pwc-archive/papers-with-abstracts huggingface dataset:
Subspace approximation with outliers
Authors: Amit Deshpande, Rameshwar Pratap
Date: 2020-06-30 00:00:00
Tasks: Dimensionality Reduction

The subspace approximation problem with outliers, for given $n$ points in $d$ dimensions $x_{1},\ldots, x_{n} \in R^{d}$, an integer $1 \leq k \leq d$, and an outlier parameter $0 \leq \alpha \leq 1$, is to find a $k$-dimensional linear subspace of $R^{d}$ that minimizes the sum of squared distances to its nearest $(1-\alpha)n$ points. More generally, the $\ell_{p}$ subspace approximation problem with outliers minimizes the sum of $p$-th powers of distances instead of the sum of squared distances. Even the case of robust PCA is non-trivial, and previous work requires additional assumptions on the input. Any multiplicative approximation algorithm for the subspace approximation problem with outliers must solve the robust subspace recovery problem, a special case in which the $(1-\alpha)n$ inliers in the optimal solution are promised to lie exactly on a $k$-dimensional linear subspace. However, robust subspace recovery is Small Set Expansion (SSE)-hard. We show how to extend dimension reduction techniques and bi-criteria approximations based on sampling to the problem of subspace approximation with outliers. To get around the SSE-hardness of robust subspace recovery, we assume that the squared distance error of the optimal $k$-dimensional subspace summed over the optimal $(1-\alpha)n$ inliers is at least $\delta$ times its squared-error summed over all $n$ points, for some $0 < \delta \leq 1 - \alpha$. With this assumption, we give an efficient algorithm to find a subset of $poly(k/\epsilon) \log(1/\delta) \log\log(1/\delta)$ points whose span contains a $k$-dimensional subspace that gives a multiplicative $(1+\epsilon)$-approximation to the optimal solution. The running time of our algorithm is linear in $n$ and $d$. Interestingly, our results hold even when the fraction of outliers $\alpha$ is large, as long as the obvious condition $0 < \delta \leq 1 - \alpha$ is satisfied.
Vehicle Re-ID for Surround-view Camera System
Authors: Zizhang Wu, Man Wang, Lingxiao Yin, Weiwei Sun, Jason Wang, Huangbin Wu
Date: 2020-06-30 00:00:00
Tasks: Autonomous Driving, Vehicle Re-Identification

The vehicle re-identification (ReID) plays a critical role in the perception system of autonomous driving, which attracts more and more attention in recent years. However, to our best knowledge, there is no existing complete solution for the surround-view system mounted on the vehicle. In this paper, we argue two main challenges in above scenario: i) In single camera view, it is difficult to recognize the same vehicle from the past image frames due to the fisheye distortion, occlusion, truncation, etc. ii) In multi-camera view, the appearance of the same vehicle varies greatly from different camera's viewpoints. Thus, we present an integral vehicle Re-ID solution to address these problems. Specifically, we propose a novel quality evaluation mechanism to balance the effect of tracking box's drift and target's consistency. Besides, we take advantage of the Re-ID network based on attention mechanism, then combined with a spatial constraint strategy to further boost the performance between different cameras. The experiments demonstrate that our solution achieves state-of-the-art accuracy while being real-time in practice. Besides, we will release the code and annotated fisheye dataset for the benefit of community.
A Generative Neural Network Framework for Automated Software Testing
Authors: Leonid Joffe, David J. Clark
Date: 2020-06-29 00:00:00
Tasks: software testing

Search Based Software Testing (SBST) is a popular automated testing technique which uses a feedback mechanism to search for faults in software. Despite its popularity, it has fundamental challenges related to the design, construction and interpretation of the feedback. Neural Networks (NN) have been hugely popular in recent years for a wide range of tasks. We believe that they can address many of the issues inherent to common SBST approaches. Unfortunately, NNs require large and representative training datasets. In this work we present an SBST framework based on a deconvolutional generative neural network. Not only does it retain the beneficial qualities that make NNs appropriate for SBST tasks, it also produces its own training data which circumvents the problem of acquiring a training dataset that limits the use of NNs. We demonstrate through a series of experiments that this architecture is possible and practical. It generates diverse, sensible program inputs, while exploring the space of program behaviours. It also creates a meaningful ordering over program behaviours and is able to find crashing executions. This is all done without any prior knowledge of the program. We believe this proof of concept opens new directions for future work at the intersection of SBST and neural networks.
Statistical Mechanical Analysis of Neural Network Pruning
Authors: Rupam Acharyya, Ankani Chattoraj, Boyu Zhang, Shouman Das, Daniel Stefankovic
Date: 2020-06-30 00:00:00
Tasks: Computational Efficiency, Network Pruning

