pwc-archive/papers-with-abstracts huggingface dataset:
Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization
Authors: Qing Li, Jiasong Zhu, Rui Cao, Ke Sun, Jonathan M. Garibaldi, Qingquan Li, Bozhi Liu, Guoping Qiu
Date: 2019-01-04 00:00:00
Tasks: Autonomous Driving, Camera Relocalization, Deep Learning, Metric Learning, Multi-Task Learning

6DOF camera relocalization is an important component of autonomous driving
and navigation. Deep learning has recently emerged as a promising technique to
tackle this problem. In this paper, we present a novel relative geometry-aware
Siamese neural network to enhance the performance of deep learning-based
methods through explicitly exploiting the relative geometry constraints between
images. We perform multi-task learning and predict the absolute and relative
poses simultaneously. We regularize the shared-weight twin networks in both the
pose and feature domains to ensure that the estimated poses are globally as
well as locally correct. We employ metric learning and design a novel adaptive
metric distance loss to learn a feature that is capable of distinguishing poses
of visually similar images from different locations. We evaluate the proposed
method on public indoor and outdoor benchmarks and the experimental results
demonstrate that our method can significantly improve localization performance.
Furthermore, extensive ablation evaluations are conducted to demonstrate the
effectiveness of different terms of the loss function.
Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning
Authors: Tomasz Konopczyński, Thorben Kröger, Lei Zheng, Jürgen Hesser
Date: 2019-01-04 00:00:00
Tasks: 3D Instance Segmentation, Clustering, Instance Segmentation, Segmentation, Semantic Segmentation

We propose a novel approach for automatic extraction (instance segmentation)
of fibers from low resolution 3D X-ray computed tomography scans of short glass
fiber reinforced polymers. We have designed a 3D instance segmentation
architecture built upon a deep fully convolutional network for semantic
segmentation with an extra output for embedding learning. We show that the
embedding learning is capable of learning a mapping of voxels to an embedded
space in which a standard clustering algorithm can be used to distinguish
between different instances of an object in a volume. In addition, we discuss a
merging post-processing method which makes it possible to process volumes of
any size. The proposed 3D instance segmentation network together with our
merging algorithm is the first known to authors knowledge procedure that
produces results good enough, that they can be used for further analysis of low
resolution fiber composites CT scans.
Multi-Product Dynamic Pricing in High-Dimensions with Heterogeneous Price Sensitivity
Authors: Adel Javanmard, Hamid Nazerzadeh, Simeng Shao
Date: 2019-01-04 00:00:00
Tasks: Sensitivity, Vocal Bursts Intensity Prediction

We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (MNL) choice model and her utility depends on the product features as well as the prices offered. The seller a-priori does not know the parameters of the choice model but can learn them through interactions with customers. The seller's goal is to design a pricing policy that maximizes her cumulative revenue. This model is motivated by online marketplaces such as Airbnb platform and online advertising. We measure the performance of a pricing policy in terms of regret, which is the expected revenue loss with respect to a clairvoyant policy that knows the parameters of the choice model in advance and always sets the revenue-maximizing prices. We propose a pricing policy, named M3P, that achieves a $T$-period regret of $O(\log(Td) ( \sqrt{T}+ d\log(T)))$ under heterogeneous price sensitivity for products with features of dimension $d$. We also use tools from information theory to prove that no policy can achieve worst-case $T$-regret better than $\Omega(\sqrt{T})$.
Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks
Authors: Daniel Kerrigan, Mateusz Trokielewicz, Adam Czajka, Kevin Bowyer
Date: 2019-01-04 00:00:00
Tasks: Deep Learning, Image Segmentation, Iris Recognition, Iris Segmentation, Segmentation, Semantic Segmentation

