pwc-archive/papers-with-abstracts huggingface dataset:
U-vectors: Generating clusterable speaker embedding from unlabeled data
Authors: M. F. Mridha, Abu Quwsar Ohi, Muhammad Mostafa Monowar, Md. Abdul Hamid, Md. Rashedul Islam, Yutaka Watanobe
Date: 2021-02-07 00:00:00
Tasks: Domain Adaptation, Speaker Recognition

Speaker recognition deals with recognizing speakers by their speech. Most speaker recognition systems are built upon two stages, the first stage extracts low dimensional correlation embeddings from speech, and the second performs the classification task. The robustness of a speaker recognition system mainly depends on the extraction process of speech embeddings, which are primarily pre-trained on a large-scale dataset. As the embedding systems are pre-trained, the performance of speaker recognition models greatly depends on domain adaptation policy, which may reduce if trained using inadequate data. This paper introduces a speaker recognition strategy dealing with unlabeled data, which generates clusterable embedding vectors from small fixed-size speech frames. The unsupervised training strategy involves an assumption that a small speech segment should include a single speaker. Depending on such a belief, a pairwise constraint is constructed with noise augmentation policies, used to train AutoEmbedder architecture that generates speaker embeddings. Without relying on domain adaption policy, the process unsupervisely produces clusterable speaker embeddings, termed unsupervised vectors (u-vectors). The evaluation is concluded in two popular speaker recognition datasets for English language, TIMIT, and LibriSpeech. Also, a Bengali dataset is included to illustrate the diversity of the domain shifts for speaker recognition systems. Finally, we conclude that the proposed approach achieves satisfactory performance using pairwise architectures.
Model-Augmented Q-learning
Authors: Youngmin Oh, Jinwoo Shin, Eunho Yang, Sung Ju Hwang
Date: 2021-02-07 00:00:00
Tasks: model, Q-Learning

In recent years, $Q$-learning has become indispensable for model-free reinforcement learning (MFRL). However, it suffers from well-known problems such as under- and overestimation bias of the value, which may adversely affect the policy learning. To resolve this issue, we propose a MFRL framework that is augmented with the components of model-based RL. Specifically, we propose to estimate not only the $Q$-values but also both the transition and the reward with a shared network. We further utilize the estimated reward from the model estimators for $Q$-learning, which promotes interaction between the estimators. We show that the proposed scheme, called Model-augmented $Q$-learning (MQL), obtains a policy-invariant solution which is identical to the solution obtained by learning with true reward. Finally, we also provide a trick to prioritize past experiences in the replay buffer by utilizing model-estimation errors. We experimentally validate MQL built upon state-of-the-art off-policy MFRL methods, and show that MQL largely improves their performance and convergence. The proposed scheme is simple to implement and does not require additional training cost.
Towards a mathematical framework to inform Neural Network modelling via Polynomial Regression
Authors: Pablo Morala, Jenny Alexandra Cifuentes, Rosa E. Lillo, Iñaki Ucar
Date: 2021-02-07 00:00:00
Tasks: regression

Even when neural networks are widely used in a large number of applications, they are still considered as black boxes and present some difficulties for dimensioning or evaluating their prediction error. This has led to an increasing interest in the overlapping area between neural networks and more traditional statistical methods, which can help overcome those problems. In this article, a mathematical framework relating neural networks and polynomial regression is explored by building an explicit expression for the coefficients of a polynomial regression from the weights of a given neural network, using a Taylor expansion approach. This is achieved for single hidden layer neural networks in regression problems. The validity of the proposed method depends on different factors like the distribution of the synaptic potentials or the chosen activation function. The performance of this method is empirically tested via simulation of synthetic data generated from polynomials to train neural networks with different structures and hyperparameters, showing that almost identical predictions can be obtained when certain conditions are met. Lastly, when learning from polynomial generated data, the proposed method produces polynomials that approximate correctly the data locally.
Damage detection using in-domain and cross-domain transfer learning
Authors: Zaharah A. Bukhsh, Nils Jansen, Aaqib Saeed
Date: 2021-02-07 00:00:00
Tasks: Structural Health Monitoring, Transfer Learning

