pwc-archive/papers-with-abstracts huggingface dataset:
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
Authors: Ali Harakeh, Michael Smart, Steven L. Waslander
Date: 2019-03-09 00:00:00
Tasks: Object, object-detection, Object Detection

When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions. Methods for uncertainty estimation in the output of deep object detectors (DNNs) have been proposed in recent works, but have had limited success due to 1) information loss at the detectors non-maximum suppression (NMS) stage, and 2) failure to take into account the multitask, many-to-one nature of anchor-based object detection. To that end, we introduce BayesOD, an uncertainty estimation approach that reformulates the standard object detector inference and Non-Maximum suppression components from a Bayesian perspective. Experiments performed on four common object detection datasets show that BayesOD provides uncertainty estimates that are better correlated with the accuracy of detections, manifesting as a significant reduction of 9.77\%-13.13\% on the minimum Gaussian uncertainty error metric and a reduction of 1.63\%-5.23\% on the minimum Categorical uncertainty error metric. Code will be released at {\url{https://github.com/asharakeh/bayes-od-rc}}.
LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices
Authors: Laura Fink, Sing Chun Lee, Jie Ying Wu, Xingtong Liu, Tianyu Song, Yordanka Stoyanova, Marc Stamminger, Nassir Navab, Mathias Unberath
Date: 2019-03-09 00:00:00
Tasks: Data Visualization, Image Generation, Navigate

With the increasing computational power of today's workstations, real-time physically-based rendering is within reach, rapidly gaining attention across a variety of domains. These have expeditiously applied to medicine, where it is a powerful tool for intuitive 3D data visualization. Embedded devices such as optical see-through head-mounted displays (OST HMDs) have been a trend for medical augmented reality. However, leveraging the obvious benefits of physically-based rendering remains challenging on these devices because of limited computational power, memory usage, and power consumption. We navigate the compromise between device limitations and image quality to achieve reasonable rendering results by introducing a novel light field that can be sampled in real-time on embedded devices. We demonstrate its applications in medicine and discuss limitations of the proposed method. An open-source version of this project is available at https://github.com/lorafib/LumiPath which provides full insight on implementation and exemplary demonstrational material.
Interpolation Consistency Training for Semi-Supervised Learning
Authors: Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz
Date: 2019-03-09 00:00:00
Tasks: General Classification, Semi-Supervised Image Classification

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. In classification problems, ICT moves the decision boundary to low-density regions of the data distribution. Our experiments show that ICT achieves state-of-the-art performance when applied to standard neural network architectures on the CIFAR-10 and SVHN benchmark datasets. Our theoretical analysis shows that ICT corresponds to a certain type of data-adaptive regularization with unlabeled points which reduces overfitting to labeled points under high confidence values.
Successive Over Relaxation Q-Learning
Authors: Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi, Shalabh Bhatnagar
Date: 2019-03-09 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

In a discounted reward Markov Decision Process (MDP), the objective is to find the optimal value function, i.e., the value function corresponding to an optimal policy. This problem reduces to solving a functional equation known as the Bellman equation and a fixed point iteration scheme known as the value iteration is utilized to obtain the solution. In literature, a successive over-relaxation based value iteration scheme is proposed to speed-up the computation of the optimal value function. The speed-up is achieved by constructing a modified Bellman equation that ensures faster convergence to the optimal value function. However, in many practical applications, the model information is not known and we resort to Reinforcement Learning (RL) algorithms to obtain optimal policy and value function. One such popular algorithm is Q-learning. In this paper, we propose Successive Over-Relaxation (SOR) Q-learning. We first derive a modified fixed point iteration for SOR Q-values and utilize stochastic approximation to derive a learning algorithm to compute the optimal value function and an optimal policy. We then prove the almost sure convergence of the SOR Q-learning to SOR Q-values. Finally, through numerical experiments, we show that SOR Q-learning is faster compared to the standard Q-learning algorithm.
Program Classification Using Gated Graph Attention Neural Network for Online Programming Service
Authors: Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen, Haifeng Li
Date: 2019-03-09 00:00:00
Tasks: General Classification, Graph Attention, Graph Neural Network