Deep learning architectures with a huge number of parameters are often compressed using pruning techniques to ensure computational efficiency of inference during deployment. Despite multitude of empirical advances, there is a lack of theoretical understanding of the effectiveness of different pruning methods. We inspect different pruning techniques under the statistical mechanics formulation of a teacher-student framework and derive their generalization error (GE) bounds. It has been shown that Determinantal Point Process (DPP) based node pruning method is notably superior to competing approaches when tested on real datasets. Using GE bounds in the aforementioned setup we provide theoretical guarantees for their empirical observations. Another consistent finding in literature is that sparse neural networks (edge pruned) generalize better than dense neural networks (node pruned) for a fixed number of parameters. We use our theoretical setup to prove this finding and show that even the baseline random edge pruning method performs better than the DPP node pruning method. We also validate this empirically on real datasets.
Recovering Joint Probability of Discrete Random Variables from Pairwise Marginals
Authors: Shahana Ibrahim, Xiao Fu
Date: 2020-06-30 00:00:00
Tasks: Tensor Decomposition

Learning the joint probability of random variables (RVs) is the cornerstone of statistical signal processing and machine learning. However, direct nonparametric estimation for high-dimensional joint probability is in general impossible, due to the curse of dimensionality. Recent work has proposed to recover the joint probability mass function (PMF) of an arbitrary number of RVs from three-dimensional marginals, leveraging the algebraic properties of low-rank tensor decomposition and the (unknown) dependence among the RVs. Nonetheless, accurately estimating three-dimensional marginals can still be costly in terms of sample complexity, affecting the performance of this line of work in practice in the sample-starved regime. Using three-dimensional marginals also involves challenging tensor decomposition problems whose tractability is unclear. This work puts forth a new framework for learning the joint PMF using only pairwise marginals, which naturally enjoys a lower sample complexity relative to the third-order ones. A coupled nonnegative matrix factorization (CNMF) framework is developed, and its joint PMF recovery guarantees under various conditions are analyzed. Our method also features a Gram--Schmidt (GS)-like algorithm that exhibits competitive runtime performance. The algorithm is shown to provably recover the joint PMF up to bounded error in finite iterations, under reasonable conditions. It is also shown that a recently proposed economical expectation maximization (EM) algorithm guarantees to improve upon the GS-like algorithm's output, thereby further lifting up the accuracy and efficiency. Real-data experiments are employed to showcase the effectiveness.
Optimization Landscape of Tucker Decomposition
Authors: Abraham Frandsen, Rong Ge
Date: 2020-06-29 00:00:00

Tucker decomposition is a popular technique for many data analysis and machine learning applications. Finding a Tucker decomposition is a nonconvex optimization problem. As the scale of the problems increases, local search algorithms such as stochastic gradient descent have become popular in practice. In this paper, we characterize the optimization landscape of the Tucker decomposition problem. In particular, we show that if the tensor has an exact Tucker decomposition, for a standard nonconvex objective of Tucker decomposition, all local minima are also globally optimal. We also give a local search algorithm that can find an approximate local (and global) optimal solution in polynomial time.
Conditional GAN for timeseries generation
Authors: Kaleb E Smith, Anthony O. Smith
Date: 2020-06-30 00:00:00
Tasks: Generative Adversarial Network, Time Series, Time Series Analysis

It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. This work attempts to ease the frustration by proposing a new architecture, Time Series GAN (TSGAN), to model realistic time series data. We evaluate TSGAN on 70 data sets from a benchmark time series database. Our results demonstrate that TSGAN performs better than the competition both quantitatively using the Frechet Inception Score (FID) metric, and qualitatively when classification is used as the evaluation criteria.
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Authors: Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge
Date: 2020-06-30 00:00:00

