pwc-archive/papers-with-abstracts huggingface dataset:
Resilience from Diversity: Population-based approach to harden models against adversarial attacks
Authors: Jasser Jasser, Ivan Garibay
Date: 2021-11-19 00:00:00
Tasks: Diversity

Traditional deep learning networks (DNN) exhibit intriguing vulnerabilities that allow an attacker to force them to fail at their task. Notorious attacks such as the Fast Gradient Sign Method (FGSM) and the more powerful Projected Gradient Descent (PGD) generate adversarial samples by adding a magnitude of perturbation $\epsilon$ to the input's computed gradient, resulting in a deterioration of the effectiveness of the model's classification. This work introduces a model that is resilient to adversarial attacks. Our model leverages an established mechanism of defense which utilizes randomness and a population of DNNs. More precisely, our model consists of a population of $n$ diverse submodels, each one of them trained to individually obtain a high accuracy for the task at hand, while forced to maintain meaningful differences in their weights. Each time our model receives a classification query, it selects a submodel from its population at random to answer the query. To counter the attack transferability, diversity is introduced and maintained in the population of submodels. Thus introducing the concept of counter linking weights. A Counter-Linked Model (CLM) consists of a population of DNNs of the same architecture where a periodic random similarity examination is conducted during the simultaneous training to guarantee diversity while maintaining accuracy. Though the randomization technique proved to be resilient against adversarial attacks, we show that by retraining the DNNs ensemble or training them from the start with counter linking would enhance the robustness by around 20\% when tested on the MNIST dataset and at least 15\% when tested on the CIFAR-10 dataset. When CLM is coupled with adversarial training, this defense mechanism achieves state-of-the-art robustness.
Pointer over Attention: An Improved Bangla Text Summarization Approach Using Hybrid Pointer Generator Network
Authors: Nobel Dhar, Gaurob Saha, Prithwiraj Bhattacharjee, Avi Mallick, Md Saiful Islam
Date: 2021-11-19 00:00:00
Tasks: Abstractive Text Summarization, Articles, Text Summarization

Despite the success of the neural sequence-to-sequence model for abstractive text summarization, it has a few shortcomings, such as repeating inaccurate factual details and tending to repeat themselves. We propose a hybrid pointer generator network to solve the shortcomings of reproducing factual details inadequately and phrase repetition. We augment the attention-based sequence-to-sequence using a hybrid pointer generator network that can generate Out-of-Vocabulary words and enhance accuracy in reproducing authentic details and a coverage mechanism that discourages repetition. It produces a reasonable-sized output text that preserves the conceptual integrity and factual information of the input article. For evaluation, we primarily employed "BANSData" - a highly adopted publicly available Bengali dataset. Additionally, we prepared a large-scale dataset called "BANS-133" which consists of 133k Bangla news articles associated with human-generated summaries. Experimenting with the proposed model, we achieved ROUGE-1 and ROUGE-2 scores of 0.66, 0.41 for the "BANSData" dataset and 0.67, 0.42 for the BANS-133k" dataset, respectively. We demonstrated that the proposed system surpasses previous state-of-the-art Bengali abstractive summarization techniques and its stability on a larger dataset. "BANS-133" datasets and code-base will be publicly available for research.
Over-the-Air Federated Learning with Retransmissions (Extended Version)
Authors: Henrik Hellström, Viktoria Fodor, Carlo Fischione
Date: 2021-11-19 00:00:00
Tasks: Federated Learning

Motivated by increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL convergence over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Finally, we propose a heuristic for selecting the optimal number of retransmissions, which can be calculated before training the ML model. Numerical results demonstrate that the introduction of retransmissions can lead to improved ML performance, without incurring extra costs in terms of communication or computation. Additionally, we provide simulation results on our heuristic which indicate that it can correctly identify the optimal number of retransmissions for different wireless network setups and machine learning problems.
Non asymptotic bounds in asynchronous sum-weight gossip protocols
Authors: David Picard, Jérôme Fellus, Stéphane Garnier
Date: 2021-11-19 00:00:00

This paper focuses on non-asymptotic diffusion time in asynchronous gossip protocols. Asynchronous gossip protocols are designed to perform distributed computation in a network of nodes by randomly exchanging messages on the associated graph. To achieve consensus among nodes, a minimal number of messages has to be exchanged. We provides a probabilistic bound to such number for the general case. We provide a explicit formula for fully connected graphs depending only on the number of nodes and an approximation for any graph depending on the spectrum of the graph.
Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari
Authors: Dominik Schmidt, Thomas Schmied
Date: 2021-11-19 00:00:00
Tasks: Atari Games, GPU

