pwc-archive/papers-with-abstracts huggingface dataset:
Classification using Ensemble Learning under Weighted Misclassification Loss
Authors: Yizhen Xu, Tao Liu, Michael J. Daniels, Rami Kantor, Ann Mwangi, Joseph W. Hogan
Date: 2018-12-16 00:00:00
Tasks: Binary Classification, Classification, Ensemble Learning, General Classification

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Depending on scenario, higher premium may be placed on avoiding false-positives which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification loss/risk. We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification loss. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using an ensemble learner. Simulations and examples show that our method, which derives the score and threshold jointly, more accurately estimates overall risk and has better operating characteristics compared with methods that derive the score first and the cutoff conditionally on the score especially for finite samples.
A Logarithmic Barrier Method For Proximal Policy Optimization
Authors: Cheng Zeng, Hongming Zhang
Date: 2018-12-16 00:00:00
Tasks: MuJoCo, Reinforcement Learning

Proximal policy optimization(PPO) has been proposed as a first-order
optimization method for reinforcement learning. We should notice that an
exterior penalty method is used in it. Often, the minimizers of the exterior
penalty functions approach feasibility only in the limits as the penalty
parameter grows increasingly large. Therefore, it may result in the low level
of sampling efficiency. This method, which we call proximal policy optimization
with barrier method (PPO-B), keeps almost all advantageous spheres of PPO such
as easy implementation and good generalization. Specifically, a new surrogate
objective with interior penalty method is proposed to avoid the defect arose
from exterior penalty method. Conclusions can be draw that PPO-B is able to
outperform PPO in terms of sampling efficiency since PPO-B achieved clearly
better performance on Atari and Mujoco environment than PPO.
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Authors: Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, Nasir Rajpoot
Date: 2018-12-16 00:00:00
Tasks: Classification, General Classification, Multi-tissue Nucleus Segmentation, Nuclear Segmentation, Segmentation

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation and classification is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. Then for each segmented instance, the network predicts the type of nucleus via a devoted up-sampling branch. We demonstrate state-of-the-art performance compared to other methods on multiple independent multi-tissue histology image datasets. As part of this work, we introduce a new dataset of Haematoxylin & Eosin stained colorectal adenocarcinoma image tiles, containing 24,319 exhaustively annotated nuclei with associated class labels.
Non-attracting Regions of Local Minima in Deep and Wide Neural Networks
Authors: Henning Petzka, Cristian Sminchisescu
Date: 2018-12-16 00:00:00

Understanding the loss surface of neural networks is essential for the design of models with predictable performance and their success in applications. Experimental results suggest that sufficiently deep and wide neural networks are not negatively impacted by suboptimal local minima. Despite recent progress, the reason for this outcome is not fully understood. Could deep networks have very few, if at all, suboptimal local optima? or could all of them be equally good? We provide a construction to show that suboptimal local minima (i.e., non-global ones), even though degenerate, exist for fully connected neural networks with sigmoid activation functions. The local minima obtained by our construction belong to a connected set of local solutions that can be escaped from via a non-increasing path on the loss curve. For extremely wide neural networks of decreasing width after the wide layer, we prove that every suboptimal local minimum belongs to such a connected set. This provides a partial explanation for the successful application of deep neural networks. In addition, we also characterize under what conditions the same construction leads to saddle points instead of local minima for deep neural networks.
A near Pareto optimal approach to student-supervisor allocation with two sided preferences and workload balance
Authors: Victor Sanchez-Anguix, Rithin Chalumuri, Reyhan Aydogan, Vicente Julian
Date: 2018-12-16 00:00:00

The problem of allocating students to supervisors for the development of a
personal project or a dissertation is a crucial activity in the higher
education environment, as it enables students to get feedback on their work
from an expert and improve their personal, academic, and professional
abilities. In this article, we propose a multi-objective and near Pareto
optimal genetic algorithm for the allocation of students to supervisors. The
allocation takes into consideration the students and supervisors' preferences
on research/project topics, the lower and upper supervision quotas of
supervisors, as well as the workload balance amongst supervisors. We introduce
novel mutation and crossover operators for the student-supervisor allocation
problem. The experiments carried out show that the components of the genetic
algorithm are more apt for the problem than classic components, and that the
genetic algorithm is capable of producing allocations that are near Pareto
optimal in a reasonable time.
The Adverse Effects of Code Duplication in Machine Learning Models of Code
Authors: Miltiadis Allamanis
Date: 2018-12-16 00:00:00
Tasks: BIG-bench Machine Learning