The online programing services, such as Github,TopCoder, and EduCoder, have
promoted a lot of social interactions among the service users. However, the
existing social interactions is rather limited and inefficient due to the rapid
increasing of source-code repositories, which is difficult to explore manually.
The emergence of source-code mining provides a promising way to analyze those
source codes, so that those source codes can be relatively easy to understand
and share among those service users. Among all the source-code mining
attempts,program classification lays a foundation for various tasks related to
source-code understanding, because it is impossible for a machine to understand
a computer program if it cannot classify the program correctly. Although
numerous machine learning models, such as the Natural Language Processing (NLP)
based models and the Abstract Syntax Tree (AST) based models, have been
proposed to classify computer programs based on their corresponding source
codes, the existing works cannot fully characterize the source codes from the
perspective of both the syntax and semantic information. To address this
problem, we proposed a Graph Neural Network (GNN) based model, which integrates
data flow and function call information to the AST,and applies an improved GNN
model to the integrated graph, so as to achieve the state-of-art program
classification accuracy. The experiment results have shown that the proposed
work can classify programs with accuracy over 97%.
Combining 3D Morphable Models: A Large scale Face-and-Head Model
Authors: Stylianos Ploumpis, Haoyang Wang, Nick Pears, William A. P. Smith, Stefanos Zafeiriou
Date: 2019-03-09 00:00:00
Proceeding: CVPR 2019 6

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for
representing the 3D surfaces of an object class. In this context, we identify
an interesting question that has previously not received research attention: is
it possible to combine two or more 3DMMs that (a) are built using different
templates that perhaps only partly overlap, (b) have different representation
capabilities and (c) are built from different datasets that may not be
publicly-available? In answering this question, we make two contributions.
First, we propose two methods for solving this problem: i. use a regressor to
complete missing parts of one model using the other, ii. use the Gaussian
Process framework to blend covariance matrices from multiple models. Second, as
an example application of our approach, we build a new face-and-head shape
model that combines the variability and facial detail of the LSFM with the full
head modelling of the LYHM. The resulting combined shape model achieves
state-of-the-art performance and outperforms existing head models by a large
margin. Finally, as an application experiment, we reconstruct full head
representations from single, unconstrained images by utilizing our proposed
large-scale model in conjunction with the FaceWarehouse blendshapes for
handling expressions.
Orthogonal Estimation of Wasserstein Distances
Authors: Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, Adrian Weller
Date: 2019-03-09 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Wasserstein distances are increasingly used in a wide variety of applications
in machine learning. Sliced Wasserstein distances form an important subclass
which may be estimated efficiently through one-dimensional sorting operations.
In this paper, we propose a new variant of sliced Wasserstein distance, study
the use of orthogonal coupling in Monte Carlo estimation of Wasserstein
distances and draw connections with stratified sampling, and evaluate our
approaches experimentally in a range of large-scale experiments in generative
modelling and reinforcement learning.
A New Approach for Topic Detection using Adaptive Neural Networks
Authors: Meriem Manai
Date: 2019-03-09 00:00:00

Topic detection becomes more important due to the increase of information
electronically available and the necessity to process and filter it. In this
context our master's thesis work was carried out, where we proposed to present
a new approach to the detection of topics called ClusART. Thus, we proposed a
three-phase approach, namely : a first phase during which lexical preprocessing
was conducted. A second phase during which the construction and generation of
vectors representing the documents was carried out. A third phase which is
itself composed of two steps. In the first step we used the FuzzyART algorithm
for the training phase. In the second step we used a classifier using Paragraph
Vector for the test phase. The comparative study of our approach on the 20
Newsgroups dataset showed that our approach is able to detect almost relevant
topics.
Logic Rules Powered Knowledge Graph Embedding
Authors: Pengwei Wang, Dejing Dou, Fangzhao Wu, Nisansa de Silva, Lianwen Jin
Date: 2019-03-09 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Link Prediction, Triple Classification

Large scale knowledge graph embedding has attracted much attention from both
academia and industry in the field of Artificial Intelligence. However, most
existing methods concentrate solely on fact triples contained in the given
knowledge graph. Inspired by the fact that logic rules can provide a flexible
and declarative language for expressing rich background knowledge, it is
natural to integrate logic rules into knowledge graph embedding, to transfer
human knowledge to entity and relation embedding, and strengthen the learning
process. In this paper, we propose a novel logic rule-enhanced method which can
be easily integrated with any translation based knowledge graph embedding
model, such as TransE . We first introduce a method to automatically mine the
logic rules and corresponding confidences from the triples. And then, to put
both triples and mined logic rules within the same semantic space, all triples
in the knowledge graph are represented as first-order logic. Finally, we define
several operations on the first-order logic and minimize a global loss over
both of the mined logic rules and the transformed first-order logics. We
conduct extensive experiments for link prediction and triple classification on
three datasets: WN18, FB166, and FB15K. Experiments show that the rule-enhanced
method can significantly improve the performance of several baselines. The
highlight of our model is that the filtered Hits@1, which is a pivotal
evaluation in the knowledge inference task, has a significant improvement (up
to 700% improvement).
Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method
Authors: Shangsheng Xie, Mingming Lu
Date: 2019-03-09 00:00:00
Tasks: Few-Shot Learning, Node Classification

