Preserving Pooling in Deep Networks
Authors: Faraz Saeedan, Nicolas Weber, Michael Goesele, Stefan Roth
Date: 2018-04-11 00:00:00
Proceeding: CVPR 2018 6

Most convolutional neural networks use some method for gradually downscaling
the size of the hidden layers. This is commonly referred to as pooling, and is
applied to reduce the number of parameters, improve invariance to certain
distortions, and increase the receptive field size. Since pooling by nature is
a lossy process, it is crucial that each such layer maintains the portion of
the activations that is most important for the network's discriminability. Yet,
simple maximization or averaging over blocks, max or average pooling, or plain
downsampling in the form of strided convolutions are the standard. In this
paper, we aim to leverage recent results on image downscaling for the purposes
of deep learning. Inspired by the human visual system, which focuses on local
spatial changes, we propose detail-preserving pooling (DPP), an adaptive
pooling method that magnifies spatial changes and preserves important
structural detail. Importantly, its parameters can be learned jointly with the
rest of the network. We analyze some of its theoretical properties and show its
empirical benefits on several datasets and networks, where DPP consistently
outperforms previous pooling approaches.
Seed-Point Detection of Clumped Convex Objects by Short-Range Attractive Long-Range Repulsive Particle Clustering
Authors: James Kapaldo, Xu Han, Domingo Mery
Date: 2018-04-11 00:00:00
Tasks: Clustering

Locating the center of convex objects is important in both image processing
and unsupervised machine learning/data clustering fields. The automated
analysis of biological images uses both of these fields for locating cell
nuclei and for discovering new biological effects or cell phenotypes. In this
work, we develop a novel clustering method for locating the centers of
overlapping convex objects by modeling particles that interact by a short-range
attractive and long-range repulsive potential and are confined to a potential
well created from the data. We apply this method to locating the centers of
clumped nuclei in cultured cells, where we show that it results in a
significant improvement over existing methods (8.2% in F$_1$ score); and we
apply it to unsupervised learning on a difficult data set that has rare classes
without local density maxima, and show it is able to well locate cluster
centers when other clustering techniques fail.
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
Authors: Meiguang Jin, Givi Meishvili, Paolo Favaro
Date: 2018-04-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Computational Efficiency, Deblurring

We present a method to extract a video sequence from a single motion-blurred
image. Motion-blurred images are the result of an averaging process, where
instant frames are accumulated over time during the exposure of the sensor.
Unfortunately, reversing this process is nontrivial. Firstly, averaging
destroys the temporal ordering of the frames. Secondly, the recovery of a
single frame is a blind deconvolution task, which is highly ill-posed. We
present a deep learning scheme that gradually reconstructs a temporal ordering
by sequentially extracting pairs of frames. Our main contribution is to
introduce loss functions invariant to the temporal order. This lets a neural
network choose during training what frame to output among the possible
combinations. We also address the ill-posedness of deblurring by designing a
network with a large receptive field and implemented via resampling to achieve
a higher computational efficiency. Our proposed method can successfully
retrieve sharp image sequences from a single motion blurred image and can
generalize well on synthetic and real datasets captured with different cameras.
Deep Outdoor Illumination Estimation
Authors: Yannick Hold-Geoffroy, Kalyan Sunkavalli, Sunil Hadap, Emiliano Gambaretto, Jean-François Lalonde
Date: 2016-11-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: Outdoor Light Source Estimation

We present a CNN-based technique to estimate high-dynamic range outdoor
illumination from a single low dynamic range image. To train the CNN, we
leverage a large dataset of outdoor panoramas. We fit a low-dimensional
physically-based outdoor illumination model to the skies in these panoramas
giving us a compact set of parameters (including sun position, atmospheric
conditions, and camera parameters). We extract limited field-of-view images
from the panoramas, and train a CNN with this large set of input image--output
lighting parameter pairs. Given a test image, this network can be used to infer
illumination parameters that can, in turn, be used to reconstruct an outdoor
illumination environment map. We demonstrate that our approach allows the
recovery of plausible illumination conditions and enables photorealistic
virtual object insertion from a single image. An extensive evaluation on both
the panorama dataset and captured HDR environment maps shows that our technique
significantly outperforms previous solutions to this problem.
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access
Authors: Chao Gan, Ruida Zhou, Jing Yang, Cong Shen
Date: 2018-04-11 00:00:00