The field of big code relies on mining large corpora of code to perform some learning task. A significant threat to this approach has been recently identified by Lopes et al. (2017) who found a large amount of near-duplicate code on GitHub. However, the impact of code duplication has not been noticed by researchers devising machine learning models for source code. In this work, we explore the effects of code duplication on machine learning models showing that reported performance metrics are sometimes inflated by up to 100% when testing on duplicated code corpora compared to the performance on de-duplicated corpora which more accurately represent how machine learning models of code are used by software engineers. We present a duplication index for widely used datasets, list best practices for collecting code corpora and evaluating machine learning models on them. Finally, we release tools to help the community avoid this problem in future research.
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion
Authors: Seungjoon Lee, Felix Dietrich, George E. Karniadakis, Ioannis G. Kevrekidis
Date: 2018-12-16 00:00:00
Tasks: Gaussian Processes, regression

In statistical modeling with Gaussian Process regression, it has been shown
that combining (few) high-fidelity data with (many) low-fidelity data can
enhance prediction accuracy, compared to prediction based on the few
high-fidelity data only. Such information fusion techniques for multifidelity
data commonly approach the high-fidelity model $f_h(t)$ as a function of two
variables $(t,y)$, and then using $f_l(t)$ as the $y$ data. More generally, the
high-fidelity model can be written as a function of several variables
$(t,y_1,y_2....)$; the low-fidelity model $f_l$ and, say, some of its
derivatives, can then be substituted for these variables. In this paper, we
will explore mathematical algorithms for multifidelity information fusion that
use such an approach towards improving the representation of the high-fidelity
function with only a few training data points. Given that $f_h$ may not be a
simple function -- and sometimes not even a function -- of $f_l$, we
demonstrate that using additional functions of $t$, such as derivatives or
shifts of $f_l$, can drastically improve the approximation of $f_h$ through
Gaussian Processes. We also point out a connection with "embedology" techniques
from topology and dynamical systems.
Ensemble of Learning Project Productivity in Software Effort Based on Use Case Points
Authors: Mohammad Azzeh, Ali Bou Nassif, Shadi Banitaan, Cuauhtemoc Lopez-Martin
Date: 2018-12-16 00:00:00

It is well recognized that the project productivity is a key driver in
estimating software project effort from Use Case Point size metric at early
software development stages. Although, there are few proposed models for
predicting productivity, there is no consistent conclusion regarding which
model is the superior. Therefore, instead of building a new productivity
prediction model, this paper presents a new ensemble construction mechanism
applied for software project productivity prediction. Ensemble is an effective
technique when performance of base models is poor. We proposed a weighted mean
method to aggregate predicted productivities based on average of errors
produced by training model. The obtained results show that the using ensemble
is a good alternative approach when accuracies of base models are not
consistently accurate over different datasets, and when models behave
diversely.
Embedding Cardinality Constraints in Neural Link Predictors
Authors: Emir Muñoz, Pasquale Minervini, Matthias Nickles
Date: 2018-12-16 00:00:00
Tasks: Knowledge Base Completion, Link Prediction

Neural link predictors learn distributed representations of entities and
relations in a knowledge graph. They are remarkably powerful in the link
prediction and knowledge base completion tasks, mainly due to the learned
representations that capture important statistical dependencies in the data.
Recent works in the area have focused on either designing new scoring functions
or incorporating extra information into the learning process to improve the
representations. Yet the representations are mostly learned from the observed
links between entities, ignoring commonsense or schema knowledge associated
with the relations in the graph. A fundamental aspect of the topology of
relational data is the cardinality information, which bounds the number of
predictions given for a relation between a minimum and maximum frequency. In
this paper, we propose a new regularisation approach to incorporate relation
cardinality constraints to any existing neural link predictor without affecting
their efficiency or scalability. Our regularisation term aims to impose
boundaries on the number of predictions with high probability, thus,
structuring the embeddings space to respect commonsense cardinality assumptions
resulting in better representations. Experimental results on Freebase, WordNet
and YAGO show that, given suitable prior knowledge, the proposed method
positively impacts the predictive accuracy of downstream link prediction tasks.
"When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams
Authors: Bhaskar Gautam, Annappa Basava, Abhishek Singh, Amit Agrawal
Date: 2018-12-16 00:00:00

