pwc-archive/papers-with-abstracts huggingface dataset:
PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques
Authors: Yubin Park, Joyce C. Ho
Date: 2018-07-22 00:00:00
Tasks: Feature Importance

Stochastic Gradient TreeBoost is often found in many winning solutions in
public data science challenges. Unfortunately, the best performance requires
extensive parameter tuning and can be prone to overfitting. We propose
PaloBoost, a Stochastic Gradient TreeBoost model that uses novel regularization
techniques to guard against overfitting and is robust to parameter settings.
PaloBoost uses the under-utilized out-of-bag samples to perform gradient-aware
pruning and estimate adaptive learning rates. Unlike other Stochastic Gradient
TreeBoost models that use the out-of-bag samples to estimate test errors,
PaloBoost treats the samples as a second batch of training samples to prune the
trees and adjust the learning rates. As a result, PaloBoost can dynamically
adjust tree depths and learning rates to achieve faster learning at the start
and slower learning as the algorithm converges. We illustrate how these
regularization techniques can be efficiently implemented and propose a new
formula for calculating feature importance to reflect the node coverages and
learning rates. Extensive experimental results on seven datasets demonstrate
that PaloBoost is robust to overfitting, is less sensitivity to the parameters,
and can also effectively identify meaningful features.
Pedestrian Trajectory Prediction with Structured Memory Hierarchies
Authors: Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2018-07-22 00:00:00
Tasks: Decision Making, Pedestrian Trajectory Prediction, Prediction, Trajectory Prediction

This paper presents a novel framework for human trajectory prediction based
on multimodal data (video and radar). Motivated by recent neuroscience
discoveries, we propose incorporating a structured memory component in the
human trajectory prediction pipeline to capture historical information to
improve performance. We introduce structured LSTM cells for modelling the
memory content hierarchically, preserving the spatiotemporal structure of the
information and enabling us to capture both short-term and long-term context.
We demonstrate how this architecture can be extended to integrate salient
information from multiple modalities to automatically store and retrieve
important information for decision making without any supervision. We evaluate
the effectiveness of the proposed models on a novel multimodal dataset that we
introduce, consisting of 40,000 pedestrian trajectories, acquired jointly from
a radar system and a CCTV camera system installed in a public place. The
performance is also evaluated on the publicly available New York Grand Central
pedestrian database. In both settings, the proposed models demonstrate their
capability to better anticipate future pedestrian motion compared to existing
state of the art.
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
Authors: Zhen-Yu Wu, Zhangyang Wang, Zhaowen Wang, Hailin Jin
Date: 2018-07-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Privacy Preserving, Temporal Action Localization

This paper aims to improve privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, by formulating a unique adversarial training framework. The proposed framework explicitly learns a degradation transform for the original video inputs, in order to optimize the trade-off between target task performance and the associated privacy budgets on the degraded video. A notable challenge is that the privacy budget, often defined and measured in task-driven contexts, cannot be reliably indicated using any single model performance, because a strong protection of privacy has to sustain against any possible model that tries to hack privacy information. Such an uncommon situation has motivated us to propose two strategies, i.e., budget model restarting and ensemble, to enhance the generalization of the learned degradation on protecting privacy against unseen hacker models. Novel training strategies, evaluation protocols, and result visualization methods have been designed accordingly. Two experiments on privacy-preserving action recognition, with privacy budgets defined in various ways, manifest the compelling effectiveness of the proposed framework in simultaneously maintaining high target task (action recognition) performance while suppressing the privacy breach risk.
Examining Scientific Writing Styles from the Perspective of Linguistic Complexity
Authors: Chao Lu, Yi Bu, Jie Wang, Ying Ding, Vetle Torvik, Matthew Schnaars, Chengzhi Zhang
Date: 2018-07-22 00:00:00
Tasks: Articles, Diversity, Sentence

Publishing articles in high-impact English journals is difficult for scholars
around the world, especially for non-native English-speaking scholars (NNESs),
most of whom struggle with proficiency in English. In order to uncover the
differences in English scientific writing between native English-speaking
scholars (NESs) and NNESs, we collected a large-scale data set containing more
than 150,000 full-text articles published in PLoS between 2006 and 2015. We
divided these articles into three groups according to the ethnic backgrounds of
the first and corresponding authors, obtained by Ethnea, and examined the
scientific writing styles in English from a two-fold perspective of linguistic
complexity: (1) syntactic complexity, including measurements of sentence length
and sentence complexity; and (2) lexical complexity, including measurements of
lexical diversity, lexical density, and lexical sophistication. The
observations suggest marginal differences between groups in syntactical and
lexical complexity.
Knowledge-based Transfer Learning Explanation
Authors: Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan, Ian Horrocks, Huajun Chen
Date: 2018-07-22 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Transfer Learning

