pwc-archive/papers-with-abstracts huggingface dataset:
Data Driven Computing with Noisy Material Data Sets
Authors: T.Kirchdoerfer, M.Ortiz
Date: 2017-11-01 00:00:00
Proceeding: Computer Methods in Applied Mechanics and Engineering 2017 11
Tasks: Clustering, Stress-Strain Relation

We formulate a Data Driven Computing paradigm, termed max-ent Data Driven Computing, that generalizes distance-minimizing Data Driven Computing and is robust with respect to outliers. Robustness is achieved by means of clustering analysis. Specifically, we assign data points a variable relevance depending on distance to the solution and on maximum-entropy estimation. The resulting scheme consists of the minimization of a suitably-defined free energy over phase space subject to compatibility and equilibrium constraints. Distance-minimizing Data Driven schemes are recovered in the limit of zero temperature. We present selected numerical tests that establish the convergence properties of the max-ent Data Driven solvers and solutions.
Unbiased Learning to Rank with Unbiased Propensity Estimation
Authors: Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft
Date: 2018-04-16 00:00:00
Tasks: Learning-To-Rank, parameter estimation

Learning to rank with biased click data is a well-known challenge. A variety
of methods has been explored to debias click data for learning to rank such as
click models, result interleaving and, more recently, the unbiased
learning-to-rank framework based on inverse propensity weighting. Despite their
differences, most existing studies separate the estimation of click bias
(namely the \textit{propensity model}) from the learning of ranking algorithms.
To estimate click propensities, they either conduct online result
randomization, which can negatively affect the user experience, or offline
parameter estimation, which has special requirements for click data and is
optimized for objectives (e.g. click likelihood) that are not directly related
to the ranking performance of the system. In this work, we address those
problems by unifying the learning of propensity models and ranking models. We
find that the problem of estimating a propensity model from click data is a
dual problem of unbiased learning to rank. Based on this observation, we
propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker
and an \textit{unbiased propensity model}. DLA is an automatic unbiased
learning-to-rank framework as it directly learns unbiased ranking models from
biased click data without any preprocessing. It can adapt to the change of bias
distributions and is applicable to online learning. Our empirical experiments
with synthetic and real-world data show that the models trained with DLA
significantly outperformed the unbiased learning-to-rank algorithms based on
result randomization and the models trained with relevance signals extracted by
click models.
Variational MCMC
Authors: Nando de Freitas, Pedro Hojen-Sorensen, Michael. I. Jordan, Stuart Russell
Date: 2013-01-10 00:00:00
Tasks: parameter estimation

We propose a new class of learning algorithms that combines variational
approximation and Markov chain Monte Carlo (MCMC) simulation. Naive algorithms
that use the variational approximation as proposal distribution can perform
poorly because this approximation tends to underestimate the true variance and
other features of the data. We solve this problem by introducing more
sophisticated MCMC algorithms. One of these algorithms is a mixture of two MCMC
kernels: a random walk Metropolis kernel and a blockMetropolis-Hastings (MH)
kernel with a variational approximation as proposaldistribution. The MH kernel
allows one to locate regions of high probability efficiently. The Metropolis
kernel allows us to explore the vicinity of these regions. This algorithm
outperforms variationalapproximations because it yields slightly better
estimates of the mean and considerably better estimates of higher moments, such
as covariances. It also outperforms standard MCMC algorithms because it locates
theregions of high probability quickly, thus speeding up convergence. We
demonstrate this algorithm on the problem of Bayesian parameter estimation for
logistic (sigmoid) belief networks.
Deep Unsupervised Cardinality Estimation
Authors: Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, Ion Stoica
Date: 2019-05-10 00:00:00
Tasks: Density Estimation

Cardinality estimation has long been grounded in statistical tools for density estimation. To capture the rich multivariate distributions of relational tables, we propose the use of a new type of high-capacity statistical model: deep autoregressive models. However, direct application of these models leads to a limited estimator that is prohibitively expensive to evaluate for range or wildcard predicates. To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more. Like classical synopses, our estimator summarizes the data without supervision. Unlike previous solutions, we approximate the joint data distribution without any independence assumptions. Evaluated on real-world datasets and compared against real systems and dominant families of techniques, our estimator achieves single-digit multiplicative error at tail, an up to 90$\times$ accuracy improvement over the second best method, and is space- and runtime-efficient.
Mutual Information Scaling and Expressive Power of Sequence Models
Authors: Huitao Shen
Date: 2019-05-10 00:00:00

