pwc-archive/papers-with-abstracts huggingface dataset:
A scalable and fast artificial neural network syndrome decoder for surface codes
Authors: Spiro Gicev, Lloyd C. L. Hollenberg, Muhammad Usman
Date: 2021-10-12 00:00:00
Tasks: Decoder

Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing. When operated as stabilizer codes, surface code computations consist of a syndrome decoding step where measured stabilizer operators are used to determine appropriate corrections for errors in physical qubits. Decoding algorithms have undergone substantial development, with recent work incorporating machine learning (ML) techniques. Despite promising initial results, the ML-based syndrome decoders are still limited to small scale demonstrations with low latency and are incapable of handling surface codes with boundary conditions and various shapes needed for lattice surgery and braiding. Here, we report the development of an artificial neural network (ANN) based scalable and fast syndrome decoder capable of decoding surface codes of arbitrary shape and size with data qubits suffering from the depolarizing error model. Based on rigorous training over 50 million random quantum error instances, our ANN decoder is shown to work with code distances exceeding 1000 (more than 4 million physical qubits), which is the largest ML-based decoder demonstration to-date. The established ANN decoder demonstrates an execution time in principle independent of code distance, implying that its implementation on dedicated hardware could potentially offer surface code decoding times of O($\mu$sec), commensurate with the experimentally realisable qubit coherence times. With the anticipated scale-up of quantum processors within the next decade, their augmentation with a fast and scalable syndrome decoder such as developed in our work is expected to play a decisive role towards experimental implementation of fault-tolerant quantum information processing.
On the Self-Penalization Phenomenon in Feature Selection
Authors: Michael I. Jordan, Keli Liu, Feng Ruan
Date: 2021-10-12 00:00:00
Tasks: feature selection

We describe an implicit sparsity-inducing mechanism based on minimization over a family of kernels: \begin{equation*} \min_{\beta, f}~\widehat{\mathbb{E}}[L(Y, f(\beta^{1/q} \odot X)] + \lambda_n \|f\|_{\mathcal{H}_q}^2~~\text{subject to}~~\beta \ge 0, \end{equation*} where $L$ is the loss, $\odot$ is coordinate-wise multiplication and $\mathcal{H}_q$ is the reproducing kernel Hilbert space based on the kernel $k_q(x, x') = h(\|x-x'\|_q^q)$, where $\|\cdot\|_q$ is the $\ell_q$ norm. Using gradient descent to optimize this objective with respect to $\beta$ leads to exactly sparse stationary points with high probability. The sparsity is achieved without using any of the well-known explicit sparsification techniques such as penalization (e.g., $\ell_1$), early stopping or post-processing (e.g., clipping). As an application, we use this sparsity-inducing mechanism to build algorithms consistent for feature selection.
Sharing FANCI Features: A Privacy Analysis of Feature Extraction for DGA Detection
Authors: Benedikt Holmes, Arthur Drichel, Ulrike Meyer
Date: 2021-10-12 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

The goal of Domain Generation Algorithm (DGA) detection is to recognize infections with bot malware and is often done with help of Machine Learning approaches that classify non-resolving Domain Name System (DNS) traffic and are trained on possibly sensitive data. In parallel, the rise of privacy research in the Machine Learning world leads to privacy-preserving measures that are tightly coupled with a deep learning model's architecture or training routine, while non deep learning approaches are commonly better suited for the application of privacy-enhancing methods outside the actual classification module. In this work, we aim to measure the privacy capability of the feature extractor of feature-based DGA detector FANCI (Feature-based Automated Nxdomain Classification and Intelligence). Our goal is to assess whether a data-rich adversary can learn an inverse mapping of FANCI's feature extractor and thereby reconstruct domain names from feature vectors. Attack success would pose a privacy threat to sharing FANCI's feature representation, while the opposite would enable this representation to be shared without privacy concerns. Using three real-world data sets, we train a recurrent Machine Learning model on the reconstruction task. Our approaches result in poor reconstruction performance and we attempt to back our findings with a mathematical review of the feature extraction process. We thus reckon that sharing FANCI's feature representation does not constitute a considerable privacy leakage.
Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes
Authors: M. Arana-Catania, Rob Procter, Yulan He, Maria Liakata
Date: 2021-10-12 00:00:00
Proceeding: EMNLP (newsum) 2021 11
Tasks: Articles, Machine Translation, Translation

We present work on summarising deliberative processes for non-English languages. Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text. We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model. Texts are translated into English, summarised, and translated back to the original language. We obtain promising results regarding the fluency, consistency and relevance of the summaries produced. Our approach is easy to implement for many languages for production purposes by simply changing the translation model.
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations
Authors: Yingshi Chen, Xinli Song, HanYang Dai, Tao Liu, Wuzhi Zhong, Guoyang Wu
Date: 2021-10-12 00:00:00
Tasks: Q-Learning, Scheduling