We investigate the capabilities of transfer learning in the area of structural health monitoring. In particular, we are interested in damage detection for concrete structures. Typical image datasets for such problems are relatively small, calling for the transfer of learned representation from a related large-scale dataset. Past efforts of damage detection using images have mainly considered cross-domain transfer learning approaches using pre-trained IMAGENET models that are subsequently fine-tuned for the target task. However, there are rising concerns about the generalizability of IMAGENET representations for specific target domains, such as for visual inspection and medical imaging. We, therefore, evaluate a combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges. We perform comprehensive comparisons to study the impact of cross-domain and in-domain transfer, with various initialization strategies, using six publicly available visual inspection datasets. The pre-trained models are also evaluated for their ability to cope with the extremely low-data regime. We show that the combination of cross-domain and in-domain transfer persistently shows superior performance specially with tiny datasets. Likewise, we also provide visual explanations of predictive models to enable algorithmic transparency and provide insights to experts about the intrinsic decision logic of typically black-box deep models.
Estimate The Efficiency Of Multiprocessor's Cash Memory Work Algorithms
Authors: Mohamed A. Hamada, Abdelrahman Abdallah
Date: 2021-02-07 00:00:00

Many computer systems for calculating the proper organization of memory are among the most critical issues. Using a tier cache memory (along with branching prediction) is an effective means of increasing modern multi-core processors' performance. Designing high-performance processors is a complex task and requires preliminary verification and analysis of the model level, usually used in analytical and simulation modeling. The refinement of extreme programming is an unfortunate challenge. Few experts disagree with the synthesis of access points. This article demonstrates that Internet QoS and 16-bit architectures are always incompatible, but it's the same situation for write-back caches. The solution to this problem can be implemented by analyzing simulation models of different complexity in combination with the analytical evaluation of individual algorithms. This work is devoted to designing a multi-parameter simulation model of a multi-process for evaluating the performance of cache memory algorithms and the optimality of the structure. Optimization of the structures and algorithms of the cache memory allows you to accelerate the interaction of the memory process and improve the performance of the entire system.
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
Authors: Zekun Li, Wei Zhao, Feng Shi, Lei Qi, Xingzhi Xie, Ying WEI, Zhongxiang Ding, Yang Gao, Shangjie Wu, Jun Liu, Yinghuan Shi, Dinggang Shen
Date: 2021-02-07 00:00:00
Tasks: COVID-19 Diagnosis, Data Augmentation, Multiple Instance Learning, Self-Supervised Learning, Specificity

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues -- weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works.
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks
Authors: Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar
Date: 2021-02-07 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Generalization Bounds, Meta-Learning

In this paper, we study the generalization properties of Model-Agnostic Meta-Learning (MAML) algorithms for supervised learning problems. We focus on the setting in which we train the MAML model over $m$ tasks, each with $n$ data points, and characterize its generalization error from two points of view: First, we assume the new task at test time is one of the training tasks, and we show that, for strongly convex objective functions, the expected excess population loss is bounded by ${\mathcal{O}}(1/mn)$. Second, we consider the MAML algorithm's generalization to an unseen task and show that the resulting generalization error depends on the total variation distance between the underlying distributions of the new task and the tasks observed during the training process. Our proof techniques rely on the connections between algorithmic stability and generalization bounds of algorithms. In particular, we propose a new definition of stability for meta-learning algorithms, which allows us to capture the role of both the number of tasks $m$ and number of samples per task $n$ on the generalization error of MAML.
Graph Neural Network to Dilute Outliers for Refactoring Monolith Application
Authors: Utkarsh Desai, Sambaran Bandyopadhyay, Srikanth Tamilselvam
Date: 2021-02-07 00:00:00
Tasks: Clustering, Graph Neural Network

Microservices are becoming the defacto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can happen independently. It also provides natural benefits when deployed on the cloud since resources can be allocated dynamically to necessary components based on demand. Therefore, enterprises as part of their journey to cloud, are increasingly looking to refactor their monolith application into one or more candidate microservices; wherein each service contains a group of software entities (e.g., classes) that are responsible for a common functionality. Graphs are a natural choice to represent a software system. Each software entity can be represented as nodes and its dependencies with other entities as links. Therefore, this problem of refactoring can be viewed as a graph based clustering task. In this work, we propose a novel method to adapt the recent advancements in graph neural networks in the context of code to better understand the software and apply them in the clustering task. In that process, we also identify the outliers in the graph which can be directly mapped to top refactor candidates in the software. Our solution is able to improve state-of-the-art performance compared to works from both software engineering and existing graph representation based techniques.
Effective and Scalable Clustering on Massive Attributed Graphs
Authors: Renchi Yang, Jieming Shi, Yin Yang, Keke Huang, Shiqi Zhang, Xiaokui Xiao
Date: 2021-02-07 00:00:00
Tasks: Attribute, Clustering, CPU, Graph Clustering

Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes in G into k disjoint clusters, such that nodes within the same cluster share similar topological and attribute characteristics, while those in different clusters are dissimilar. This problem is challenging on massive graphs, e.g., with millions of nodes and billions of edges. For such graphs, existing solutions either incur prohibitively high costs, or produce clustering results with compromised quality. In this paper, we propose ACMin, an effective approach to k-AGC that yields high-quality clusters with cost linear to the size of the input graph G. The main contributions of ACMin are twofold: (i) a novel formulation of the k-AGC problem based on an attributed multi-hop conductance quality measure custom-made for this problem setting, which effectively captures cluster coherence in terms of both topological proximities and attribute similarities, and (ii) a linear-time optimization solver that obtains high-quality clusters iteratively, based on efficient matrix operations such as orthogonal iterations, an alternative optimization approach, as well as an initialization technique that significantly speeds up the convergence of ACMin in practice. Extensive experiments, comparing 11 competitors on 6 real datasets, demonstrate that ACMin consistently outperforms all competitors in terms of result quality measured against ground-truth labels, while being up to orders of magnitude faster. In particular, on the Microsoft Academic Knowledge Graph dataset with 265.2 million edges and 1.1 billion attribute values, ACMin outputs high-quality results for 5-AGC within 1.68 hours using a single CPU core, while none of the 11 competitors finish within 3 days.
Neural Termination Analysis
Authors: Mirco Giacobbe, Daniel Kroening, Julian Parsert
Date: 2021-02-07 00:00:00

We introduce a novel approach to the automated termination analysis of computer programs: we use neural networks to represent ranking functions. Ranking functions map program states to values that are bounded from below and decrease as a program runs; the existence of a ranking function proves that the program terminates. We train a neural network from sampled execution traces of a program so that the network's output decreases along the traces; then, we use symbolic reasoning to formally verify that it generalises to all possible executions. Upon the affirmative answer we obtain a formal certificate of termination for the program, which we call a neural ranking function. We demonstrate that thanks to the ability of neural networks to represent nonlinear functions our method succeeds over programs that are beyond the reach of state-of-the-art tools. This includes programs that use disjunctions in their loop conditions and programs that include nonlinear expressions.
Bandits for Learning to Explain from Explanations
Authors: Freya Behrens, Stefano Teso, Davide Mottin
Date: 2021-02-07 00:00:00
Tasks: Gaussian Processes, Multi-Armed Bandits

We introduce Explearn, an online algorithm that learns to jointly output predictions and explanations for those predictions. Explearn leverages Gaussian Processes (GP)-based contextual bandits. This brings two key benefits. First, GPs naturally capture different kinds of explanations and enable the system designer to control how explanations generalize across the space by virtue of choosing a suitable kernel. Second, Explearn builds on recent results in contextual bandits which guarantee convergence with high probability. Our initial experiments hint at the promise of the approach.
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
Authors: Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan, Theerawit Wilaiprasitporn
Date: 2021-02-07 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Metric Learning, Motor Imagery, Multi-Task Learning

Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite great advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72%, and 2.23% on the SMR-BCI, and OpenBMI datasets, respectively. We demonstrate that MIN2Net improves discriminative information in the latent representation. This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without the need for calibration.
Lazy OCO: Online Convex Optimization on a Switching Budget
Authors: Uri Sherman, Tomer Koren
Date: 2021-02-07 00:00:00