This paper offers three new, open-source, deep learning-based iris
segmentation methods, and the methodology how to use irregular segmentation
masks in a conventional Gabor-wavelet-based iris recognition. To train and
validate the methods, we used a wide spectrum of iris images acquired by
different teams and different sensors and offered publicly, including data
taken from CASIA-Iris-Interval-v4, BioSec, ND-Iris-0405, UBIRIS,
Warsaw-BioBase-Post-Mortem-Iris v2.0 (post-mortem iris images), and
ND-TWINS-2009-2010 (iris images acquired from identical twins). This varied
training data should increase the generalization capabilities of the proposed
segmentation techniques. In database-disjoint training and testing, we show
that deep learning-based segmentation outperforms the conventional (OSIRIS)
segmentation in terms of Intersection over Union calculated between the
obtained results and manually annotated ground-truth. Interestingly, the
Gabor-based iris matching is not always better when deep learning-based
segmentation is used, and is on par with the method employing Daugman's based
segmentation.
Transformed $\ell_1$ Regularization for Learning Sparse Deep Neural Networks
Authors: Rongrong Ma, Jianyu Miao, Lingfeng Niu, Peng Zhang
Date: 2019-01-04 00:00:00

Deep neural networks (DNNs) have achieved extraordinary success in numerous
areas. However, to attain this success, DNNs often carry a large number of
weight parameters, leading to heavy costs of memory and computation resources.
Overfitting is also likely to happen in such network when the training data are
insufficient. These shortcomings severely hinder the application of DNNs in
resource-constrained platforms. In fact, many network weights are known to be
redundant and can be removed from the network without much loss of performance.
To this end, we introduce a new non-convex integrated transformed $\ell_1$
regularizer to promote sparsity for DNNs, which removes both redundant
connections and unnecessary neurons simultaneously. To be specific, we apply
the transformed $\ell_1$ to the matrix space of network weights and utilize it
to remove redundant connections. Besides, group sparsity is also employed as an
auxiliary to remove unnecessary neurons. An efficient stochastic proximal
gradient algorithm is presented to solve the new model at the same time. To the
best of our knowledge, this is the first work to utilize a non-convex
regularizer in sparse optimization based method to promote sparsity for DNNs.
Experiments on several public datasets demonstrate the effectiveness of the
proposed method.
Vehicle Re-Identification: an Efficient Baseline Using Triplet Embedding
Authors: Ratnesh Kumar, Edwin Weill, Farzin Aghdasi, Parthsarathy Sriram
Date: 2019-01-04 00:00:00
Tasks: Triplet, Vehicle Re-Identification

In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of these losses applied to vehicle re-identification and demonstrate that using the best practices for learning embeddings outperform most of the previous approaches proposed in the vehicle re-identification literature. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature.
A Joint Model for Multimodal Document Quality Assessment
Authors: Aili Shen, Bahar Salehi, Timothy Baldwin, Jianzhong Qi
Date: 2019-01-04 00:00:00
Tasks: Articles, model

The quality of a document is affected by various factors, including
grammaticality, readability, stylistics, and expertise depth, making the task
of document quality assessment a complex one. In this paper, we explore this
task in the context of assessing the quality of Wikipedia articles and academic
papers. Observing that the visual rendering of a document can capture implicit
quality indicators that are not present in the document text --- such as
images, font choices, and visual layout --- we propose a joint model that
combines the text content with a visual rendering of the document for document
quality assessment. Experimental results over two datasets reveal that textual
and visual features are complementary, achieving state-of-the-art results.
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
Authors: Tong Geng, Tianqi Wang, Ang Li, Xi Jin, Martin Herbordt
Date: 2019-01-04 00:00:00
Tasks: GPU

Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is generally done by distributing tasks in batch mode using methods such as distributed synchronous SGD. Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size. In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer parallelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is a fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation. As a result, storage demand is reduced to the point where only on-chip memory is used for the convolution layers. We evaluate FPDeep with the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that FPDeep has good scalability to a large number of FPGAs, with the limiting factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than comparable GPU servers.
On Reproducing Kernel Banach Spaces: Generic Definitions and Unified Framework of Constructions
Authors: Rongrong Lin, Haizhang Zhang, Jun Zhang
Date: 2019-01-04 00:00:00
Tasks: BIG-bench Machine Learning

