pwc-archive/papers-with-abstracts huggingface dataset:
Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System
Authors: Kevin K. Bowden, Jiaqi Wu, Wen Cui, Juraj Juraska, Vrindavan Harrison, Brian Schwarzmann, Nicholas Santer, Steve Whittaker, Marilyn Walker
Date: 2019-08-13 00:00:00
Tasks: Scheduling, Topic coverage

Conversational systems typically focus on functional tasks such as scheduling appointments or creating todo lists. Instead we design and evaluate SlugBot (SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support casual open-domain social inter-action. This novel application requires both broad topic coverage and engaging interactive skills. We developed a new technical approach to meet this demanding situation by crowd-sourcing novel content and introducing playful conversational strategies based on storytelling and games. We collected over 10,000 conversations during August 2018 as part of the Alexa Prize competition. We also conducted an in-lab follow-up qualitative evaluation. Over-all users found SB moderately engaging; conversations averaged 3.6 minutes and involved 26 user turns. However, users reacted very differently to different conversation subtypes. Storytelling and games were evaluated positively; these were seen as entertaining with predictable interactive structure. They also led users to impute personality and intelligence to SB. In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven. Theoretical and design implications suggest a move away from conversational systems that simply provide factual information. Future systems should be designed to have their own opinions with personal stories to share, and SB provides an example of how we might achieve this.
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
Authors: Taesun Whang, Dongyub Lee, Chanhee Lee, Kisu Yang, Dongsuk Oh, Heuiseok Lim
Date: 2019-08-13 00:00:00
Tasks: Conversational Response Selection, Language Modeling, Language Modelling, Retrieval

We focus on multi-turn response selection in a retrieval-based dialog system. In this paper, we utilize the powerful pre-trained language model Bi-directional Encoder Representations from Transformer (BERT) for a multi-turn dialog system and propose a highly effective post-training method on domain-specific corpus. Although BERT is easily adopted to various NLP tasks and outperforms previous baselines of each task, it still has limitations if a task corpus is too focused on a certain domain. Post-training on domain-specific corpus (e.g., Ubuntu Corpus) helps the model to train contextualized representations and words that do not appear in general corpus (e.g., English Wikipedia). Experimental results show that our approach achieves new state-of-the-art on two response selection benchmarks (i.e., Ubuntu Corpus V1, Advising Corpus) performance improvement by 5.9% and 6% on R@1.
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction
Authors: Jordan J. Bird, Diego R. Faria, Luis J. Manso, Anikó Ekárt, Christopher D. Buckingham
Date: 2019-08-13 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Sentiment Analysis, Sentiment Classification

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations.
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Authors: Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Gu, Vikash Kumar
Date: 2019-08-13 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit manipulation behaviors using a reinforcement learning (RL) approach. Our method hinges on the use of hierarchical sim2real -- a simulated environment is used to learn low-level goal-reaching skills, which are then used as the action space for a high-level RL controller, also trained in simulation. The full hierarchical policy is then transferred to the real world in a zero-shot fashion. The application of domain randomization during training enables the learned behaviors to generalize to real-world settings, while the use of hierarchy provides a modular paradigm for learning and transferring increasingly complex behaviors. We evaluate our method on a number of real-world tasks, including coordinated object manipulation in a multi-agent setting. See videos at https://sites.google.com/view/manipulation-via-locomotion
Requirements Engineering for Machine Learning: Perspectives from Data Scientists
Authors: Andreas Vogelsang, Markus Borg
Date: 2019-08-13 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requirements Engineering (RE) for ML-based systems. As a first step, we interviewed four data scientists to understand how ML experts approach elicitation, specification, and assurance of requirements and expectations. The results show that changes in the development paradigm, i.e., from coding to training, also demands changes in RE. We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process. Our study provides a first contribution towards an RE methodology for ML systems.
Convergence Rates of Variational Inference in Sparse Deep Learning
Authors: Badr-Eddine Chérief-Abdellatif
Date: 2019-08-09 00:00:00
Proceeding: ICML 2020 1
Tasks: Bayesian Inference, Deep Learning, Model Selection, Variational Inference

Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning. Meanwhile, a few recent works have provided theoretical justification and new insights on deep neural networks for estimating smooth functions in usual settings such as nonparametric regression. In this paper, we show that variational inference for sparse deep learning retains the same generalization properties than exact Bayesian inference. In particular, we highlight the connection between estimation and approximation theories via the classical bias-variance trade-off and show that it leads to near-minimax rates of convergence for H\"older smooth functions. Additionally, we show that the model selection framework over the neural network architecture via ELBO maximization does not overfit and adaptively achieves the optimal rate of convergence.
Differentially Private Link Prediction With Protected Connections
Authors: Abir De, Soumen Chakrabarti
Date: 2019-07-20 00:00:00
Tasks: Learning-To-Rank, Link Prediction, Prediction

Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some of their connections protected or private. Motivated by this observation, our goal is to design a differentially private LP algorithm, which trades off between privacy of the protected node-pairs and the link prediction accuracy. More specifically, we first propose a form of differential privacy on graphs, which models the privacy loss only of those node-pairs which are marked as protected. Next, we develop DPLP , a learning to rank algorithm, which applies a monotone transform to base scores from a non-private LP system, and then adds noise. DPLP is trained with a privacy induced ranking loss, which optimizes the ranking utility for a given maximum allowed level of privacy leakage of the protected node-pairs. Under a recently-introduced latent node embedding model, we present a formal trade-off between privacy and LP utility. Extensive experiments with several real-life graphs and several LP heuristics show that DPLP can trade off between privacy and predictive performance more effectively than several alternatives.
Deep material network with cohesive layers: Multi-stage training and interfacial failure analysis
Authors: Zeliang Liu
Date: 2019-08-07 00:00:00

A fundamental issue in multiscale materials modeling and design is the consideration of traction-separation behavior at the interface. By enriching the deep material network (DMN) with cohesive layers, the paper presents a novel data-driven material model which enables accurate and efficient prediction of multiscale responses for heterogeneous materials with interfacial effect. In the newly invoked cohesive building block, the fitting parameters have physical meanings related to the length scale and orientation of the cohesive layer. It is shown that the enriched material network can be effectively optimized via a multi-stage training strategy, with training data generated only from linear elastic direct numerical simulation (DNS). The extrapolation capability of the method to unknown material and loading spaces is demonstrated through the debonding analysis of a unidirectional fiber-reinforced composite, where the interface behavior is governed by an irreversible softening mixed-mode cohesive law. Its predictive accuracy is validated against the nonlinear path-dependent DNS results, and the reduction in computational time is particularly significant.
Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition
Authors: Subhadeep Dey, Petr Motlicek, Trung Bui, Franck Dernoncourt
Date: 2019-08-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Diversity, speech-recognition, Speech Recognition

In this paper, we explore various approaches for semi supervised learning in an end to end automatic speech recognition (ASR) framework. The first step in our approach involves training a seed model on the limited amount of labelled data. Additional unlabelled speech data is employed through a data selection mechanism to obtain the best hypothesized output, further used to retrain the seed model. However, uncertainties of the model may not be well captured with a single hypothesis. As opposed to this technique, we apply a dropout mechanism to capture the uncertainty by obtaining multiple hypothesized text transcripts of an speech recording. We assume that the diversity of automatically generated transcripts for an utterance will implicitly increase the reliability of the model. Finally, the data selection process is also applied on these hypothesized transcripts to reduce the uncertainty. Experiments on freely available TEDLIUM corpus and proprietary Adobe's internal dataset show that the proposed approach significantly reduces ASR errors, compared to the baseline model.
Skeleton Pruning as Trade-off between Skeleton Simplicity and Reconstruction Error
Authors: SHEN Wei1, BAI Xiang1∗, YANG XingWei2 & LATECKI Longin Jan2
Date: 2013-04-01 00:00:00
Proceeding: journal 2013 4

