pwc-archive/papers-with-abstracts huggingface dataset:
3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-Art
Authors: Xian-Feng Han, Shi-Jie Sun, Xiang-Yu Song, Guo-Qiang Xiao
Date: 2018-02-07 00:00:00

The introduction of inexpensive 3D data acquisition devices has promisingly facilitated the wide availability and popularity of 3D point cloud, which attracts more attention to the effective extraction of novel 3D point cloud descriptors for accuracy of the efficiency of 3D computer vision tasks in recent years. However, how to develop discriminative and robust feature descriptors from 3D point cloud remains a challenging task due to their intrinsic characteristics. In this paper, we give a comprehensively insightful investigation of the existing 3D point cloud descriptors. These methods can principally be divided into two categories according to the advancement of descriptors: hand-crafted based and deep learning-based apporaches, which will be further discussed from the perspective of elaborate classification, their advantages, and limitations. Finally, we present the future research direction of the extraction of 3D point cloud descriptors.
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Authors: Patrick L. McDermott, Christopher K. Wikle
Date: 2017-11-02 00:00:00
Tasks: Spatio-Temporal Forecasting, Uncertainty Quantification

Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used
in the machine learning and dynamical systems literature to represent complex
dynamical or sequential relationships between variables. More recently, as deep
learning models have become more common, RNNs have been used to forecast
increasingly complicated systems. Dynamical spatio-temporal processes represent
a class of complex systems that can potentially benefit from these types of
models. Although the RNN literature is expansive and highly developed,
uncertainty quantification is often ignored. Even when considered, the
uncertainty is generally quantified without the use of a rigorous framework,
such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a
more formal framework while maintaining the forecast accuracy that makes these
models appealing, by presenting a Bayesian RNN model for nonlinear
spatio-temporal forecasting. Additionally, we make simple modifications to the
basic RNN to help accommodate the unique nature of nonlinear spatio-temporal
data. The proposed model is applied to a Lorenz simulation and two real-world
nonlinear spatio-temporal forecasting applications.
Curiosity-driven reinforcement learning with homeostatic regulation
Authors: Ildefons Magrans de Abril, Ryota Kanai
Date: 2018-01-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a curiosity reward based on information theory principles and
consistent with the animal instinct to maintain certain critical parameters
within a bounded range. Our experimental validation shows the added value of
the additional homeostatic drive to enhance the overall information gain of a
reinforcement learning agent interacting with a complex environment using
continuous actions. Our method builds upon two ideas: i) To take advantage of a
new Bellman-like equation of information gain and ii) to simplify the
computation of the local rewards by avoiding the approximation of complex
distributions over continuous states and actions.
Spectral Image Visualization Using Generative Adversarial Networks
Authors: Siyu Chen, Danping Liao, Yuntao Qian
Date: 2018-02-07 00:00:00
Tasks: Generative Adversarial Network

Spectral images captured by satellites and radio-telescopes are analyzed to
obtain information about geological compositions distributions, distant asters
as well as undersea terrain. Spectral images usually contain tens to hundreds
of continuous narrow spectral bands and are widely used in various fields. But
the vast majority of those image signals are beyond the visible range, which
calls for special visualization technique. The visualizations of spectral
images shall convey as much information as possible from the original signal
and facilitate image interpretation. However, most of the existing visualizatio
methods display spectral images in false colors, which contradict with human's
experience and expectation. In this paper, we present a novel visualization
generative adversarial network (GAN) to display spectral images in natural
colors. To achieve our goal, we propose a loss function which consists of an
adversarial loss and a structure loss. The adversarial loss pushes our solution
to the natural image distribution using a discriminator network that is trained
to differentiate between false-color images and natural-color images. We also
use a cycle loss as the structure constraint to guarantee structure
consistency. Experimental results show that our method is able to generate
structure-preserved and natural-looking visualizations.
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts
Authors: Zongwei Zhou, Jae Y. Shin, Suryakanth R. Gurudu, Michael B. Gotway, Jianming Liang
Date: 2018-02-03 00:00:00
Tasks: Active Learning, Transfer Learning

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such large annotated datasets, as annotating medical images is not only tedious, laborious, and time consuming, but it also demands costly, specialty-oriented skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework, which starts directly with a pre-trained CNN to seek "worthy" samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning. We have evaluated our method using three distinct medical imaging applications, demonstrating that it can reduce annotation efforts by at least half compared with random selection.
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Authors: Rosaura G. Vidal, Sreya Banerjee, Klemen Grm, Vitomir Struc, Walter J. Scheirer
Date: 2017-10-09 00:00:00
Tasks: Deblurring, General Classification, Image Restoration, Super-Resolution

Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal in an input image. But the historically divergent goals of
the computational photography and visual recognition communities have created a
significant need for more work in this direction. To facilitate new research,
we introduce a new benchmark dataset called UG^2, which contains three
difficult real-world scenarios: uncontrolled videos taken by UAVs and manned
gliders, as well as controlled videos taken on the ground. Over 160,000
annotated frames forhundreds of ImageNet classes are available, which are used
for baseline experiments that assess the impact of known and unknown image
artifacts and other conditions on common deep learning-based object
classification approaches. Further, current image restoration and enhancement
techniques are evaluated by determining whether or not theyimprove baseline
classification performance. Results showthat there is plenty of room for
algorithmic innovation, making this dataset a useful tool going forward.
From Game-theoretic Multi-agent Log Linear Learning to Reinforcement Learning
Authors: Mohammadhosein Hasanbeig, Lacra Pavel
Date: 2018-02-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The main focus of this paper is on enhancement of two types of game-theoretic
learning algorithms: log-linear learning and reinforcement learning. The
standard analysis of log-linear learning needs a highly structured environment,
i.e. strong assumptions about the game from an implementation perspective. In
this paper, we introduce a variant of log-linear learning that provides
asymptotic guarantees while relaxing the structural assumptions to include
synchronous updates and limitations in information available to the players. On
the other hand, model-free reinforcement learning is able to perform even under
weaker assumptions on players' knowledge about the environment and other
players' strategies. We propose a reinforcement algorithm that uses a
double-aggregation scheme in order to deepen players' insight about the
environment and constant learning step-size which achieves a higher convergence
rate. Numerical experiments are conducted to verify each algorithm's robustness
and performance.
A Critical Investigation of Deep Reinforcement Learning for Navigation
Authors: Vikas Dhiman, Shurjo Banerjee, Brent Griffin, Jeffrey M. Siskind, Jason J. Corso
Date: 2018-02-07 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The navigation problem is classically approached in two steps: an exploration
step, where map-information about the environment is gathered; and an
exploitation step, where this information is used to navigate efficiently. Deep
reinforcement learning (DRL) algorithms, alternatively, approach the problem of
navigation in an end-to-end fashion. Inspired by the classical approach, we ask
whether DRL algorithms are able to inherently explore, gather and exploit
map-information over the course of navigation. We build upon Mirowski et al.
[2017] work and introduce a systematic suite of experiments that vary three
parameters: the agent's starting location, the agent's target location, and the
maze structure. We choose evaluation metrics that explicitly measure the
algorithm's ability to gather and exploit map-information. Our experiments show
that when trained and tested on the same maps, the algorithm successfully
gathers and exploits map-information. However, when trained and tested on
different sets of maps, the algorithm fails to transfer the ability to gather
and exploit map-information to unseen maps. Furthermore, we find that when the
goal location is randomized and the map is kept static, the algorithm is able
to gather and exploit map-information but the exploitation is far from optimal.
We open-source our experimental suite in the hopes that it serves as a
framework for the comparison of future algorithms and leads to the discovery of
robust alternatives to classical navigation methods.
Universal Deep Neural Network Compression
Authors: Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
Date: 2018-02-07 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018
Tasks: Neural Network Compression, Quantization

In this paper, we investigate lossy compression of deep neural networks
(DNNs) by weight quantization and lossless source coding for memory-efficient
deployment. Whereas the previous work addressed non-universal scalar
quantization and entropy coding of DNN weights, we for the first time introduce
universal DNN compression by universal vector quantization and universal source
coding. In particular, we examine universal randomized lattice quantization of
DNNs, which randomizes DNN weights by uniform random dithering before lattice
quantization and can perform near-optimally on any source without relying on
knowledge of its probability distribution. Moreover, we present a method of
fine-tuning vector quantized DNNs to recover the performance loss after
quantization. Our experimental results show that the proposed universal DNN
compression scheme compresses the 32-layer ResNet (trained on CIFAR-10) and the
AlexNet (trained on ImageNet) with compression ratios of $47.1$ and $42.5$,
respectively.
Learning Role-based Graph Embeddings
Authors: Nesreen K. Ahmed, Ryan Rossi, John Boaz Lee, Theodore L. Willke, Rong Zhou, Xiangnan Kong, Hoda Eldardiry
Date: 2018-02-07 00:00:00
Proceeding: IJCAI 2018 7
Tasks: Inductive Learning, Network Embedding