Sequence models assign probabilities to variable-length sequences such as natural language texts. The ability of sequence models to capture temporal dependence can be characterized by the temporal scaling of correlation and mutual information. In this paper, we study the mutual information of recurrent neural networks (RNNs) including long short-term memories and self-attention networks such as Transformers. Through a combination of theoretical study of linear RNNs and empirical study of nonlinear RNNs, we find their mutual information decays exponentially in temporal distance. On the other hand, Transformers can capture long-range mutual information more efficiently, making them preferable in modeling sequences with slow power-law mutual information, such as natural languages and stock prices. We discuss the connection of these results with statistical mechanics. We also point out the non-uniformity problem in many natural language datasets. We hope this work provides a new perspective in understanding the expressive power of sequence models and shed new light on improving the architecture of them.
Interpreting and Evaluating Neural Network Robustness
Authors: Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen
Date: 2019-05-10 00:00:00
Tasks: Adversarial Attack

Recently, adversarial deception becomes one of the most considerable threats to deep neural networks. However, compared to extensive research in new designs of various adversarial attacks and defenses, the neural networks' intrinsic robustness property is still lack of thorough investigation. This work aims to qualitatively interpret the adversarial attack and defense mechanism through loss visualization, and establish a quantitative metric to evaluate the neural network model's intrinsic robustness. The proposed robustness metric identifies the upper bound of a model's prediction divergence in the given domain and thus indicates whether the model can maintain a stable prediction. With extensive experiments, our metric demonstrates several advantages over conventional adversarial testing accuracy based robustness estimation: (1) it provides a uniformed evaluation to models with different structures and parameter scales; (2) it over-performs conventional accuracy based robustness estimation and provides a more reliable evaluation that is invariant to different test settings; (3) it can be fast generated without considerable testing cost.
Exploiting temporal context for 3D human pose estimation in the wild
Authors: Anurag Arnab, Carl Doersch, Andrew Zisserman
Date: 2019-05-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

We present a bundle-adjustment-based algorithm for recovering accurate 3D human pose and meshes from monocular videos. Unlike previous algorithms which operate on single frames, we show that reconstructing a person over an entire sequence gives extra constraints that can resolve ambiguities. This is because videos often give multiple views of a person, yet the overall body shape does not change and 3D positions vary slowly. Our method improves not only on standard mocap-based datasets like Human 3.6M -- where we show quantitative improvements -- but also on challenging in-the-wild datasets such as Kinetics. Building upon our algorithm, we present a new dataset of more than 3 million frames of YouTube videos from Kinetics with automatically generated 3D poses and meshes. We show that retraining a single-frame 3D pose estimator on this data improves accuracy on both real-world and mocap data by evaluating on the 3DPW and HumanEVA datasets.
Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web
Authors: Demetris Paschalides, Alexandros Kornilakis, Chrysovalantis Christodoulou, Rafael Andreou, George Pallis, Marios D. Dikaiakos, Evangelos Markatos
Date: 2019-05-10 00:00:00
Tasks: Articles, Fake News Detection, Misinformation

Over the past few years, we have been witnessing the rise of misinformation on the Web. People fall victims of fake news during their daily lives and assist their further propagation knowingly and inadvertently. There have been many initiatives that are trying to mitigate the damage caused by fake news, focusing on signals from either domain flag-lists, online social networks or artificial intelligence. In this work, we present Check-It, a system that combines, in an intelligent way, a variety of signals into a pipeline for fake news identification. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection, respecting the user's privacy. Experimental results show that Check-It is able to outperform the state-of-the-art methods. On a dataset, consisting of 9 millions of articles labeled as fake and real, Check-It obtains classification accuracies that exceed 99%.
Breast Tumor Classification and Segmentation using Convolutional Neural Networks
Authors: Parvin Yousefikamal
Date: 2019-05-10 00:00:00
Tasks: Classification, Clustering, General Classification, image-classification, Image Classification, Segmentation