We study a variant of online convex optimization where the player is permitted to switch decisions at most $S$ times in expectation throughout $T$ rounds. Similar problems have been addressed in prior work for the discrete decision set setting, and more recently in the continuous setting but only with an adaptive adversary. In this work, we aim to fill the gap and present computationally efficient algorithms in the more prevalent oblivious setting, establishing a regret bound of $O(T/S)$ for general convex losses and $\widetilde O(T/S^2)$ for strongly convex losses. In addition, for stochastic i.i.d.~losses, we present a simple algorithm that performs $\log T$ switches with only a multiplicative $\log T$ factor overhead in its regret in both the general and strongly convex settings. Finally, we complement our algorithms with lower bounds that match our upper bounds in some of the cases we consider.
Dimension Free Generalization Bounds for Non Linear Metric Learning
Authors: Mark Kozdoba, Shie Mannor
Date: 2021-02-07 00:00:00
Tasks: Generalization Bounds, Metric Learning

In this work we study generalization guarantees for the metric learning problem, where the metric is induced by a neural network type embedding of the data. Specifically, we provide uniform generalization bounds for two regimes -- the sparse regime, and a non-sparse regime which we term \emph{bounded amplification}. The sparse regime bounds correspond to situations where $\ell_1$-type norms of the parameters are small. Similarly to the situation in classification, solutions satisfying such bounds can be obtained by an appropriate regularization of the problem. On the other hand, unregularized SGD optimization of a metric learning loss typically does not produce sparse solutions. We show that despite this lack of sparsity, by relying on a different, new property of the solutions, it is still possible to provide dimension free generalization guarantees. Consequently, these bounds can explain generalization in non sparse real experimental situations. We illustrate the studied phenomena on the MNIST and 20newsgroups datasets.
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
Authors: Ofir Nabati, Tom Zahavy, Shie Mannor
Date: 2021-02-07 00:00:00
Tasks: Efficient Exploration, Multi-Armed Bandits, Representation Learning

We study neural-linear bandits for solving problems where {\em both} exploration and representation learning play an important role. Neural-linear bandits harnesses the representation power of Deep Neural Networks (DNNs) and combines it with efficient exploration mechanisms by leveraging uncertainty estimation of the model, designed for linear contextual bandits on top of the last hidden layer. In order to mitigate the problem of representation change during the process, new uncertainty estimations are computed using stored data from an unlimited buffer. Nevertheless, when the amount of stored data is limited, a phenomenon called catastrophic forgetting emerges. To alleviate this, we propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online. We applied our algorithm, Limited Memory Neural-Linear with Likelihood Matching (NeuralLinear-LiM2) on a variety of datasets and observed that our algorithm achieves comparable performance to the unlimited memory approach while exhibits resilience to catastrophic forgetting.
Unsupervised Sentence-embeddings by Manifold Approximation and Projection
Authors: Subhradeep Kayal
Date: 2021-02-07 00:00:00
Proceeding: EACL 2021 2
Tasks: Sentence, Sentence Embeddings, text-classification, Text Classification, Topological Data Analysis

The concept of unsupervised universal sentence encoders has gained traction recently, wherein pre-trained models generate effective task-agnostic fixed-dimensional representations for phrases, sentences and paragraphs. Such methods are of varying complexity, from simple weighted-averages of word vectors to complex language-models based on bidirectional transformers. In this work we propose a novel technique to generate sentence-embeddings in an unsupervised fashion by projecting the sentences onto a fixed-dimensional manifold with the objective of preserving local neighbourhoods in the original space. To delineate such neighbourhoods we experiment with several set-distance metrics, including the recently proposed Word Mover's distance, while the fixed-dimensional projection is achieved by employing a scalable and efficient manifold approximation method rooted in topological data analysis. We test our approach, which we term EMAP or Embeddings by Manifold Approximation and Projection, on six publicly available text-classification datasets of varying size and complexity. Empirical results show that our method consistently performs similar to or better than several alternative state-of-the-art approaches.
A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle
Authors: Yu Han, Shizhan Lu, Haiyan Xu
Date: 2021-02-07 00:00:00
Tasks: Clustering

Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. A novel and fast clustering algorithm, fission clustering algorithm, is proposed in recent year. In this article, we propose a robust fission clustering (RFC) algorithm and a self-adaptive noise identification method. The RFC and the self-adaptive noise identification method are combine to propose a self-adaptive robust fission clustering (SARFC) algorithm. Several frequently-used datasets were applied to test the performance of the proposed clustering approach and to compare the results with those of other algorithms. The comprehensive comparisons indicate that the proposed method has advantages over other common methods.