Recently, there has been emerging interest in constructing reproducing kernel Banach spaces (RKBS) for applied and theoretical purposes such as machine learning, sampling reconstruction, sparse approximation and functional analysis. Existing constructions include the reflexive RKBS via a bilinear form, the semi-inner-product RKBS, the RKBS with $\ell^1$ norm, the $p$-norm RKBS via generalized Mercer kernels, etc. The definitions of RKBS and the associated reproducing kernel in those references are dependent on the construction. Moreover, relations among those constructions are unclear. We explore a generic definition of RKBS and the reproducing kernel for RKBS that is independent of construction. Furthermore, we propose a framework of constructing RKBSs that unifies existing constructions mentioned above via a continuous bilinear form and a pair of feature maps. A new class of Orlicz RKBSs is proposed. Finally, we develop representer theorems for machine learning in RKBSs constructed in our framework, which also unifies representer theorems in existing RKBSs.
Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation
Authors: Wan-Ping Nicole Chen, Yuan-Chin Ivan Chang
Date: 2019-01-04 00:00:00
Tasks: Classification, Density Estimation, General Classification, Variable Selection

The model interpretation is essential in many application scenarios and to
build a classification model with a ease of model interpretation may provide
useful information for further studies and improvement. It is common to
encounter with a lengthy set of variables in modern data analysis, especially
when data are collected in some automatic ways. This kinds of datasets may not
collected with a specific analysis target and usually contains redundant
features, which have no contribution to a the current analysis task of
interest. Variable selection is a common way to increase the ability of model
interpretation and is popularly used with some parametric classification
models. There is a lack of studies about variable selection in nonparametric
classification models such as the density estimation-based methods and this is
especially the case for multiple-class classification situations. In this study
we study multiple-class classification problems using the thought of sparse
non-parametric density estimation and propose a method for identifying high
impacts variables for each class. We present the asymptotic properties and the
computation procedure for the proposed method together with some suggested
sample size. We also repost the numerical results using both synthesized and
some real data sets.
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Authors: Prashanth L. A., Krishna Jagannathan, Ravi Kumar Kolla
Date: 2019-01-04 00:00:00
Proceeding: ICML 2020 1
Tasks: Multi-Armed Bandits

Conditional Value-at-Risk (CVaR) is a widely used risk metric in applications such as finance. We derive concentration bounds for CVaR estimates, considering separately the cases of light-tailed and heavy-tailed distributions. In the light-tailed case, we use a classical CVaR estimator based on the empirical distribution constructed from the samples. For heavy-tailed random variables, we assume a mild `bounded moment' condition, and derive a concentration bound for a truncation-based estimator. Notably, our concentration bounds enjoy an exponential decay in the sample size, for heavy-tailed as well as light-tailed distributions. To demonstrate the applicability of our concentration results, we consider a CVaR optimization problem in a multi-armed bandit setting. Specifically, we address the best CVaR-arm identification problem under a fixed budget. We modify the well-known successive rejects algorithm to incorporate a CVaR-based criterion. Using the CVaR concentration result, we derive an upper-bound on the probability of incorrect identification by the proposed algorithm.
Brief Review of Computational Intelligence Algorithms
Authors: Satyarth Vaidya, Arshveer Kaur, Lavika Goel
Date: 2019-01-04 00:00:00

Computational Intelligence algorithms have gained a lot of attention of
researchers in the recent years due to their ability to deliver near optimal
solutions.
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video
Authors: Alisha Sharma, Jonathan Ventura
Date: 2019-01-04 00:00:00
Tasks: Depth Estimation, Motion Estimation

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3D modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We also introduce Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting.
Contrastive Adaptation Network for Unsupervised Domain Adaptation
Authors: Guoliang Kang, Lu Jiang, Yi Yang, Alexander G. Hauptmann
Date: 2019-01-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain
data while manual annotations are only available in the source domain. Previous
methods minimize the domain discrepancy neglecting the class information, which
may lead to misalignment and poor generalization performance. To address this
issue, this paper proposes Contrastive Adaptation Network (CAN) optimizing a
new metric which explicitly models the intra-class domain discrepancy and the
inter-class domain discrepancy. We design an alternating update strategy for
training CAN in an end-to-end manner. Experiments on two real-world benchmarks
Office-31 and VisDA-2017 demonstrate that CAN performs favorably against the
state-of-the-art methods and produces more discriminative features.
QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge
Authors: Rajarshi Bhattacharyya, Archana Bura, Desik Rengarajan, Mason Rumuly, Bainan Xia, Srinivas Shakkottai, Dileep Kalathil, Ricky K. P. Mok, Amogh Dhamdhere
Date: 2019-01-04 00:00:00
Tasks: Reinforcement Learning