To solve the problem that convolutional neural networks (CNNs) are difficult
to process non-grid type relational data like graphs, Kipf et al. proposed a
graph convolutional neural network (GCN). The core idea of the GCN is to
perform two-fold informational fusion for each node in a given graph during
each iteration: the fusion of graph structure information and the fusion of
node feature dimensions. Because of the characteristic of the combinatorial
generalizations, GCN has been widely used in the fields of scene semantic
relationship analysis, natural language processing and few-shot learning etc.
However, due to its two-fold informational fusion involves mathematical
irreversible calculations, it is hard to explain the decision reason for the
prediction of the each node classification. Unfortunately, most of the existing
attribution analysis methods concentrate on the models like CNNs, which are
utilized to process grid-like data. It is difficult to apply those analysis
methods to the GCN directly. It is because compared with the independence among
CNNs input data, there is correlation between the GCN input data. This
resulting in the existing attribution analysis methods can only obtain the
partial model contribution from the central node features to the final decision
of the GCN, but ignores the other model contribution from central node features
and its neighbor nodes features to that decision. To this end, we propose a
gradient attribution analysis method for the GCN called Node Attribution Method
(NAM), which can get the model contribution from not only the central node but
also its neighbor nodes to the GCN output. We also propose the Node Importance
Visualization (NIV) method to visualize the central node and its neighbor nodes
based on the value of the contribution...
A tractable ellipsoidal approximation for voltage regulation problems
Authors: Pan Li, Baihong Jin, Ruoxuan Xiong, Dai Wang, Alberto Sangiovanni-Vincentelli, Baosen Zhang
Date: 2019-03-09 00:00:00
Tasks: BIG-bench Machine Learning

We present a machine learning approach to the solution of chance constrained
optimizations in the context of voltage regulation problems in power system
operation. The novelty of our approach resides in approximating the feasible
region of uncertainty with an ellipsoid. We formulate this problem using a
learning model similar to Support Vector Machines (SVM) and propose a sampling
algorithm that efficiently trains the model. We demonstrate our approach on a
voltage regulation problem using standard IEEE distribution test feeders.
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks
Authors: Jianping Cao, Senzhang Wang, Danyan Wen, Zhaohui Peng, Philip S. Yu, Fei-Yue Wang
Date: 2019-03-09 00:00:00
Tasks: Clustering, Text Clustering, Transfer Learning

Currently, many intelligence systems contain the texts from multi-sources,
e.g., bulletin board system (BBS) posts, tweets and news. These texts can be
``comparative'' since they may be semantically correlated and thus provide us
with different perspectives toward the same topics or events. To better
organize the multi-sourced texts and obtain more comprehensive knowledge, we
propose to study the novel problem of Mutual Clustering on Comparative Texts
(MCCT), which aims to cluster the comparative texts simultaneously and
collaboratively. The MCCT problem is difficult to address because 1)
comparative texts usually present different data formats and structures and
thus they are hard to organize, and 2) there lacks an effective method to
connect the semantically correlated comparative texts to facilitate clustering
them in an unified way. To this aim, in this paper we propose a Heterogeneous
Information Network-based Text clustering framework HINT. HINT first models
multi-sourced texts (e.g. news and tweets) as heterogeneous information
networks by introducing the shared ``anchor texts'' to connect the comparative
texts. Next, two similarity matrices based on HINT as well as a transition
matrix for cross-text-source knowledge transfer are constructed. Comparative
texts clustering are then conducted by utilizing the constructed matrices.
Finally, a mutual clustering algorithm is also proposed to further unify the
separate clustering results of the comparative texts by introducing a
clustering consistency constraint. We conduct extensive experimental on three
tweets-news datasets, and the results demonstrate the effectiveness and
robustness of the proposed method in addressing the MCCT problem.
Machine Learning Based Prediction and Classification of Computational Jobs in Cloud Computing Centers
Authors: Zheqi Zhu, Pingyi Fan
Date: 2019-03-09 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing, Clustering, General Classification, Scheduling