Random walks are at the heart of many existing network embedding methods.
However, such algorithms have many limitations that arise from the use of
random walks, e.g., the features resulting from these methods are unable to
transfer to new nodes and graphs as they are tied to vertex identity. In this
work, we introduce the Role2Vec framework which uses the flexible notion of
attributed random walks, and serves as a basis for generalizing existing
methods such as DeepWalk, node2vec, and many others that leverage random walks.
Our proposed framework enables these methods to be more widely applicable for
both transductive and inductive learning as well as for use on graphs with
attributes (if available). This is achieved by learning functions that
generalize to new nodes and graphs. We show that our proposed framework is
effective with an average AUC improvement of 16.55% while requiring on average
853x less space than existing methods on a variety of graphs.
Crowd counting via scale-adaptive convolutional neural network
Authors: Lu Zhang, Miaojing Shi, Qiaobo Chen
Date: 2017-11-13 00:00:00
Tasks: Crowd Counting

The task of crowd counting is to automatically estimate the pedestrian number
in crowd images. To cope with the scale and perspective changes that commonly
exist in crowd images, state-of-the-art approaches employ multi-column CNN
architectures to regress density maps of crowd images. Multiple columns have
different receptive fields corresponding to pedestrians (heads) of different
scales. We instead propose a scale-adaptive CNN (SaCNN) architecture with a
backbone of fixed small receptive fields. We extract feature maps from multiple
layers and adapt them to have the same output size; we combine them to produce
the final density map. The number of people is computed by integrating the
density map. We also introduce a relative count loss along with the density map
loss to improve the network generalization on crowd scenes with few
pedestrians, where most representative approaches perform poorly on. We conduct
extensive experiments on the ShanghaiTech, UCF_CC_50 and WorldExpo datasets as
well as a new dataset SmartCity that we collect for crowd scenes with few
people. The results demonstrate significant improvements of SaCNN over the
state-of-the-art.
ZOOpt: Toolbox for Derivative-Free Optimization
Authors: Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Chao Qian, Yang Yu
Date: 2017-12-31 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Optimization

Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (Zeroth Order Optimization) toolbox that provides efficient derivative-free solvers and is designed easy to use. ZOOpt provides single-machine parallel optimization on the basis of python core and multi-machine distributed optimization for time-consuming tasks by incorporating with the Ray framework -- a famous platform for building distributed applications. ZOOpt particularly focuses on optimization problems in machine learning, addressing high-dimensional and noisy problems such as hyper-parameter tuning and direct policy search. The toolbox is maintained toward a ready-to-use tool in real-world machine learning tasks.
Practical Transfer Learning for Bayesian Optimization
Authors: Matthias Feurer, Benjamin Letham, Frank Hutter, Eytan Bakshy
Date: 2018-02-06 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, Hyperparameter Optimization, Meta-Learning, Transfer Learning

When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We develop a new hyperparameter-free ensemble model for Bayesian optimization that is a generalization of two existing transfer learning extensions to Bayesian optimization and establish a worst-case bound compared to vanilla Bayesian optimization. Using a large collection of hyperparameter optimization benchmark problems, we demonstrate that our contributions substantially reduce optimization time compared to standard Gaussian process-based Bayesian optimization and improve over the current state-of-the-art for transfer hyperparameter optimization.
Efficient Large-Scale Multi-Modal Classification
Authors: D. Kiela, E. Grave, A. Joulin, T. Mikolov
Date: 2018-02-06 00:00:00
Tasks: Classification, Computational Efficiency, General Classification, Multi-modal Classification

While the incipient internet was largely text-based, the modern digital world
is becoming increasingly multi-modal. Here, we examine multi-modal
classification where one modality is discrete, e.g. text, and the other is
continuous, e.g. visual representations transferred from a convolutional neural
network. In particular, we focus on scenarios where we have to be able to
classify large quantities of data quickly. We investigate various methods for
performing multi-modal fusion and analyze their trade-offs in terms of
classification accuracy and computational efficiency. Our findings indicate
that the inclusion of continuous information improves performance over
text-only on a range of multi-modal classification tasks, even with simple
fusion methods. In addition, we experiment with discretizing the continuous
features in order to speed up and simplify the fusion process even further. Our
results show that fusion with discretized features outperforms text-only
classification, at a fraction of the computational cost of full multi-modal
fusion, with the additional benefit of improved interpretability.
How to Make Causal Inferences Using Texts
Authors: Naoki Egami, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart
Date: 2018-02-06 00:00:00