Across the Arcade Learning Environment, Rainbow achieves a level of performance competitive with humans and modern RL algorithms. However, attaining this level of performance requires large amounts of data and hardware resources, making research in this area computationally expensive and use in practical applications often infeasible. This paper's contribution is threefold: We (1) propose an improved version of Rainbow, seeking to drastically reduce Rainbow's data, training time, and compute requirements while maintaining its competitive performance; (2) we empirically demonstrate the effectiveness of our approach through experiments on the Arcade Learning Environment, and (3) we conduct a number of ablation studies to investigate the effect of the individual proposed modifications. Our improved version of Rainbow reaches a median human normalized score close to classic Rainbow's, while using 20 times less data and requiring only 7.5 hours of training time on a single GPU. We also provide our full implementation including pre-trained models.
Posterior concentration and fast convergence rates for generalized Bayesian learning
Authors: Lam Si Tung Ho, Binh T. Nguyen, Vu Dinh, Duy Nguyen
Date: 2021-11-19 00:00:00
Tasks: regression

In this paper, we study the learning rate of generalized Bayes estimators in a general setting where the hypothesis class can be uncountable and have an irregular shape, the loss function can have heavy tails, and the optimal hypothesis may not be unique. We prove that under the multi-scale Bernstein's condition, the generalized posterior distribution concentrates around the set of optimal hypotheses and the generalized Bayes estimator can achieve fast learning rate. Our results are applied to show that the standard Bayesian linear regression is robust to heavy-tailed distributions.
Interpreting deep urban sound classification using Layer-wise Relevance Propagation
Authors: Marco Colussi, Stavros Ntalampiras
Date: 2021-11-19 00:00:00
Tasks: Classification, Sound Classification

After constructing a deep neural network for urban sound classification, this work focuses on the sensitive application of assisting drivers suffering from hearing loss. As such, clear etiology justifying and interpreting model predictions comprise a strong requirement. To this end, we used two different representations of audio signals, i.e. Mel and constant-Q spectrograms, while the decisions made by the deep neural network are explained via layer-wise relevance propagation. At the same time, frequency content assigned with high relevance in both feature sets, indicates extremely discriminative information characterizing the present classification task. Overall, we present an explainable AI framework for understanding deep urban sound classification.
Policy Gradient Approach to Compilation of Variational Quantum Circuits
Authors: David A. Herrera-Martí
Date: 2021-11-19 00:00:00

We propose a method for finding approximate compilations of quantum unitary transformations, based on techniques from policy gradient reinforcement learning. The choice of a stochastic policy allows us to rephrase the optimization problem in terms of probability distributions, rather than variational gates. In this framework, the optimal configuration is found by optimizing over distribution parameters, rather than over free angles. We show numerically that this approach can be more competitive than gradient-free methods, for a comparable amount of resources, both for noiseless and noisy circuits. Another interesting feature of this approach to variational compilation is that it does not need a separate register and long-range interactions to estimate the end-point fidelity, which is an improvement over methods which rely on the Hilbert-Schmidt test. We expect these techniques to be relevant for training variational circuits in other contexts.
Towards Traffic Scene Description: The Semantic Scene Graph
Authors: Maximilian Zipfl, J. Marius Zöllner
Date: 2021-11-19 00:00:00

For the classification of traffic scenes, a description model is necessary that can describe the scene in a uniform way, independent of its domain. A model to describe a traffic scene in a semantic way is described in this paper. The description model allows to describe a traffic scene independently of the road geometry and road topology. Here, the traffic participants are projected onto the road network and represented as nodes in a graph. Depending on the relative location between two traffic participants with respect to the road topology, semantically classified edges are created between the corresponding nodes. For concretization, the edge attributes are extended by relative distances and velocities between both traffic participants with regard to the course of the lane. An important aspect of the description is that it can be converted easily into a machine-readable format. The current description focuses on dynamic objects of a traffic scene and considers traffic participants, such as pedestrians or vehicles.
An Expectation-Maximization Perspective on Federated Learning
Authors: Christos Louizos, Matthias Reisser, Joseph Soriaga, Max Welling
Date: 2021-11-19 00:00:00
Tasks: Federated Learning

Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting. This perspective on FedAvg unifies several recent works in the field and opens up the possibility for extensions through different choices for the hierarchical model. Based on this view, we further propose a variant of the hierarchical model that employs prior distributions to promote sparsity. By similarly using the hard-EM algorithm for learning, we obtain FedSparse, a procedure that can learn sparse neural networks in the federated learning setting. FedSparse reduces communication costs from client to server and vice-versa, as well as the computational costs for inference with the sparsified network - both of which are of great practical importance in federated learning.
Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulations
Authors: Piotr Białas, Piotr Korcyl, Tomasz Stebel
Date: 2021-11-19 00:00:00

We provide a deepened study of autocorrelations in Neural Markov Chain Monte Carlo (NMCMC) simulations, a version of the traditional Metropolis algorithm which employs neural networks to provide independent proposals. We illustrate our ideas using the two-dimensional Ising model. We discuss several estimates of autocorrelation times in the context of NMCMC, some inspired by analytical results derived for the Metropolized Independent Sampler (MIS). We check their reliability by estimating them on a small system where analytical results can also be obtained. Based on the analytical results for MIS we propose a new loss function and study its impact on the autocorelation times. Although, this function's performance is a bit inferior to the traditional Kullback-Leibler divergence, it offers two training algorithms which in some situations may be beneficial. By studying a small, $4 \times 4$, system we gain access to the dynamics of the training process which we visualize using several observables. Furthermore, we quantitatively investigate the impact of imposing global discrete symmetries of the system in the neural network training process on the autocorrelation times. Eventually, we propose a scheme which incorporates partial heat-bath updates which considerably improves the quality of the training. The impact of the above enhancements is discussed for a $16 \times 16$ spin system. The summary of our findings may serve as a guidance to the implementation of Neural Markov Chain Monte Carlo simulations for more complicated models.
Computing Graph Edit Distance with Algorithms on Quantum Devices
Authors: Massimiliano Incudini, Fabio Tarocco, Riccardo Mengoni, Alessandra Di Pierro, Antonio Mandarino
Date: 2021-11-19 00:00:00

Distance measures provide the foundation for many popular algorithms in Machine Learning and Pattern Recognition. Different notions of distance can be used depending on the types of the data the algorithm is working on. For graph-shaped data, an important notion is the Graph Edit Distance (GED) that measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical. As the complexity of computing GED is the same as NP-hard problems, it is reasonable to consider approximate solutions. In this paper we present a QUBO formulation of the GED problem. This allows us to implement two different approaches, namely quantum annealing and variational quantum algorithms that run on the two types of quantum hardware currently available: quantum annealer and gate-based quantum computer, respectively. Considering the current state of noisy intermediate-scale quantum computers, we base our study on proof-of-principle tests of their performance.
Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification
Authors: Cangxiong Chen, Neill D. F. Campbell
Date: 2021-11-19 00:00:00
Tasks: Attribute, Federated Learning, image-classification, Image Classification

Federated learning of deep learning models for supervised tasks, e.g. image classification and segmentation, has found many applications: for example in human-in-the-loop tasks such as film post-production where it enables sharing of domain expertise of human artists in an efficient and effective fashion. In many such applications, we need to protect the training data from being leaked when gradients are shared in the training process due to IP or privacy concerns. Recent works have demonstrated that it is possible to reconstruct the training data from gradients for an image-classification model when its architecture is known. However, there is still an incomplete theoretical understanding of the efficacy and failure of such attacks. In this paper, we analyse the source of training-data leakage from gradients. We formulate the problem of training data reconstruction as solving an optimisation problem iteratively for each layer. The layer-wise objective function is primarily defined by weights and gradients from the current layer as well as the output from the reconstruction of the subsequent layer, but it might also involve a 'pull-back' constraint from the preceding layer. Training data can be reconstructed when we solve the problem backward from the output of the network through each layer. Based on this formulation, we are able to attribute the potential leakage of the training data in a deep network to its architecture. We also propose a metric to measure the level of security of a deep learning model against gradient-based attacks on the training data.
Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesis
Authors: Alexandra Vioni, Myrsini Christidou, Nikolaos Ellinas, Georgios Vamvoukakis, Panos Kakoulidis, TaeHoon Kim, June Sig Sung, Hyoungmin Park, Aimilios Chalamandaris, Pirros Tsiakoulis
Date: 2021-11-19 00:00:00
Tasks: Clustering, Decoder, Speech Synthesis, text-to-speech, Text to Speech