Skeletons can be viewed as a compact shape representation in that each shape can be completely
reconstructed from its skeleton. However, the usefulness of a skeletal representation is strongly limited by its
instability. Skeletons suffer from contour noise in that small contour deformation may lead to large structural
changes in the skeleton. A large number of skeleton computation and skeleton pruning approaches has been
proposed to address this issue. Our approach differs fundamentally in the fact that we cast skeleton pruning
as a trade-off between skeleton simplicity and shape reconstruction error. An ideal skeleton of a given shape
should be the skeleton with a simplest possible structure that provides a best possible reconstruction of a given
shape. To quantify this trade-off, we propose that the skeleton simplicity corresponds to model simplicity in the
Bayesian framework, and the shape reconstruction accuracy is expressed as goodness of fit to the data. We also
provide a simple algorithm to approximate the maximum of the Bayesian posterior probability which defines an
order for iteratively removing the end branches to obtain the pruned skeleton. Presented experimental results
obtained without any parameter tuning clearly demonstrate that the resulting skeletons are stable to boundary
deformations and intra class shape variability.
Robust People Detection using Computer Vision
Authors: Endri Dibra
Date: 2013-07-07 00:00:00
Proceeding: semanticscholar 2013 7

People detection is a challenging and interesting field of research that has
many applications, one of which being video surveillance. Besides monitoring or detection of abnormalities, there is an increasing trend in using video
surveillance to draw statistics that can help in managing and planning. In
an environment like a restaurant, an automatic system could be used to
determine the transition of people and the presence of seated people at tables. This information could help to direct the service for the tables. The
purpose of this thesis was then to develop a framework for automatic people
detection in restaurants. Adopting state-of-the-art Histograms of Oriented
Gradients (HOG) as feature descriptors and Linear Support Vector Machines (SVM) to train classifiers, we propose a novel method for people
detection in scenes seen from an oblique camera view. Unlike other holistic
methods, which train one general people classifier from available datasets of
people, like the INRIA dataset, and search through the whole image applying the classifier model in a sliding window multi-scale fashion, we propose
a method based on training of multiple specific classifiers in specific positions in the scene. This scheme allows users to overcome the difficulties
that arise from people occlusions and foreshortening that are inherent in
other holistic approaches. Furthermore, by performing training only with
data generated by synthetic models of humans in synthetic 3D models of
the scene, we avoid the problem of acquiring datasets of real images for
training. In addition, by performing training only with synthetic models
we provide ourselves with a rich dataset of humans in various poses and
articulations, not only for standing configurations, but also for sitting (our
method is general enough to extend to other poses, but detection of standing and sitting people is the focus here). We demonstrate that this method outperforms the state-of-the-art holistic HOG approach by comparing it to
four detectors trained on the INRIA dataset and on three different datasets
from our scene real footage. In the end, we show that our detector, which
performs single scale detection only on previously trained scene positions,
is twice faster when compared to the sliding window multi-scale methods.
PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation
Authors: Na Zhao, Tat-Seng Chua, Gim Hee Lee
Date: 2019-08-15 00:00:00
Tasks: Scene Segmentation, Segmentation, Semantic Segmentation

In this paper, we present the PS^2-Net -- a locally and globally aware deep learning framework for semantic segmentation on 3D scene-level point clouds. In order to deeply incorporate local structures and global context to support 3D scene segmentation, our network is built on four repeatedly stacked encoders, where each encoder has two basic components: EdgeConv that captures local structures and NetVLAD that models global context. Different from existing start-of-the-art methods for point-based scene semantic segmentation that either violate or do not achieve permutation invariance, our PS^2-Net is designed to be permutation invariant which is an essential property of any deep network used to process unordered point clouds. We further provide theoretical proof to guarantee the permutation invariance property of our network. We perform extensive experiments on two large-scale 3D indoor scene datasets and demonstrate that our PS2-Net is able to achieve state-of-the-art performances as compared to existing approaches.
Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography
Authors: Jelmer M. Wolterink, Tim Leiner, Ivana Išgum
Date: 2019-08-14 00:00:00
Tasks: Coronary Artery Segmentation

Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices in the mesh graph. The method was trained and evaluated using the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Surface meshes enclosing the full coronary artery tree were automatically extracted. A quantitative evaluation on 78 coronary artery segments showed that these meshes corresponded closely to reference annotations, with a Dice similarity coefficient of 0.75/0.73, a mean surface distance of 0.25/0.28 mm, and a Hausdorff distance of 1.53/1.86 mm in healthy/diseased vessel segments. The results showed that inclusion of mesh information in a GCN improves segmentation overlap and accuracy over a baseline model without interaction on the mesh. The results indicate that GCNs allow efficient extraction of coronary artery surface meshes and that the use of GCNs leads to regular and more accurate meshes.
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Authors: Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
Date: 2019-08-15 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Semantic Segmentation, Transfer Learning, Tumor Segmentation

Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods are inapplicable for small datasets, which are very common in medical problems. To this end, we propose a knowledge transfer method between diseases via the Generative Bayesian Prior network. Our approach is compared to a pre-train approach and random initialization and obtains the best results in terms of Dice Similarity Coefficient metric for the small subsets of the Brain Tumor Segmentation 2018 database (BRATS2018).
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation
Authors: Balamurali Murugesan, Kaushik Sarveswaran, Sharath M. Shankaranarayana, Keerthi Ram, Jayaraj Joseph, Mohanasankar Sivaprakasam
Date: 2019-08-14 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Multi-Task Learning, Semantic Segmentation

For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications. A rising trend in these architectures is to employ joint-learning of the target region with an auxiliary task, a method commonly known as multi-task learning. These approaches help impose smoothness and shape priors, which vanilla FCN approaches do not necessarily incorporate. In this paper, we propose a novel plug-and-play module, which we term as Conv-MCD, which exploits structural information in two ways - i) using the contour map and ii) using the distance map, both of which can be obtained from ground truth segmentation maps with no additional annotation costs. The key benefit of our module is the ease of its addition to any state-of-the-art architecture, resulting in a significant improvement in performance with a minimal increase in parameters. To substantiate the above claim, we conduct extensive experiments using 4 state-of-the-art architectures across various evaluation metrics, and report a significant increase in performance in relation to the base networks. In addition to the aforementioned experiments, we also perform ablative studies and visualization of feature maps to further elucidate our approach.
PHYRE: A New Benchmark for Physical Reasoning
Authors: Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross Girshick
Date: 2019-08-15 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Visual Reasoning

Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn efficient but useful models of physics. For code and to play PHYRE for yourself, please visit https://player.phyre.ai.
A Multimodal Vision Sensor for Autonomous Driving
Authors: Dongming Sun, Xiao Huang, Kailun Yang
Date: 2019-08-15 00:00:00
Tasks: Autonomous Driving, Semantic Segmentation

This paper describes a multimodal vision sensor that integrates three types of cameras, including a stereo camera, a polarization camera and a panoramic camera. Each sensor provides a specific dimension of information: the stereo camera measures depth per pixel, the polarization obtains the degree of polarization, and the panoramic camera captures a 360-degree landscape. Data fusion and advanced environment perception could be built upon the combination of sensors. Designed especially for autonomous driving, this vision sensor is shipped with a robust semantic segmentation network. In addition, we demonstrate how cross-modal enhancement could be achieved by registering the color image and the polarization image. An example of water hazard detection is given. To prove the multimodal vision sensor's compatibility with different devices, a brief runtime performance analysis is carried out.