New text as data techniques offer a great promise: the ability to inductively
discover measures that are useful for testing social science theories of
interest from large collections of text. We introduce a conceptual framework
for making causal inferences with discovered measures as a treatment or
outcome. Our framework enables researchers to discover high-dimensional textual
interventions and estimate the ways that observed treatments affect text-based
outcomes. We argue that nearly all text-based causal inferences depend upon a
latent representation of the text and we provide a framework to learn the
latent representation. But estimating this latent representation, we show,
creates new risks: we may introduce an identification problem or overfit. To
address these risks we describe a split-sample framework and apply it to
estimate causal effects from an experiment on immigration attitudes and a study
on bureaucratic response. Our work provides a rigorous foundation for
text-based causal inferences.
Critical Percolation as a Framework to Analyze the Training of Deep Networks
Authors: Zohar Ringel, Rodrigo de Bem
Date: 2018-02-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification

In this paper we approach two relevant deep learning topics: i) tackling of
graph structured input data and ii) a better understanding and analysis of deep
networks and related learning algorithms. With this in mind we focus on the
topological classification of reachability in a particular subset of planar
graphs (Mazes). Doing so, we are able to model the topology of data while
staying in Euclidean space, thus allowing its processing with standard CNN
architectures. We suggest a suitable architecture for this problem and show
that it can express a perfect solution to the classification task. The shape of
the cost function around this solution is also derived and, remarkably, does
not depend on the size of the maze in the large maze limit. Responsible for
this behavior are rare events in the dataset which strongly regulate the shape
of the cost function near this global minimum. We further identify an obstacle
to learning in the form of poorly performing local minima in which the network
chooses to ignore some of the inputs. We further support our claims with
training experiments and numerical analysis of the cost function on networks
with up to $128$ layers.
Non-Projective Dependency Parsing via Latent Heads Representation (LHR)
Authors: Matteo Grella, Simone Cangialosi
Date: 2018-02-06 00:00:00
Tasks: Dependency Parsing

In this paper, we introduce a novel approach based on a bidirectional
recurrent autoencoder to perform globally optimized non-projective dependency
parsing via semi-supervised learning. The syntactic analysis is completed at
the end of the neural process that generates a Latent Heads Representation
(LHR), without any algorithmic constraint and with a linear complexity. The
resulting "latent syntactic structure" can be used directly in other semantic
tasks. The LHR is transformed into the usual dependency tree computing a simple
vectors similarity. We believe that our model has the potential to compete with
much more complex state-of-the-art parsing architectures.
Investigations on Knowledge Base Embedding for Relation Prediction and Extraction
Authors: Peng Xu, Denilson Barbosa
Date: 2018-02-06 00:00:00
Tasks: Prediction, Relation, Relation Extraction, Relation Prediction

We report an evaluation of the effectiveness of the existing knowledge base
embedding models for relation prediction and for relation extraction on a wide
range of benchmarks. We also describe a new benchmark, which is much larger and
complex than previous ones, which we introduce to help validate the
effectiveness of both tasks. The results demonstrate that knowledge base
embedding models are generally effective for relation prediction but unable to
give improvements for the state-of-art neural relation extraction model with
the existing strategies, while pointing limitations of existing methods.
Structural Recurrent Neural Network (SRNN) for Group Activity Analysis
Authors: Sovan Biswas, Juergen Gall
Date: 2018-02-06 00:00:00

A group of persons can be analyzed at various semantic levels such as
individual actions, their interactions, and the activity of the entire group.
In this paper, we propose a structural recurrent neural network (SRNN) that
uses a series of interconnected RNNs to jointly capture the actions of
individuals, their interactions, as well as the group activity. While previous
structural recurrent neural networks assumed that the number of nodes and edges
is constant, we use a grid pooling layer to address the fact that the number of
individuals in a group can vary. We evaluate two variants of the structural
recurrent neural network on the Volleyball Dataset.
A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography
Authors: Jyotirmoy Banerjee, Premal A. Patel, Fred Ushakov, Donald Peebles, Jan Deprest, Sebastien Ourselin, David Hawkes, Tom Vercauteren
Date: 2018-02-06 00:00:00

We propose a spatial compounding technique and variational framework to
improve 3D ultrasound image quality by compositing multiple ultrasound volumes
acquired from different probe orientations. In the composite volume, instead of
intensity values, we estimate a tensor at every voxel. The resultant tensor
image encapsulates the directional information of the underlying imaging data
and can be used to generate ultrasound volumes from arbitrary, potentially
unseen, probe positions. Extending the work of Hennersperger et al., we
introduce a log-Euclidean framework to ensure that the tensors are
positive-definite, eventually ensuring non-negative images. Additionally, we
regularise the underpinning ill-posed variational problem while preserving edge
information by relying on a total variation penalisation of the tensor field in
the log domain. We present results on in vivo human data to show the efficacy
of the approach.