Machine learning explanation can significantly boost machine learning's
application in decision making, but the usability of current methods is limited
in human-centric explanation, especially for transfer learning, an important
machine learning branch that aims at utilizing knowledge from one learning
domain (i.e., a pair of dataset and prediction task) to enhance prediction
model training in another learning domain. In this paper, we propose an
ontology-based approach for human-centric explanation of transfer learning.
Three kinds of knowledge-based explanatory evidence, with different
granularities, including general factors, particular narrators and core
contexts are first proposed and then inferred with both local ontologies and
external knowledge bases. The evaluation with US flight data and DBpedia has
presented their confidence and availability in explaining the transferability
of feature representation in flight departure delay forecasting.
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
Authors: Chih-Chung Hsu, Chia-Wen Lin, Weng-Tai Su, Gene Cheung
Date: 2018-07-22 00:00:00
Tasks: Face Hallucination, Face Reconstruction, Face Verification, Generative Adversarial Network, Hallucination

Despite generative adversarial networks (GANs) can hallucinate
photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they
cannot guarantee preserving the identities of hallucinated HR faces, making the
HR faces poorly recognizable. To address this problem, we propose a Siamese GAN
(SiGAN) to reconstruct HR faces that visually resemble their corresponding
identities. On top of a Siamese network, the proposed SiGAN consists of a pair
of two identical generators and one discriminator. We incorporate
reconstruction error and identity label information in the loss function of
SiGAN in a pairwise manner. By iteratively optimizing the loss functions of the
generator pair and discriminator of SiGAN, we cannot only achieve
photo-realistic face reconstruction, but also ensures the reconstructed
information is useful for identity recognition. Experimental results
demonstrate that SiGAN significantly outperforms existing face hallucination
GANs in objective face verification performance, while achieving
photo-realistic reconstruction. Moreover, for input LR faces from unknown
identities who are not included in training, SiGAN can still do a good job.
Modeling Brain Networks with Artificial Neural Networks
Authors: Baran Baris Kivilcim, Itir Onal Ertugrul, Fatos T. Yarman Vural
Date: 2018-07-22 00:00:00

In this study, we propose a neural network approach to capture the functional
connectivities among anatomic brain regions. The suggested approach estimates a
set of brain networks, each of which represents the connectivity patterns of a
cognitive process. We employ two different architectures of neural networks to
extract directed and undirected brain networks from functional Magnetic
Resonance Imaging (fMRI) data. Then, we use the edge weights of the estimated
brain networks to train a classifier, namely, Support Vector Machines(SVM) to
label the underlying cognitive process. We compare our brain network models
with popular models, which generate similar functional brain networks. We
observe that both undirected and directed brain networks surpass the
performances of the network models used in the fMRI literature. We also observe
that directed brain networks offer more discriminative features compared to the
undirected ones for recognizing the cognitive processes. The representation
power of the suggested brain networks are tested in a task-fMRI dataset of
Human Connectome Project and a Complex Problem Solving dataset.
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning
Authors: Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer
Date: 2018-07-22 00:00:00
Tasks: Imitation Learning, MuJoCo

While imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which attempts to quantify the confidence of the novice policy as a proxy for safety. Our method, EnsembleDAgger, approximates a Gaussian Process using an ensemble of neural networks. Using the variance as a measure of confidence, we compute a decision rule that captures how much we doubt the novice, thus determining when it is safe to allow the novice to act. With this approach, we aim to maximize the novice's share of actions, while constraining the probability of failure. We demonstrate improved safety and learning performance compared to other DAgger variants and classic imitation learning on an inverted pendulum and in the MuJoCo HalfCheetah environment.
An Intersectional Definition of Fairness
Authors: James Foulds, Rashidul Islam, Kamrun Naher Keya, SHimei Pan
Date: 2018-07-22 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens arising from the Humanities literature which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including gender, race, sexual orientation, class, and disability. We show that our criteria behave sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. We provide a learning algorithm which respects our intersectional fairness criteria. Case studies on census data and the COMPAS criminal recidivism dataset demonstrate the utility of our methods.
MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage between Teams in MOBA Games
Authors: Lijun Yu, Dawei Zhang, Xiangqun Chen, Xing Xie
Date: 2018-07-22 00:00:00