Choosing the right parameters for optimization algorithms is often the key to their success in practice. Solving this problem using a learning-to-learn approach -- using meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates -- was recently shown to be effective. However, the meta-optimization problem is difficult. In particular, the meta-gradient can often explode/vanish, and the learned optimizer may not have good generalization performance if the meta-objective is not chosen carefully. In this paper we give meta-optimization guarantees for the learning-to-learn approach on a simple problem of tuning the step size for quadratic loss. Our results show that the na\"ive objective suffers from meta-gradient explosion/vanishing problem. Although there is a way to design the meta-objective so that the meta-gradient remains polynomially bounded, computing the meta-gradient directly using backpropagation leads to numerical issues. We also characterize when it is necessary to compute the meta-objective on a separate validation set to ensure the generalization performance of the learned optimizer. Finally, we verify our results empirically and show that a similar phenomenon appears even for more complicated learned optimizers parametrized by neural networks.
Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis
Authors: Christian A. Schroth, Michael Muma
Date: 2020-06-30 00:00:00
Tasks: Clustering

This article proposes a new class of Real Elliptically Skewed (RESK) distributions and associated clustering algorithms that allow for integrating robustness and skewness into a single unified cluster analysis framework. Non-symmetrically distributed and heavy-tailed data clusters have been reported in a variety of real-world applications. Robustness is essential because a few outlying observations can severely obscure the cluster structure. The RESK distributions are a generalization of the Real Elliptically Symmetric (RES) distributions. To estimate the cluster parameters and memberships, we derive an expectation maximization (EM) algorithm for arbitrary RESK distributions. Special attention is given to a new robust skew-Huber M-estimator, which is also the maximum likelihood estimator (MLE) for the skew-Huber distribution that belongs to the RESK class. Numerical experiments on simulated and real-world data confirm the usefulness of the proposed methods for skewed and heavy-tailed data sets.
Federated Mutual Learning
Authors: Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, Chao Wu
Date: 2020-06-27 00:00:00
Tasks: Federated Learning

Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about distinctive challenges to the canonical federated learning algorithm (FedAvg). First, due to the Non-IIDness of data, the global shared model may perform worse than local models that solely trained on their private data; Second, the objective of center server and clients may be different, where center server seeks for a generalized model whereas client pursue a personalized model, and clients may run different tasks; Third, clients may need to design their customized model for various scenes and tasks; In this work, we present a novel federated learning paradigm, named Federated Mutual Leaning (FML), dealing with the three heterogeneities. FML allows clients training a generalized model collaboratively and a personalized model independently, and designing their private customized models. Thus, the Non-IIDness of data is no longer a bug but a feature that clients can be personally served better. The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph
Authors: Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
Date: 2020-06-30 00:00:00
Tasks: Attribute, Prediction, Referring Expression Comprehension, Sentence, Visual Question Answering (VQA)

We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects) across vision and language, which are essential to vision-language cross-modal tasks. Utilizing scene graphs of visual scenes, ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction tasks in the pre-training phase. Specifically, these prediction tasks are implemented by predicting nodes of different types in the scene graph parsed from the sentence. Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language. After pre-training on large scale image-text aligned datasets, we validate the effectiveness of ERNIE-ViL on 5 cross-modal downstream tasks. ERNIE-ViL achieves state-of-the-art performances on all these tasks and ranks the first place on the VCR leaderboard with an absolute improvement of 3.7%.
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
Authors: Rie Johnson, Tong Zhang
Date: 2020-06-30 00:00:00
Proceeding: ICML 2020 1

This paper presents a framework of successive functional gradient optimization for training nonconvex models such as neural networks, where training is driven by mirror descent in a function space. We provide a theoretical analysis and empirical study of the training method derived from this framework. It is shown that the method leads to better performance than that of standard training techniques.
Optimal Rates of Distributed Regression with Imperfect Kernels
Authors: Hongwei Sun, Qiang Wu
Date: 2020-06-30 00:00:00
Tasks: regression