The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose. However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a given time. Software reconfigurability of networking devices has seen wide adoption, and this in turn implies that agile control policies can be now instantiated on access networks. The goal of this work is to design, develop and demonstrate QFlow, a learning approach to create a value chain from the application on one side, to algorithms operating over reconfigurable infrastructure on the other, so that applications are able to obtain necessary resources for optimal performance. Using YouTube video streaming as an example, we illustrate how QFlow is able to adaptively provide such resources and attain a high QoE for all clients at a wireless access point.
Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding
Authors: Nicholas R. Clayton, Hussein Abbass
Date: 2019-01-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The design of reward functions in reinforcement learning is a human skill
that comes with experience. Unfortunately, there is not any methodology in the
literature that could guide a human to design the reward function or to allow a
human to transfer the skills developed in designing reward functions to another
human and in a systematic manner. In this paper, we use Systematic
Instructional Design, an approach in human education, to engineer a machine
education methodology to design reward functions for reinforcement learning. We
demonstrate the methodology in designing a hierarchical genetic reinforcement
learner that adopts a neural network representation to evolve a swarm
controller for an agent shepherding a boids-based swarm. The results reveal
that the methodology is able to guide the design of hierarchical reinforcement
learners, with each model in the hierarchy learning incrementally through a
multi-part reward function. The hierarchy acts as a decision fusion function
that combines the individual behaviours and skills learnt by each instruction
to create a smart shepherd to control the swarm.
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
Authors: Jack Lindsey, Samuel A. Ocko, Surya Ganguli, Stephane Deny
Date: 2019-01-03 00:00:00
Tasks: Anatomy

The visual system is hierarchically organized to process visual information
in successive stages. Neural representations vary drastically across the first
stages of visual processing: at the output of the retina, ganglion cell
receptive fields (RFs) exhibit a clear antagonistic center-surround structure,
whereas in the primary visual cortex, typical RFs are sharply tuned to a
precise orientation. There is currently no unified theory explaining these
differences in representations across layers. Here, using a deep convolutional
neural network trained on image recognition as a model of the visual system, we
show that such differences in representation can emerge as a direct consequence
of different neural resource constraints on the retinal and cortical networks,
and we find a single model from which both geometries spontaneously emerge at
the appropriate stages of visual processing. The key constraint is a reduced
number of neurons at the retinal output, consistent with the anatomy of the
optic nerve as a stringent bottleneck. Second, we find that, for simple
cortical networks, visual representations at the retinal output emerge as
nonlinear and lossy feature detectors, whereas they emerge as linear and
faithful encoders of the visual scene for more complex cortices. This result
predicts that the retinas of small vertebrates should perform sophisticated
nonlinear computations, extracting features directly relevant to behavior,
whereas retinas of large animals such as primates should mostly encode the
visual scene linearly and respond to a much broader range of stimuli. These
predictions could reconcile the two seemingly incompatible views of the retina
as either performing feature extraction or efficient coding of natural scenes,
by suggesting that all vertebrates lie on a spectrum between these two
objectives, depending on the degree of neural resources allocated to their
visual system.
Self-supervised Learning of Image Embedding for Continuous Control
Authors: Carlos Florensa, Jonas Degrave, Nicolas Heess, Jost Tobias Springenberg, Martin Riedmiller
Date: 2019-01-03 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Self-Supervised Learning

Operating directly from raw high dimensional sensory inputs like images is
still a challenge for robotic control. Recently, Reinforcement Learning methods
have been proposed to solve specific tasks end-to-end, from pixels to torques.
However, these approaches assume the access to a specified reward which may
require specialized instrumentation of the environment. Furthermore, the
obtained policy and representations tend to be task specific and may not
transfer well. In this work we investigate completely self-supervised learning
of a general image embedding and control primitives, based on finding the
shortest time to reach any state. We also introduce a new structure for the
state-action value function that builds a connection between model-free and
model-based methods, and improves the performance of the learning algorithm. We
experimentally demonstrate these findings in three simulated robotic tasks.