In this paper, we investigate cost-aware joint learning and optimization for
multi-channel opportunistic spectrum access in a cognitive radio system. We
investigate a discrete time model where the time axis is partitioned into
frames. Each frame consists of a sensing phase, followed by a transmission
phase. During the sensing phase, the user is able to sense a subset of channels
sequentially before it decides to use one of them in the following transmission
phase. We assume the channel states alternate between busy and idle according
to independent Bernoulli random processes from frame to frame. To capture the
inherent uncertainty in channel sensing, we assume the reward of each
transmission when the channel is idle is a random variable. We also associate
random costs with sensing and transmission actions. Our objective is to
understand how the costs and reward of the actions would affect the optimal
behavior of the user in both offline and online settings, and design the
corresponding opportunistic spectrum access strategies to maximize the expected
cumulative net reward (i.e., reward-minus-cost). We start with an offline
setting where the statistics of the channel status, costs and reward are known
beforehand. We show that the the optimal policy exhibits a recursive double
threshold structure, and the user needs to compare the channel statistics with
those thresholds sequentially in order to decide its actions. With such
insights, we then study the online setting, where the statistical information
of the channels, costs and reward are unknown a priori. We judiciously balance
exploration and exploitation, and show that the cumulative regret scales in
O(log T). We also establish a matched lower bound, which implies that our
online algorithm is order-optimal. Simulation results corroborate our
theoretical analysis.
Active Regression via Linear-Sample Sparsification
Authors: Xue Chen, Eric Price
Date: 2017-11-27 00:00:00
Tasks: Active Learning, regression

We present an approach that improves the sample complexity for a variety of
curve fitting problems, including active learning for linear regression,
polynomial regression, and continuous sparse Fourier transforms. In the active
linear regression problem, one would like to estimate the least squares
solution $\beta^*$ minimizing $\|X\beta - y\|_2$ given the entire unlabeled
dataset $X \in \mathbb{R}^{n \times d}$ but only observing a small number of
labels $y_i$. We show that $O(d)$ labels suffice to find a constant factor
approximation $\tilde{\beta}$:
  \[
  \mathbb{E}[\|X\tilde{\beta} - y\|_2^2] \leq 2 \mathbb{E}[\|X \beta^* -
y\|_2^2].
  \] This improves on the best previous result of $O(d \log d)$ from leverage
score sampling. We also present results for the \emph{inductive} setting,
showing when $\tilde{\beta}$ will generalize to fresh samples; these apply to
continuous settings such as polynomial regression. Finally, we show how the
techniques yield improved results for the non-linear sparse Fourier transform
setting.
A real-time and unsupervised face Re-Identification system for Human-Robot Interaction
Authors: Yujiang Wang, Jie Shen, Stavros Petridis, Maja Pantic
Date: 2018-04-10 00:00:00
Tasks: Clustering, Face Recognition, Online Clustering

In the context of Human-Robot Interaction (HRI), face Re-Identification (face Re-ID) aims to verify if certain detected faces have already been observed by robots. The ability of distinguishing between different users is crucial in social robots as it will enable the robot to tailor the interaction strategy toward the users' individual preferences. So far face recognition research has achieved great success, however little attention has been paid to the realistic applications of Face Re-ID in social robots. In this paper, we present an effective and unsupervised face Re-ID system which simultaneously re-identifies multiple faces for HRI. This Re-ID system employs Deep Convolutional Neural Networks to extract features, and an online clustering algorithm to determine the face's ID. Its performance is evaluated on two datasets: the TERESA video dataset collected by the TERESA robot, and the YouTube Face Dataset (YTF Dataset). We demonstrate that the optimised combination of techniques achieves an overall 93.55% accuracy on TERESA dataset and an overall 90.41% accuracy on YTF dataset. We have implemented the proposed method into a software module in the HCI^2 Framework for it to be further integrated into the TERESA robot, and has achieved real-time performance at 10~26 Frames per second.
Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation
Authors: Hyojin Bahng, Seungjoo Yoo, Wonwoong Cho, David K. Park, Ziming Wu, Xiaojuan Ma, Jaegul Choo
Date: 2018-04-11 00:00:00
Proceeding: ECCV 2018 9
Tasks: Colorization, Image Colorization, Sentence