The proliferation of smartphones and wearable devices has increased the
availability of large amounts of geospatial streams to provide significant
automated discovery of knowledge in pervasive environments, but most prominent
information related to altering interests have not yet adequately capitalized.
In this paper, we provide a novel algorithm to exploit the dynamic fluctuations
in user's point-of-interest while forecasting the future place of visit with
fine granularity. Our proposed algorithm is based on the dynamic formation of
collective personality communities using different languages, opinions,
geographical and temporal distributions for finding out optimized equivalent
content. We performed extensive empirical experiments involving, real-time
streams derived from 0.6 million stream tuples of micro-blog comprising 1945
social person fusion with graph algorithm and feed-forward neural network model
as a predictive classification model. Lastly, The framework achieves 62.10%
mean average precision on 1,20,000 embeddings on unlabeled users and
surprisingly 85.92% increment on the state-of-the-art approach.
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
Authors: Guangli Li, Lei Liu, Xueying Wang, Xiao Dong, Peng Zhao, Xiaobing Feng
Date: 2018-12-16 00:00:00
Tasks: Collaborative Inference, Quantization

Recently, deep neural networks (DNNs) have been widely applied in mobile
intelligent applications. The inference for the DNNs is usually performed in
the cloud. However, it leads to a large overhead of transmitting data via
wireless network. In this paper, we demonstrate the advantages of the
cloud-edge collaborative inference with quantization. By analyzing the
characteristics of layers in DNNs, an auto-tuning neural network quantization
framework for collaborative inference is proposed. We study the effectiveness
of mixed-precision collaborative inference of state-of-the-art DNNs by using
ImageNet dataset. The experimental results show that our framework can generate
reasonable network partitions and reduce the storage on mobile devices with
trivial loss of accuracy.
Classifier and Exemplar Synthesis for Zero-Shot Learning
Authors: Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha
Date: 2018-12-16 00:00:00
Tasks: Denoising, Zero-Shot Learning

Zero-shot learning (ZSL) enables solving a task without the need to see its examples. In this paper, we propose two ZSL frameworks that learn to synthesize parameters for novel unseen classes. First, we propose to cast the problem of ZSL as learning manifold embeddings from graphs composed of object classes, leading to a flexible approach that synthesizes "classifiers" for the unseen classes. Then, we define an auxiliary task of synthesizing "exemplars" for the unseen classes to be used as an automatic denoising mechanism for any existing ZSL approaches or as an effective ZSL model by itself. On five visual recognition benchmark datasets, we demonstrate the superior performances of our proposed frameworks in various scenarios of both conventional and generalized ZSL. Finally, we provide valuable insights through a series of empirical analyses, among which are a comparison of semantic representations on the full ImageNet benchmark as well as a comparison of metrics used in generalized ZSL. Our code and data are publicly available at https://github.com/pujols/Zero-shot-learning-journal
Model-free Tracking with Deep Appearance and Motion Features Integration
Authors: Xiaolong Jiang, Peizhao Li, Xian-Tong Zhen, Xian-Bin Cao
Date: 2018-12-16 00:00:00
Tasks: Motion Detection, Object, Object Tracking

Being able to track an anonymous object, a model-free tracker is
comprehensively applicable regardless of the target type. However, designing
such a generalized framework is challenged by the lack of object-oriented prior
information. As one solution, a real-time model-free object tracking approach
is designed in this work relying on Convolutional Neural Networks (CNNs). To
overcome the object-centric information scarcity, both appearance and motion
features are deeply integrated by the proposed AMNet, which is an end-to-end
offline trained two-stream network. Between the two parallel streams, the ANet
investigates appearance features with a multi-scale Siamese atrous CNN,
enabling the tracking-by-matching strategy. The MNet achieves deep motion
detection to localize anonymous moving objects by processing generic motion
features. The final tracking result at each frame is generated by fusing the
output response maps from both sub-networks. The proposed AMNet reports leading
performance on both OTB and VOT benchmark datasets with favorable real-time
processing speed.
Visual Dialogue without Vision or Dialogue
Authors: Daniela Massiceti, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr
Date: 2018-12-16 00:00:00
Tasks: Question Answering, Visual Dialog