Breast cancer is considered as the most fatal type of cancer among women worldwide and it is crucially important to be diagnosed at its early stages. In the current study, we aim to represent a fast and efficient framework which consists of two main parts:1- image classification, and 2- tumor region segmentation. At the initial stage, the images are classified into the two categories of normal and abnormal. Since the Deep Neural Networks have performed successfully in machine vision task, we would employ the convolutional neural networks for the classification of images. In the second stage, the suggested framework is to diagnose and segment the tumor in the mammography images. First, the mammography images are pre-processed by removing noise and artifacts, and then, segment the image using the level-set algorithm based on the spatial fuzzy c-means clustering. The proper initialization and optimal configuration have strong effects on the performance of the level-set segmentation. Thus, in our suggested framework, we have improved the level-set algorithm by utilizing the spatial fuzzy c-means clustering which ultimately results in a more precise segmentation. In order to evaluate the proposed approach, we conducted experiments using the Mammographic Image Analysis (MIAS) dataset. The tests have shown that the convolutional neural networks could achieve high accuracy in classification of images. Moreover, the improved level-set segmentation method, along with the fuzzy c-means clustering, could perfectly do the segmentation on the tumor area. The suggested method has classified the images with the accuracy of 78% and the AUC of 69%, which, as compared to the previous methods, is 2% more accurate and 6% better AUC; and has been able to extract the tumor area in a more precise way.
Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks
Authors: Zhengwei Wang, Qi She, Alan F. Smeaton, Tomas E. Ward, Graham Healy
Date: 2019-05-10 00:00:00
Tasks: Image Generation, Speech Synthesis

Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. Arguably the most striking results have been in the area of image synthesis. However, evaluating the performance of GANs is still an open and challenging problem. Existing evaluation metrics primarily measure the dissimilarity between real and generated images using automated statistical methods. They often require large sample sizes for evaluation and do not directly reflect human perception of image quality. In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals. Our results show that Neuroscore has superior performance to the current evaluation metrics in that: (1) It is more consistent with human judgment; (2) The evaluation process needs much smaller numbers of samples; and (3) It is able to rank the quality of images on a per GAN basis. A convolutional neural network (CNN) based neuro-AI interface is proposed to predict Neuroscore from GAN-generated images directly without the need for neural responses. Importantly, we show that including neural responses during the training phase of the network can significantly improve the prediction capability of the proposed model. Materials related to this work are provided at https://github.com/villawang/Neuro-AI-Interface.
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model
Authors: Tong Wang, Qihang Lin
Date: 2019-05-10 00:00:00
Tasks: Interpretable Machine Learning, model

Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpretable models). In this work, we propose a novel framework for building a Hybrid Predictive Model (HPM) that integrates an interpretable model with any black-box model to combine their strengths. The interpretable model substitutes the black-box model on a subset of data where the black-box is overkill or nearly overkill, gaining transparency at no or low cost of the predictive accuracy. We design a principled objective function that considers predictive accuracy, model interpretability, and model transparency (defined as the percentage of data processed by the interpretable substitute.) Under this framework, we propose two hybrid models, one substituting with association rules and the other with linear models, and we design customized training algorithms for both models. We test the hybrid models on structured data and text data where interpretable models collaborate with various state-of-the-art black-box models. Results show that hybrid models obtain an efficient trade-off between transparency and predictive performance, characterized by our proposed efficient frontiers.
Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy
Authors: Patrik Christen, Olivier Del Fabbro
Date: 2019-05-10 00:00:00
Tasks: Philosophy

Many computer models such as cellular automata and artificial neural networks have been developed and successfully applied. However, in some cases, these models might be restrictive on the possible solutions or their solutions might be difficult to interpret. To overcome this problem, we outline a new approach, the so-called allagmatic method, that automatically programs and executes models with as little limitations as possible while maintaining human interpretability. Earlier we described a metamodel and its building blocks according to the philosophical concepts of structure (spatial dimension) and operation (temporal dimension). They are entity, milieu, and update function that together abstractly describe cellular automata, artificial neural networks, and possibly any kind of computer model. By automatically combining these building blocks in an evolutionary computation, interpretability might be increased by the relationship to the metamodel, and models might be translated into more interpretable models via the metamodel. We propose generic and object-oriented programming to implement the entities and their milieus as dynamic and generic arrays and the update function as a method. We show two experiments where a simple cellular automaton and an artificial neural network are automatically programmed, compiled, and executed. A target state is successfully evolved and learned in the cellular automaton and artificial neural network, respectively. We conclude that the allagmatic method can create and execute cellular automaton and artificial neural network models in an automated manner with the guidance of philosophy.
Semi-supervised and Population Based Training for Voice Commands Recognition
Authors: Oguz H. Elibol, Gokce Keskin, Anil Thomas
Date: 2019-05-10 00:00:00
Tasks: Classification, General Classification, Model Optimization