With the rapid growth of the data volume and the fast increasing of the
computational model complexity in the scenario of cloud computing, it becomes
an important topic that how to handle users' requests by scheduling
computational jobs and assigning the resources in data center.
  In order to have a better perception of the computing jobs and their requests
of resources, we analyze its characteristics and focus on the prediction and
classification of the computing jobs with some machine learning approaches.
Specifically, we apply LSTM neural network to predict the arrival of the jobs
and the aggregated requests for computing resources. Then we evaluate it on
Google Cluster dataset and it shows that the accuracy has been improved
compared to the current existing methods. Additionally, to have a better
understanding of the computing jobs, we use an unsupervised hierarchical
clustering algorithm, BIRCH, to make classification and get some
interpretability of our results in the computing centers.
Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction
Authors: Yifei Shi, Angel Xuan Chang, Zhelun Wu, Manolis Savva, Kai Xu
Date: 2019-03-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Denoising, Object, object-detection, Object Detection, Scene Understanding

Indoor scenes exhibit rich hierarchical structure in 3D object layouts. Many
tasks in 3D scene understanding can benefit from reasoning jointly about the
hierarchical context of a scene, and the identities of objects. We present a
variational denoising recursive autoencoder (VDRAE) that generates and
iteratively refines a hierarchical representation of 3D object layouts,
interleaving bottom-up encoding for context aggregation and top-down decoding
for propagation. We train our VDRAE on large-scale 3D scene datasets to predict
both instance-level segmentations and a 3D object detections from an
over-segmentation of an input point cloud. We show that our VDRAE improves
object detection performance on real-world 3D point cloud datasets compared to
baselines from prior work.
Two-Hop Walks Indicate PageRank Order
Authors: Ying Tang
Date: 2019-03-09 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper shows that pairwise PageRank orders emerge from two-hop walks. The
main tool used here refers to a specially designed sign-mirror function and a
parameter curve, whose low-order derivative information implies pairwise
PageRank orders with high probability. We study the pairwise correct rate by
placing the Google matrix $\textbf{G}$ in a probabilistic framework, where
$\textbf{G}$ may be equipped with different random ensembles for
model-generated or real-world networks with sparse, small-world, scale-free
features, the proof of which is mixed by mathematical and numerical evidence.
We believe that the underlying spectral distribution of aforementioned networks
is responsible for the high pairwise correct rate. Moreover, the perspective of
this paper naturally leads to an $O(1)$ algorithm for any single pairwise
PageRank comparison if assuming both $\textbf{A}=\textbf{G}-\textbf{I}_n$,
where $\textbf{I}_n$ denotes the identity matrix of order $n$, and
$\textbf{A}^2$ are ready on hand (e.g., constructed offline in an incremental
manner), based on which it is easy to extract the top $k$ list in $O(kn)$, thus
making it possible for PageRank algorithm to deal with super large-scale
datasets in real time.
Robust Influence Maximization for Hyperparametric Models
Authors: Dimitris Kalimeris, Gal Kaplun, Yaron Singer
Date: 2019-03-09 00:00:00

In this paper, we study the problem of robust influence maximization in the independent cascade model under a hyperparametric assumption. In social networks users influence and are influenced by individuals with similar characteristics and as such, they are associated with some features. A recent surging research direction in influence maximization focuses on the case where the edge probabilities on the graph are not arbitrary but are generated as a function of the features of the users and a global hyperparameter. We propose a model where the objective is to maximize the worst-case number of influenced users for any possible value of that hyperparameter. We provide theoretical results showing that proper robust solution in our model is NP-hard and an algorithm that achieves improper robust optimization. We make-use of sampling based techniques and of the renowned multiplicative weight updates algorithm. Additionally, we validate our method empirically and prove that it outperforms the state-of-the-art robust influence maximization techniques.
How Effectively Can Indoor Wireless Positioning Relieve Visual Tracking Pains: A Camera-Rao Bound Viewpoint
Authors: Panwen Hu, Zizheng Yan, Rui Huang, Feng Yin
Date: 2019-03-09 00:00:00
Tasks: Visual Tracking

Visual tracking is fragile in some difficult scenarios, for instance,
appearance ambiguity and variation, occlusion can easily degrade most of visual
trackers to some extent. In this paper, visual tracking is empowered with
wireless positioning to achieve high accuracy while maintaining robustness.
Fundamentally different from the previous works, this study does not involve
any specific wireless positioning algorithms. Instead, we use the confidence
region derived from the wireless positioning Cramer-Rao bound (CRB) as the
search region of visual trackers. The proposed framework is low-cost and very
simple to implement, yet readily leads to enhanced and robustified visual
tracking performance in difficult scenarios as corroborated by our experimental
results. Most importantly, it is utmost valuable for the practioners to
pre-evaluate how effectively can the wireless resources available at hand
alleviate the visual tracking pains.