This paper proposes a novel approach to generate multiple color palettes that
reflect the semantics of input text and then colorize a given grayscale image
according to the generated color palette. In contrast to existing approaches,
our model can understand rich text, whether it is a single word, a phrase, or a
sentence, and generate multiple possible palettes from it. For this task, we
introduce our manually curated dataset called Palette-and-Text (PAT). Our
proposed model called Text2Colors consists of two conditional generative
adversarial networks: the text-to-palette generation networks and the
palette-based colorization networks. The former captures the semantics of the
text input and produce relevant color palettes. The latter colorizes a
grayscale image using the generated color palette. Our evaluation results show
that people preferred our generated palettes over ground truth palettes and
that our model can effectively reflect the given palette when colorizing an
image.
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences
Authors: Zhengyuan Yang, Yuncheng Li, Jianchao Yang, Jiebo Luo
Date: 2018-01-31 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Action recognition with 3D skeleton sequences is becoming popular due to its
speed and robustness. The recently proposed Convolutional Neural Networks (CNN)
based methods have shown good performance in learning spatio-temporal
representations for skeleton sequences. Despite the good recognition accuracy
achieved by previous CNN based methods, there exist two problems that
potentially limit the performance. First, previous skeleton representations are
generated by chaining joints with a fixed order. The corresponding semantic
meaning is unclear and the structural information among the joints is lost.
Second, previous models do not have an ability to focus on informative joints.
The attention mechanism is important for skeleton based action recognition
because there exist spatio-temporal key stages while the joint predictions can
be inaccurate. To solve these two problems, we propose a novel CNN based method
for skeleton based action recognition. We first redesign the skeleton
representations with a depth-first tree traversal order, which enhances the
semantic meaning of skeleton images and better preserves the associated
structural information. We then propose the idea of a two-branch attention
architecture that focuses on spatio-temporal key stages and filters out
unreliable joint predictions. A base attention model with the simplest
structure is first introduced. By improving the structures in both branches, we
further propose a Global Long-sequence Attention Network (GLAN). Furthermore,
in order to adjust the kernel's spatio-temporal aspect ratios and better
capture long term dependencies, we propose a Sub-Sequence Attention Network
(SSAN) that takes sub-image sequences as inputs. Our experiment results on NTU
RGB+D and SBU Kinetic Interaction outperforms the state-of-the-art. The model
is further validated on noisy estimated poses from UCF101 and Kinetics.
Flexible and Scalable Deep Learning with MMLSpark
Authors: Mark Hamilton, Sudarshan Raghunathan, Akshaya Annavajhala, Danil Kirsanov, Eduardo de Leon, Eli Barzilay, Ilya Matiach, Joe Davison, Maureen Busch, Miruna Oprescu, Ratan Sur, Roope Astala, Tong Wen, ChangYoung Park
Date: 2018-04-11 00:00:00
Tasks: Deep Learning, Distributed Computing

In this work we detail a novel open source library, called MMLSpark, that
combines the flexible deep learning library Cognitive Toolkit, with the
distributed computing framework Apache Spark. To achieve this, we have
contributed Java Language bindings to the Cognitive Toolkit, and added several
new components to the Spark ecosystem. In addition, we also integrate the
popular image processing library OpenCV with Spark, and present a tool for the
automated generation of PySpark wrappers from any SparkML estimator and use
this tool to expose all work to the PySpark ecosystem. Finally, we provide a
large library of tools for working and developing within the Spark ecosystem.
We apply this work to the automated classification of Snow Leopards from camera
trap images, and provide an end to end solution for the non-profit conservation
organization, the Snow Leopard Trust.
Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
Authors: Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William R. Schwartz, Jefersson A. dos Santos
Date: 2018-04-11 00:00:00
Tasks: Semantic Segmentation