Multiplayer Online Battle Arena (MOBA) is currently one of the most popular
genres of digital games around the world. The domain of knowledge contained in
these complicated games is large. It is hard for humans and algorithms to
evaluate the real-time game situation or predict the game result. In this
paper, we introduce MOBA-Slice, a time slice based evaluation framework of
relative advantage between teams in MOBA games. MOBA-Slice is a quantitative
evaluation method based on learning, similar to the value network of AlphaGo.
It establishes a foundation for further MOBA related research including AI
development. In MOBA-Slice, with an analysis of the deciding factors of MOBA
game results, we design a neural network model to fit our discounted evaluation
function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a
typical and popular MOBA game. Experiments on a large number of match replays
show that our model works well on arbitrary matches. MOBA-Slice not only has an
accuracy 3.7% higher than DotA Plus Assistant at result prediction, but also
supports the prediction of the remaining time of the game, and then realizes
the evaluation of relative advantage between teams.
A Statistical Method for Object Counting
Authors: Jans Glagolevs, Karlis Freivalds
Date: 2018-07-22 00:00:00
Tasks: Object, Object Counting

In this paper we present a new object counting method that is intended for
counting similarly sized and mostly round objects. Unlike many other algorithms
of the same purpose, the proposed method does not rely on identifying every
object, it uses statistical data obtained from the image instead. The method is
evaluated on images with human bone cells, oranges and pills achieving good
accuracy. Its strengths are ability to deal with touching and partly
overlapping objects, ability to work with different kinds of objects without
prior configuration and good performance.
AutoLoc: Weakly-supervised Temporal Action Localization
Authors: Zheng Shou, Hang Gao, Lei Zhang, Kazuyuki Miyazawa, Shih-Fu Chang
Date: 2018-07-22 00:00:00
Tasks: Action Localization, Temporal Action Localization, Weakly-supervised Temporal Action Localization

Temporal Action Localization (TAL) in untrimmed video is important for many
applications. But it is very expensive to annotate the segment-level ground
truth (action class and temporal boundary). This raises the interest of
addressing TAL with weak supervision, namely only video-level annotations are
available during training). However, the state-of-the-art weakly-supervised TAL
methods only focus on generating good Class Activation Sequence (CAS) over time
but conduct simple thresholding on CAS to localize actions. In this paper, we
first develop a novel weakly-supervised TAL framework called AutoLoc to
directly predict the temporal boundary of each action instance. We propose a
novel Outer-Inner-Contrastive (OIC) loss to automatically discover the needed
segment-level supervision for training such a boundary predictor. Our method
achieves dramatically improved performance: under the IoU threshold 0.5, our
method improves mAP on THUMOS'14 from 13.7% to 21.2% and mAP on ActivityNet
from 7.4% to 27.3%. It is also very encouraging to see that our
weakly-supervised method achieves comparable results with some fully-supervised
methods.
Skin Lesion Analysis Towards Melanoma Detection via End-to-end Deep Learning of Convolutional Neural Networks
Authors: Katherine M. Li, Evelyn C. Li
Date: 2018-07-22 00:00:00
Tasks: Segmentation

This article presents the design, experiments and results of our solution
submitted to the 2018 ISIC challenge: Skin Lesion Analysis Towards Melanoma
Detection. We design a pipeline using state-of-the-art Convolutional Neural
Network (CNN) models for a Lesion Boundary Segmentation task and a Lesion
Diagnosis task.
Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments
Authors: Zaid Tahir, Ahmed H. Qureshi, Yasar Ayaz, Raheel Nawaz
Date: 2018-07-22 00:00:00
Tasks: Motion Planning

Rapidly-exploring Random Tree star (RRT*) has recently gained immense
popularity in the motion planning community as it provides a probabilistically
complete and asymptotically optimal solution without requiring the complete
information of the obstacle space. In spite of all of its advantages, RRT*
converges to an optimal solution very slowly. Hence to improve the convergence
rate, its bidirectional variants were introduced, the Bi-directional RRT*
(B-RRT*) and Intelligent Bi-directional RRT* (IB-RRT*). However, as both
variants perform pure exploration, they tend to suffer in highly cluttered
environments. In order to overcome these limitations, we introduce a new
concept of potentially guided bidirectional trees in our proposed Potentially
Guided Intelligent Bi-directional RRT* (PIB-RRT*) and Potentially Guided
Bi-directional RRT* (PB-RRT*). The proposed algorithms greatly improve the
convergence rate and have a more efficient memory utilization. Theoretical and
experimental evaluation of the proposed algorithms have been made and compared
to the latest state of the art motion planning algorithms under different
challenging environmental conditions and have proven their remarkable
improvement in efficiency and convergence rate.
Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors
Authors: Nikhilesh Sharma, Nicholas Mastronarde, Jacob Chakareski
Date: 2018-07-22 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