We present a rapid design methodology that combines automated hyper-parameter tuning with semi-supervised training to build highly accurate and robust models for voice commands classification. Proposed approach allows quick evaluation of network architectures to fit performance and power constraints of available hardware, while ensuring good hyper-parameter choices for each network in real-world scenarios. Leveraging the vast amount of unlabeled data with a student/teacher based semi-supervised method, classification accuracy is improved from 84% to 94% in the validation set. For model optimization, we explore the hyper-parameter space through population based training and obtain an optimized model in the same time frame as it takes to train a single model.
Language Modeling with Deep Transformers
Authors: Kazuki Irie, Albert Zeyer, Ralf Schlüter, Hermann Ney
Date: 2019-05-10 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We explore deep autoregressive Transformer models in language modeling for speech recognition. We focus on two aspects. First, we revisit Transformer model configurations specifically for language modeling. We show that well configured Transformer models outperform our baseline models based on the shallow stack of LSTM recurrent neural network layers. We carry out experiments on the open-source LibriSpeech 960hr task, for both 200K vocabulary word-level and 10K byte-pair encoding subword-level language modeling. We apply our word-level models to conventional hybrid speech recognition by lattice rescoring, and the subword-level models to attention based encoder-decoder models by shallow fusion. Second, we show that deep Transformer language models do not require positional encoding. The positional encoding is an essential augmentation for the self-attention mechanism which is invariant to sequence ordering. However, in autoregressive setup, as is the case for language modeling, the amount of information increases along the position dimension, which is a positional signal by its own. The analysis of attention weights shows that deep autoregressive self-attention models can automatically make use of such positional information. We find that removing the positional encoding even slightly improves the performance of these models.
Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs
Authors: Okan Köpüklü, Yao Rong, Gerhard Rigoll
Date: 2019-05-10 00:00:00

The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. As the technology advances and communication between humans and machines becomes more complex, HCI systems should also be scaled accordingly in order to accommodate the introduced complexities. In this paper, we propose a methodology to scale hand gestures by forming them with predefined gesture-phonemes, and a convolutional neural network (CNN) based framework to recognize hand gestures by learning only their constituents of gesture-phonemes. The total number of possible hand gestures can be increased exponentially by increasing the number of used gesture-phonemes. For this objective, we introduce a new benchmark dataset named Scaled Hand Gestures Dataset (SHGD) with only gesture-phonemes in its training set and 3-tuples gestures in the test set. In our experimental analysis, we achieve to recognize hand gestures containing one and three gesture-phonemes with an accuracy of 98.47% (in 15 classes) and 94.69% (in 810 classes), respectively. Our dataset, code and pretrained models are publicly available.
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
Authors: Rob Ashmore, Radu Calinescu, Colin Paterson
Date: 2019-05-10 00:00:00
Tasks: BIG-bench Machine Learning, Survey

Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic and political agendas. Such unprecedented interest is fuelled by a vision of ML applicability extending to healthcare, transportation, defence and other domains of great societal importance. Achieving this vision requires the use of ML in safety-critical applications that demand levels of assurance beyond those needed for current ML applications. Our paper provides a comprehensive survey of the state-of-the-art in the assurance of ML, i.e. in the generation of evidence that ML is sufficiently safe for its intended use. The survey covers the methods capable of providing such evidence at different stages of the machine learning lifecycle, i.e. of the complex, iterative process that starts with the collection of the data used to train an ML component for a system, and ends with the deployment of that component within the system. The paper begins with a systematic presentation of the ML lifecycle and its stages. We then define assurance desiderata for each stage, review existing methods that contribute to achieving these desiderata, and identify open challenges that require further research.
EdgeSegNet: A Compact Network for Semantic Segmentation
Authors: Zhong Qiu Lin, Brendan Chwyl, Alexander Wong
Date: 2019-05-10 00:00:00
Tasks: Segmentation, Semantic Segmentation

In this study, we introduce EdgeSegNet, a compact deep convolutional neural network for the task of semantic segmentation. A human-machine collaborative design strategy is leveraged to create EdgeSegNet, where principled network design prototyping is coupled with machine-driven design exploration to create networks with customized module-level macroarchitecture and microarchitecture designs tailored for the task. Experimental results showed that EdgeSegNet can achieve semantic segmentation accuracy comparable with much larger and computationally complex networks (>20x} smaller model size than RefineNet) as well as achieving an inference speed of ~38.5 FPS on an NVidia Jetson AGX Xavier. As such, the proposed EdgeSegNet is well-suited for low-power edge scenarios.