This paper presents a method for controlling the prosody at the phoneme level in an autoregressive attention-based text-to-speech system. Instead of learning latent prosodic features with a variational framework as is commonly done, we directly extract phoneme-level F0 and duration features from the speech data in the training set. Each prosodic feature is discretized using unsupervised clustering in order to produce a sequence of prosodic labels for each utterance. This sequence is used in parallel to the phoneme sequence in order to condition the decoder with the utilization of a prosodic encoder and a corresponding attention module. Experimental results show that the proposed method retains the high quality of generated speech, while allowing phoneme-level control of F0 and duration. By replacing the F0 cluster centroids with musical notes, the model can also provide control over the note and octave within the range of the speaker.
Word-Level Style Control for Expressive, Non-attentive Speech Synthesis
Authors: Konstantinos Klapsas, Nikolaos Ellinas, June Sig Sung, Hyoungmin Park, Spyros Raptis
Date: 2021-11-19 00:00:00
Tasks: Expressive Speech Synthesis, Speech Synthesis

This paper presents an expressive speech synthesis architecture for modeling and controlling the speaking style at a word level. It attempts to learn word-level stylistic and prosodic representations of the speech data, with the aid of two encoders. The first one models style by finding a combination of style tokens for each word given the acoustic features, and the second outputs a word-level sequence conditioned only on the phonetic information in order to disentangle it from the style information. The two encoder outputs are aligned and concatenated with the phoneme encoder outputs and then decoded with a Non-Attentive Tacotron model. An extra prior encoder is used to predict the style tokens autoregressively, in order for the model to be able to run without a reference utterance. We find that the resulting model gives both word-level and global control over the style, as well as prosody transfer capabilities.
Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control
Authors: Myrsini Christidou, Alexandra Vioni, Nikolaos Ellinas, Georgios Vamvoukakis, Konstantinos Markopoulos, Panos Kakoulidis, June Sig Sung, Hyoungmin Park, Aimilios Chalamandaris, Pirros Tsiakoulis
Date: 2021-11-19 00:00:00
Tasks: Clustering, Data Augmentation, text-to-speech, Text to Speech

This paper presents a method for phoneme-level prosody control of F0 and duration on a multispeaker text-to-speech setup, which is based on prosodic clustering. An autoregressive attention-based model is used, incorporating multispeaker architecture modules in parallel to a prosody encoder. Several improvements over the basic single-speaker method are proposed that increase the prosodic control range and coverage. More specifically we employ data augmentation, F0 normalization, balanced clustering for duration, and speaker-independent prosodic clustering. These modifications enable fine-grained phoneme-level prosody control for all speakers contained in the training set, while maintaining the speaker identity. The model is also fine-tuned to unseen speakers with limited amounts of data and it is shown to maintain its prosody control capabilities, verifying that the speaker-independent prosodic clustering is effective. Experimental results verify that the model maintains high output speech quality and that the proposed method allows efficient prosody control within each speaker's range despite the variability that a multispeaker setting introduces.
Learning in High-Dimensional Feature Spaces Using ANOVA-Based Fast Matrix-Vector Multiplication
Authors: Franziska Nestler, Martin Stoll, Theresa Wagner
Date: 2021-11-19 00:00:00
Tasks: regression

Kernel matrices are crucial in many learning tasks such as support vector machines or kernel ridge regression. The kernel matrix is typically dense and large-scale. Depending on the dimension of the feature space even the computation of all of its entries in reasonable time becomes a challenging task. For such dense matrices the cost of a matrix-vector product scales quadratically with the dimensionality N , if no customized methods are applied. We propose the use of an ANOVA kernel, where we construct several kernels based on lower-dimensional feature spaces for which we provide fast algorithms realizing the matrix-vector products. We employ the non-equispaced fast Fourier transform (NFFT), which is of linear complexity for fixed accuracy. Based on a feature grouping approach, we then show how the fast matrix-vector products can be embedded into a learning method choosing kernel ridge regression and the conjugate gradient solver. We illustrate the performance of our approach on several data sets.
Data imputation and comparison of custom ensemble models with existing libraries like XGBoost, Scikit learn, etc. for Predictive Equipment failure
Authors: Tejas Y. Deo
Date: 2021-11-19 00:00:00
Tasks: Imputation, Missing Values

This paper presents comparison of custom ensemble models with the models trained using existing libraries Like Xgboost, Scikit Learn, etc. in case of predictive equipment failure for the case of oil extracting equipment setup. The dataset that is used contains many missing values and the paper proposes different model-based data imputation strategies to impute the missing values. The architecture and the training and testing process of the custom ensemble models are explained in detail.