We present a fast block direct solver for the unified dynamic simulations of power systems. This solver uses a novel Q-learning based method for task scheduling. Unified dynamic simulations of power systems represent a method in which the electric-mechanical transient, medium-term and long-term dynamic phenomena are organically united. Due to the high rank and large numbers in solving, fast solution of these equations is the key to speeding up the simulation. The sparse systems of simulation contain complex nested block structure, which could be used by the solver to speed up. For the scheduling of blocks and frontals in the solver, we use a learning based task-tree scheduling technique in the framework of Markov Decision Process. That is, we could learn optimal scheduling strategies by offline training on many sample matrices. Then for any systems, the solver would get optimal task partition and scheduling on the learned model. Our learning-based algorithm could help improve the performance of sparse solver, which has been verified in some numerical experiments. The simulation on some large power systems shows that our solver is 2-6 times faster than KLU, which is the state-of-the-art sparse solver for circuit simulation problems.
Across-Task Neural Architecture Search via Meta Learning
Authors: Jingtao Rong, Xinyi Yu, Mingyang Zhang, Linlin Ou
Date: 2021-10-12 00:00:00
Tasks: Evolutionary Algorithms, GPU, Meta-Learning, Neural Architecture Search

Adequate labeled data and expensive compute resources are the prerequisites for the success of neural architecture search(NAS). It is challenging to apply NAS in meta-learning scenarios with limited compute resources and data. In this paper, an across-task neural architecture search (AT-NAS) is proposed to address the problem through combining gradient-based meta-learning with EA-based NAS to learn over the distribution of tasks. The supernet is learned over an entire set of tasks by meta-learning its weights. Architecture encodes of subnets sampled from the supernet are iteratively adapted by evolutionary algorithms while simultaneously searching for a task-sensitive meta-network. Searched meta-network can be adapted to a novel task via a few learning steps and only costs a little search time. Empirical results show that AT-NAS surpasses the related approaches on few-shot classification accuracy. The performance of AT-NAS on classification benchmarks is comparable to that of models searched from scratch, by adapting the architecture in less than an hour from a 5-GPU-day pretrained meta-network.
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling
Authors: Ling Zhan, Tao Jia
Date: 2021-10-12 00:00:00
Tasks: Network Embedding

Heterogeneous information network (HIN) embedding aims to find the representations of nodes that preserve the proximity between entities of different nature. A family of approaches that are wildly adopted applies random walk to generate a sequence of heterogeneous context, from which the embedding is learned. However, due to the multipartite graph structure of HIN, hub nodes tend to be over-represented in the sampled sequence, giving rise to imbalanced samples of the network. Here we propose a new embedding method CoarSAS2hvec. The self-avoid short sequence sampling with the HIN coarsening procedure (CoarSAS) is utilized to better collect the rich information in HIN. An optimized loss function is used to improve the performance of the HIN structure embedding. CoarSAS2hvec outperforms nine other methods in two different tasks on four real-world data sets. The ablation study confirms that the samples collected by CoarSAS contain richer information of the network compared with those by other methods, which is characterized by a higher information entropy. Hence, the traditional loss function applied to samples by CoarSAS can also yield improved results. Our work addresses a limitation of the random-walk-based HIN embedding that has not been emphasized before, which can shed light on a range of problems in HIN analyses.
Couple Learning for semi-supervised sound event detection
Authors: Rui Tao, Long Yan, Kazushige Ouchi, Xiangdong Wang
Date: 2021-10-12 00:00:00
Tasks: Event Detection, Sound Event Detection

The recently proposed Mean Teacher method, which exploits large-scale unlabeled data in a self-ensembling manner, has achieved state-of-the-art results in several semi-supervised learning benchmarks. Spurred by current achievements, this paper proposes an effective Couple Learning method that combines a well-trained model and a Mean Teacher model. The suggested pseudo-labels generated model (PLG) increases strongly- and weakly-labeled data to improve the Mean Teacher method-s performance. Moreover, the Mean Teacher-s consistency cost reduces the noise impact in the pseudo-labels introduced by detection errors. The experimental results on Task 4 of the DCASE2020 challenge demonstrate the superiority of the proposed method, achieving about 44.25% F1-score on the public evaluation set, significantly outperforming the baseline system-s 32.39%. At the same time, we also propose a simple and effective experiment called the Variable Order Input (VOI) experiment, which proves the significance of the Couple Learning method. Our developed Couple Learning code is available on GitHub.
Optimizing Ranking Systems Online as Bandits
Authors: Chang Li
Date: 2021-10-12 00:00:00
Tasks: Learning-To-Rank, Online Ranker Evaluation, Recommendation Systems, Retrieval