We characterise some of the quirks and shortcomings in the exploration of Visual Dialogue - a sequential question-answering task where the questions and corresponding answers are related through given visual stimuli. To do so, we develop an embarrassingly simple method based on Canonical Correlation Analysis (CCA) that, on the standard dataset, achieves near state-of-the-art performance on mean rank (MR). In direct contrast to current complex and over-parametrised architectures that are both compute and time intensive, our method ignores the visual stimuli, ignores the sequencing of dialogue, does not need gradients, uses off-the-shelf feature extractors, has at least an order of magnitude fewer parameters, and learns in practically no time. We argue that these results are indicative of issues in current approaches to Visual Dialogue and conduct analyses to highlight implicit dataset biases and effects of over-constrained evaluation metrics. Our code is publicly available.
Stochastic Distributed Optimization for Machine Learning from Decentralized Features
Authors: Yaochen Hu, Di Niu, Jianming Yang, Shengping Zhou
Date: 2018-12-16 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Optimization

Distributed machine learning has been widely studied in the literature to scale up machine learning model training in the presence of an ever-increasing amount of data. We study distributed machine learning from another perspective, where the information about the training same samples are inherently decentralized and located on different parities. We propose an asynchronous stochastic gradient descent (SGD) algorithm for such a feature distributed machine learning (FDML) problem, to jointly learn from decentralized features, with theoretical convergence guarantees under bounded asynchrony. Our algorithm does not require sharing the original feature data or even local model parameters between parties, thus preserving a high level of data confidentiality. We implement our algorithm for FDML in a parameter server architecture. We compare our system with fully centralized training (which violates data locality requirements) and training only based on local features, through extensive experiments performed on a large amount of data from a real-world application, involving 5 million samples and $8700$ features in total. Experimental results have demonstrated the effectiveness and efficiency of the proposed FDML system.
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Authors: Yongqi Zhang, Quanming Yao, Yingxia Shao, Lei Chen
Date: 2018-12-16 00:00:00
Tasks: Generative Adversarial Network, Graph Embedding, Knowledge Graph Embedding, Link Prediction, Reinforcement Learning

Knowledge Graph (KG) embedding is a fundamental problem in data mining
research with many real-world applications. It aims to encode the entities and
relations in the graph into low dimensional vector space, which can be used for
subsequent algorithms. Negative sampling, which samples negative triplets from
non-observed ones in the training data, is an important step in KG embedding.
Recently, generative adversarial network (GAN), has been introduced in negative
sampling. By sampling negative triplets with large scores, these methods avoid
the problem of vanishing gradient and thus obtain better performance. However,
using GAN makes the original model more complex and hard to train, where
reinforcement learning must be used. In this paper, motivated by the
observation that negative triplets with large scores are important but rare, we
propose to directly keep track of them with the cache. However, how to sample
from and update the cache are two important questions. We carefully design the
solutions, which are not only efficient but also achieve a good balance between
exploration and exploitation. In this way, our method acts as a "distilled"
version of previous GA-based methods, which does not waste training time on
additional parameters to fit the full distribution of negative triplets. The
extensive experiments show that our method can gain significant improvement in
various KG embedding models, and outperform the state-of-the-art negative
sampling methods based on GAN.
Human Pose and Path Estimation from Aerial Video using Dynamic Classifier Selection
Authors: Asanka G Perera, Yee Wei Law, Javaan Chahl
Date: 2018-12-16 00:00:00
Tasks: General Classification

We consider the problem of estimating human pose and trajectory by an aerial
robot with a monocular camera in near real time. We present a preliminary
solution whose distinguishing feature is a dynamic classifier selection
architecture. In our solution, each video frame is corrected for perspective
using projective transformation. Then, two alternative feature sets are used:
(i) Histogram of Oriented Gradients (HOG) of the silhouette, (ii) Convolutional
Neural Network (CNN) features of the RGB image. The features (HOG or CNN) are
classified using a dynamic classifier. A class is defined as a pose-viewpoint
pair, and a total of 64 classes are defined to represent a forward walking and
turning gait sequence. Our solution provides three main advantages: (i)
Classification is efficient due to dynamic selection (4-class vs. 64-class
classification). (ii) Classification errors are confined to neighbors of the
true view-points. (iii) The robust temporal relationship between poses is used
to resolve the left-right ambiguities of human silhouettes. Experiments
conducted on both fronto-parallel videos and aerial videos confirm our solution
can achieve accurate pose and trajectory estimation for both scenarios. We
found using HOG features provides higher accuracy than using CNN features. For
example, applying the HOG-based variant of our scheme to the 'walking on a
figure 8-shaped path' dataset (1652 frames) achieved estimation accuracies of
99.6% for viewpoints and 96.2% for number of poses.