Semantic segmentation requires methods capable of learning high-level
features while dealing with large volume of data. Towards such goal,
Convolutional Networks can learn specific and adaptable features based on the
data. However, these networks are not capable of processing a whole remote
sensing image, given its huge size. To overcome such limitation, the image is
processed using fixed size patches. The definition of the input patch size is
usually performed empirically (evaluating several sizes) or imposed (by network
constraint). Both strategies suffer from drawbacks and could not lead to the
best patch size. To alleviate this problem, several works exploited
multi-context information by combining networks or layers. This process
increases the number of parameters resulting in a more difficult model to
train. In this work, we propose a novel technique to perform semantic
segmentation of remote sensing images that exploits a multi-context paradigm
without increasing the number of parameters while defining, in training time,
the best patch size. The main idea is to train a dilated network with distinct
patch sizes, allowing it to capture multi-context characteristics from
heterogeneous contexts. While processing these varying patches, the network
provides a score for each patch size, helping in the definition of the best
size for the current scenario. A systematic evaluation of the proposed
algorithm is conducted using four high-resolution remote sensing datasets with
very distinct properties. Our results show that the proposed algorithm provides
improvements in pixelwise classification accuracy when compared to
state-of-the-art methods.
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
Authors: Leshem Choshen, Lior Fox, Yonatan Loewenstein
Date: 2018-04-11 00:00:00
Proceeding: ICLR 2018 1
Tasks: Reinforcement Learning, Reinforcement Learning (RL), World Knowledge

Exploration is a fundamental aspect of Reinforcement Learning, typically
implemented using stochastic action-selection. Exploration, however, can be
more efficient if directed toward gaining new world knowledge. Visit-counters
have been proven useful both in practice and in theory for directed
exploration. However, a major limitation of counters is their locality. While
there are a few model-based solutions to this shortcoming, a model-free
approach is still missing. We propose $E$-values, a generalization of counters
that can be used to evaluate the propagating exploratory value over
state-action trajectories. We compare our approach to commonly used RL
techniques, and show that using $E$-values improves learning and performance
over traditional counters. We also show how our method can be implemented with
function approximation to efficiently learn continuous MDPs. We demonstrate
this by showing that our approach surpasses state of the art performance in the
Freeway Atari 2600 game.
DLL: A Blazing Fast Deep Neural Network Library
Authors: Baptiste Wicht, Jean Hennebert, Andreas Fischer
Date: 2018-04-11 00:00:00
Tasks: CPU, Deep Learning, General Classification, GPU

Deep Learning Library (DLL) is a new library for machine learning with deep
neural networks that focuses on speed. It supports feed-forward neural networks
such as fully-connected Artificial Neural Networks (ANNs) and Convolutional
Neural Networks (CNNs). It also has very comprehensive support for Restricted
Boltzmann Machines (RBMs) and Convolutional RBMs. Our main motivation for this
work was to propose and evaluate novel software engineering strategies with
potential to accelerate runtime for training and inference. Such strategies are
mostly independent of the underlying deep learning algorithms. On three
different datasets and for four different neural network models, we compared
DLL to five popular deep learning frameworks. Experimentally, it is shown that
the proposed framework is systematically and significantly faster on CPU and
GPU. In terms of classification performance, similar accuracies as the other
frameworks are reported.
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
Authors: Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, Stephen Clark
Date: 2018-04-11 00:00:00
Proceeding: ICLR 2018 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The ability of algorithms to evolve or learn (compositional) communication
protocols has traditionally been studied in the language evolution literature
through the use of emergent communication tasks. Here we scale up this research
by using contemporary deep learning methods and by training
reinforcement-learning neural network agents on referential communication
games. We extend previous work, in which agents were trained in symbolic
environments, by developing agents which are able to learn from raw pixel data,
a more challenging and realistic input representation. We find that the degree
of structure found in the input data affects the nature of the emerged
protocols, and thereby corroborate the hypothesis that structured compositional
language is most likely to emerge when agents perceive the world as being
structured.