Distributed machine learning systems have been receiving increasing attentions for their efficiency to process large scale data. Many distributed frameworks have been proposed for different machine learning tasks. In this paper, we study the distributed kernel regression via the divide and conquer approach. This approach has been proved asymptotically minimax optimal if the kernel is perfectly selected so that the true regression function lies in the associated reproducing kernel Hilbert space. However, this is usually, if not always, impractical because kernels that can only be selected via prior knowledge or a tuning process are hardly perfect. Instead it is more common that the kernel is good enough but imperfect in the sense that the true regression can be well approximated by but does not lie exactly in the kernel space. We show distributed kernel regression can still achieves capacity independent optimal rate in this case. To this end, we first establish a general framework that allows to analyze distributed regression with response weighted base algorithms by bounding the error of such algorithms on a single data set, provided that the error bounds has factored the impact of the unexplained variance of the response variable. Then we perform a leave one out analysis of the kernel ridge regression and bias corrected kernel ridge regression, which in combination with the aforementioned framework allows us to derive sharp error bounds and capacity independent optimal rates for the associated distributed kernel regression algorithms. As a byproduct of the thorough analysis, we also prove the kernel ridge regression can achieve rates faster than $N^{-1}$ (where $N$ is the sample size) in the noise free setting which, to our best knowledge, are first observed and novel in regression learning.
Hierarchical Qualitative Clustering: clustering mixed datasets with critical qualitative information
Authors: Diogo Seca, João Mendes-Moreira, Tiago Mendes-Neves, Ricardo Sousa
Date: 2020-06-30 00:00:00
Tasks: Clustering

Clustering can be used to extract insights from data or to verify some of the assumptions held by the domain experts, namely data segmentation. In the literature, few methods can be applied in clustering qualitative values using the context associated with other variables present in the data, without losing interpretability. Moreover, the metrics for calculating dissimilarity between qualitative values often scale poorly for high dimensional mixed datasets. In this study, we propose a novel method for clustering qualitative values, based on Hierarchical Clustering (HQC), and using Maximum Mean Discrepancy. HQC maintains the original interpretability of the qualitative information present in the dataset. We apply HQC to two datasets. Using a mixed dataset provided by Spotify, we showcase how our method can be used for clustering music artists based on the quantitative features of thousands of songs. In addition, using financial features of companies, we cluster company industries, and discuss the implications in investment portfolios diversification.
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Authors: Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
Date: 2020-06-30 00:00:00
Proceeding: ICLR 2021 1
Tasks: Machine Translation, Mixture-of-Experts, Playing the Game of 2048, Translation

Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computation cost, ease of programming, and efficient implementation on parallel devices. GShard is a module composed of a set of lightweight annotation APIs and an extension to the XLA compiler. It provides an elegant way to express a wide range of parallel computation patterns with minimal changes to the existing model code. GShard enabled us to scale up multilingual neural machine translation Transformer model with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters using automatic sharding. We demonstrate that such a giant model can efficiently be trained on 2048 TPU v3 accelerators in 4 days to achieve far superior quality for translation from 100 languages to English compared to the prior art.
Associations between finger tapping, gait and fall risk with application to fall risk assessment
Authors: Jian Ma
Date: 2020-06-30 00:00:00

As the world ages, elderly care becomes a big concern of the society. To address the elderly's issues on dementia and fall risk, we have investigated smart cognitive and fall risk assessment with machine learning methodology based on the data collected from finger tapping test and Timed Up and Go (TUG) test. Meanwhile, we have discovered the associations between cognition and finger motion from finger tapping data and the association between fall risk and gait characteristics from TUG data. In this paper, we jointly analyze the finger tapping and gait characteristics data with copula entropy. We find that the associations between certain finger tapping characteristics ('number of taps', 'average interval of tapping', 'frequency of tapping' of both hands of bimanual inphase and those of left hand of bimanual untiphase) and TUG score are relatively high. According to this finding, we propose to utilize this associations to improve the predictive models of automatic fall risk assessment we developed previously. Experimental results show that using the characteristics of both finger tapping and gait as inputs of the predictive models of predicting TUG score can considerably improve the prediction performance in terms of MAE compared with using only one type of characteristics.
Training highly effective connectivities within neural networks with randomly initialized, fixed weights
Authors: Cristian Ivan, Razvan Florian
Date: 2020-06-30 00:00:00
Tasks: Image Classification

We present some novel, straightforward methods for training the connection graph of a randomly initialized neural network without training the weights. These methods do not use hyperparameters defining cutoff thresholds and therefore remove the need for iteratively searching optimal values of such hyperparameters. We can achieve similar or higher performances than in the case of training all weights, with a similar computational cost as for standard training techniques. Besides switching connections on and off, we introduce a novel way of training a network by flipping the signs of the weights. If we try to minimize the number of changed connections, by changing less than 10% of the total it is already possible to reach more than 90% of the accuracy achieved by standard training. We obtain good results even with weights of constant magnitude or even when weights are drawn from highly asymmetric distributions. These results shed light on the over-parameterization of neural networks and on how they may be reduced to their effective size.