We investigate an energy-harvesting wireless sensor transmitting latency-sensitive data over a fading channel. The sensor injects captured data packets into its transmission queue and relies on ambient energy harvested from the environment to transmit them. We aim to find the optimal scheduling policy that decides whether or not to transmit the queue's head-of-line packet at each transmission opportunity such that the expected packet queuing delay is minimized given the available harvested energy. No prior knowledge of the stochastic processes that govern the channel, captured data, or harvested energy dynamics are assumed, thereby necessitating the use of online learning to optimize the scheduling policy. We formulate this scheduling problem as a Markov decision process (MDP) and analyze the structural properties of its optimal value function. In particular, we show that it is non-decreasing and has increasing differences in the queue backlog and that it is non-increasing and has increasing differences in the battery state. We exploit this structure to formulate a novel accelerated reinforcement learning (RL) algorithm to solve the scheduling problem online at a much faster learning rate, while limiting the induced computational complexity. Our experiments demonstrate that the proposed algorithm closely approximates the performance of an optimal offline solution that requires a priori knowledge of the channel, captured data, and harvested energy dynamics. Simultaneously, by leveraging the value function's structure, our approach achieves competitive performance relative to a state-of-the-art RL algorithm, at potentially orders of magnitude lower complexity. Finally, considerable performance gains are demonstrated over the well-known and widely used Q-learning algorithm.
Unified Hypersphere Embedding for Speaker Recognition
Authors: Mahdi Hajibabaei, Dengxin Dai
Date: 2018-07-22 00:00:00
Tasks: Speaker Recognition, Text-Independent Speaker Recognition

Incremental improvements in accuracy of Convolutional Neural Networks are
usually achieved through use of deeper and more complex models trained on
larger datasets. However, enlarging dataset and models increases the
computation and storage costs and cannot be done indefinitely. In this work, we
seek to improve the identification and verification accuracy of a
text-independent speaker recognition system without use of extra data or deeper
and more complex models by augmenting the training and testing data, finding
the optimal dimensionality of embedding space and use of more discriminative
loss functions. Results of experiments on VoxCeleb dataset suggest that: (i)
Simple repetition and random time-reversion of utterances can reduce prediction
errors by up to 18%. (ii) Lower dimensional embeddings are more suitable for
verification. (iii) Use of proposed logistic margin loss function leads to
unified embeddings with state-of-the-art identification and competitive
verification accuracies.
Correlation Net: Spatiotemporal multimodal deep learning for action recognition
Authors: Novanto Yudistira, Takio Kurita
Date: 2018-07-22 00:00:00
Tasks: Action Recognition, Deep Learning, Multimodal Deep Learning, Temporal Action Localization, Video Recognition

This paper describes a network that captures multimodal correlations over arbitrary timestamps. The proposed scheme operates as a complementary, extended network over a multimodal convolutional neural network (CNN). Spatial and temporal streams are required for action recognition by a deep CNN, but overfitting reduction and fusing these two streams remain open problems. The existing fusion approach averages the two streams. Here we propose a correlation network with a Shannon fusion for learning a pre-trained CNN. A Long-range video may consist of spatiotemporal correlations over arbitrary times, which can be captured by forming the correlation network from simple fully connected layers. This approach was found to complement the existing network fusion methods. The importance of multimodal correlation is validated in comparison experiments on the UCF-101 and HMDB-51 datasets. The multimodal correlation enhanced the accuracy of the video recognition results.
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Authors: Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikael Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, Josien P. W. Pluim
Date: 2018-07-22 00:00:00
Tasks: Mitosis Detection, Prognosis, whole slide images

Tumor proliferation is an important biomarker indicative of the prognosis of
breast cancer patients. Assessment of tumor proliferation in a clinical setting
is highly subjective and labor-intensive task. Previous efforts to automate
tumor proliferation assessment by image analysis only focused on mitosis
detection in predefined tumor regions. However, in a real-world scenario,
automatic mitosis detection should be performed in whole-slide images (WSIs)
and an automatic method should be able to produce a tumor proliferation score
given a WSI as input. To address this, we organized the TUmor Proliferation
Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores
from WSIs. The challenge dataset consisted of 500 training and 321 testing
breast cancer histopathology WSIs. In order to ensure fair and independent
evaluation, only the ground truth for the training dataset was provided to the
challenge participants. The first task of the challenge was to predict mitotic
scores, i.e., to reproduce the manual method of assessing tumor proliferation
by a pathologist. The second task was to predict the gene expression based
PAM50 proliferation scores from the WSI. The best performing automatic method
for the first task achieved a quadratic-weighted Cohen's kappa score of
$\kappa$ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the
ground truth. For the second task, the predictions of the top method had a
Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the
ground truth. This was the first study that investigated tumor proliferation
assessment from WSIs. The achieved results are promising given the difficulty
of the tasks and weakly-labelled nature of the ground truth. However, further
research is needed to improve the practical utility of image analysis methods
for this task.