Ranking system is the core part of modern retrieval and recommender systems, where the goal is to rank candidate items given user contexts. Optimizing ranking systems online means that the deployed system can serve user requests, e.g., queries in the web search, and optimize the ranking policy by learning from user interactions, e.g., clicks. Bandit is a general online learning framework and can be used in our optimization task. However, due to the unique features of ranking, there are several challenges in designing bandit algorithms for ranking system optimization. In this dissertation, we study and propose solutions for four challenges in optimizing ranking systems online: effectiveness, safety, nonstationarity, and diversification. First, the effectiveness is related to how fast the algorithm learns from interactions. We study the effective online ranker evaluation task and propose the MergeDTS algorithm to solve the problem effectively. Second, the deployed algorithm should be safe, which means the algorithm only displays reasonable content to user requests. To solve the safe online learning to rank problem, we propose the BubbleRank algorithm. Third, as users change their preferences constantly, the algorithm should handle the nonstationarity. We formulate this nonstationary online learning to rank problem as cascade non-stationary bandits and propose CascadeDUCB and CascadeSWUCB algorithms to solve the problem. Finally, the contents in ranked lists should be diverse. We consider the results diversification task and propose the CascadeHybird algorithm that considers both the item relevance and results diversification when learning from user interactions.
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform
Authors: Zhen Xu, Sergio Escalera, Isabelle Guyon, Adrien Pavão, Magali Richard, Wei-Wei Tu, Quanming Yao, Huan Zhao
Date: 2021-10-12 00:00:00
Tasks: Benchmarking

Obtaining standardized crowdsourced benchmark of computational methods is a major issue in data science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here we introduce Codabench, an open-source, community-driven platform for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench (https://www.codabench.org/) is open to everyone, free of charge, and allows benchmark organizers to compare fairly submissions, under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating the organization of benchmarks flexibly, easily and reproducibly, such as the possibility of re-using templates of benchmarks, and supplying compute resources on-demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2500 submissions. As illustrative use cases, we introduce 4 diverse benchmarks covering Graph Machine Learning, Cancer Heterogeneity, Clinical Diagnosis and Reinforcement Learning.
Information Theoretic Structured Generative Modeling
Authors: Bo Hu, Shujian Yu, Jose C. Principe
Date: 2021-10-12 00:00:00
Tasks: Density Estimation

R\'enyi's information provides a theoretical foundation for tractable and data-efficient non-parametric density estimation, based on pair-wise evaluations in a reproducing kernel Hilbert space (RKHS). This paper extends this framework to parametric probabilistic modeling, motivated by the fact that R\'enyi's information can be estimated in closed-form for Gaussian mixtures. Based on this special connection, a novel generative model framework called the structured generative model (SGM) is proposed that makes straightforward optimization possible, because costs are scale-invariant, avoiding high gradient variance while imposing less restrictions on absolute continuity, which is a huge advantage in parametric information theoretic optimization. The implementation employs a single neural network driven by an orthonormal input appended to a single white noise source adapted to learn an infinite Gaussian mixture model (IMoG), which provides an empirically tractable model distribution in low dimensions. To train SGM, we provide three novel variational cost functions, based on R\'enyi's second-order entropy and divergence, to implement minimization of cross-entropy, minimization of variational representations of $f$-divergence, and maximization of the evidence lower bound (conditional probability). We test the framework for estimation of mutual information and compare the results with the mutual information neural estimation (MINE), for density estimation, for conditional probability estimation in Markov models as well as for training adversarial networks. Our preliminary results show that SGM significantly improves MINE estimation in terms of data efficiency and variance, conventional and variational Gaussian mixture models, as well as the performance of generative adversarial networks.
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications
Authors: Juan Zuluaga-Gomez, Seyyed Saeed Sarfjoo, Amrutha Prasad, Iuliia Nigmatulina, Petr Motlicek, Karel Ondrej, Oliver Ohneiser, Hartmut Helmke
Date: 2021-10-12 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Change Detection, Chunking, speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) allows transcribing the communications between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions are used later to extract ATC named entities, e.g., aircraft callsigns. One common challenge is speech activity detection (SAD) and speaker diarization (SD). In the failure condition, two or more segments remain in the same recording, jeopardizing the overall performance. We propose a system that combines SAD and a BERT model to perform speaker change detection and speaker role detection (SRD) by chunking ASR transcripts, i.e., SD with a defined number of speakers together with SRD. The proposed model is evaluated on real-life public ATC databases. Our BERT SD model baseline reaches up to 10% and 20% token-based Jaccard error rate (JER) in public and private ATC databases. We also achieved relative improvements of 32% and 7.7% in JERs and SD error rate (DER), respectively, compared to VBx, a well-known SD system.
Music Sentiment Transfer
Authors: Miles Sigel, Michael Zhou, Jiebo Luo
Date: 2021-10-12 00:00:00
Tasks: Style Transfer

Music sentiment transfer is a completely novel task. Sentiment transfer is a natural evolution of the heavily-studied style transfer task, as sentiment transfer is rooted in applying the sentiment of a source to be the new sentiment for a target piece of media; yet compared to style transfer, sentiment transfer has been only scantily studied on images. Music sentiment transfer attempts to apply the high level objective of sentiment transfer to the domain of music. We propose CycleGAN to bridge disparate domains. In order to use the network, we choose to use symbolic, MIDI, data as the music format. Through the use of a cycle consistency loss, we are able to create one-to-one mappings that preserve the content and realism of the source data. Results and literature suggest that the task of music sentiment transfer is more difficult than image sentiment transfer because of the temporal characteristics of music and lack of existing datasets.
Deep Federated Learning for Autonomous Driving
Authors: Anh Nguyen, Tuong Do, Minh Tran, Binh X. Nguyen, Chien Duong, Tu Phan, Erman Tjiputra, Quang D. Tran
Date: 2021-10-12 00:00:00
Tasks: Autonomous Driving, Federated Learning

Autonomous driving is an active research topic in both academia and industry. However, most of the existing solutions focus on improving the accuracy by training learnable models with centralized large-scale data. Therefore, these methods do not take into account the user's privacy. In this paper, we present a new approach to learn autonomous driving policy while respecting privacy concerns. We propose a peer-to-peer Deep Federated Learning (DFL) approach to train deep architectures in a fully decentralized manner and remove the need for central orchestration. We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods. Intensively experimental results on three datasets show that our approach with FADNet and DFL achieves superior accuracy compared with other recent methods. Furthermore, our approach can maintain privacy by not collecting user data to a central server.
Predicting the Efficiency of CO$_2$ Sequestering by Metal Organic Frameworks Through Machine Learning Analysis of Structural and Electronic Properties
Authors: Mahati Manda
Date: 2021-10-12 00:00:00

Due the alarming rate of climate change, the implementation of efficient CO$_2$ capture has become crucial. This project aims to create an algorithm that predicts the uptake of CO$_2$ adsorbing Metal-Organic Frameworks (MOFs) by using Machine Learning. These values will in turn gauge the efficiency of these MOFs and provide scientists who are looking to maximize the uptake a way to know whether or not the MOF is worth synthesizing. This algorithm will save resources such as time and equipment as scientists will be able to disregard hypothetical MOFs with low efficiencies. In addition, this paper will also highlight the most important features within the data set. This research will contribute to enable the rapid synthesis of CO$_2$ adsorbing MOFs.
VarArray: Array-Geometry-Agnostic Continuous Speech Separation
Authors: Takuya Yoshioka, Xiaofei Wang, Dongmei Wang, Min Tang, Zirun Zhu, Zhuo Chen, Naoyuki Kanda
Date: 2021-10-12 00:00:00
Tasks: Speech Separation

Continuous speech separation using a microphone array was shown to be promising in dealing with the speech overlap problem in natural conversation transcription. This paper proposes VarArray, an array-geometry-agnostic speech separation neural network model. The proposed model is applicable to any number of microphones without retraining while leveraging the nonlinear correlation between the input channels. The proposed method adapts different elements that were proposed before separately, including transform-average-concatenate, conformer speech separation, and inter-channel phase differences, and combines them in an efficient and cohesive way. Large-scale evaluation was performed with two real meeting transcription tasks by using a fully developed transcription system requiring no prior knowledge such as reference segmentations, which allowed us to measure the impact that the continuous speech separation system could have in realistic settings. The proposed model outperformed a previous approach to array-geometry-agnostic modeling for all of the geometry configurations considered, achieving asclite-based speaker-agnostic word error rates of 17.5% and 20.4% for the AMI development and evaluation sets, respectively, in the end-to-end setting using no ground-truth segmentations.
Query-Reward Tradeoffs in Multi-Armed Bandits
Authors: Nadav Merlis, Yonathan Efroni, Shie Mannor
Date: 2021-10-12 00:00:00
Tasks: Multi-Armed Bandits

We consider a stochastic multi-armed bandit setting where reward must be actively queried for it to be observed. We provide tight lower and upper problem-dependent guarantees on both the regret and the number of queries. Interestingly, we prove that there is a fundamental difference between problems with a unique and multiple optimal arms, unlike in the standard multi-armed bandit problem. We also present a new, simple, UCB-style sampling concept, and show that it naturally adapts to the number of optimal arms and achieves tight regret and querying bounds.