pwc-archive/papers-with-abstracts huggingface dataset:
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training
Authors: Miloš Nikolić, Enrique Torres Sanchez, Jiahui Wang, Ali Hadi Zadeh, Mostafa Mahmoud, Ameer Abdelhadi, Kareem Ibrahim, Andreas Moshovos
Date: 2022-04-28 00:00:00

The transfer of tensors from/to memory during neural network training dominates time and energy. To improve energy efficiency and performance, research has been exploring ways to use narrower data representations. So far, these attempts relied on user-directed trial-and-error to achieve convergence. We present methods that relieve users from this responsibility. Our methods dynamically adjust the size and format of the floating-point containers used for activations and weights during training, achieving adaptivity across three dimensions: i) which datatype to use, ii) on which tensor, and iii) how it changes over time. The different meanings and distributions of exponent and mantissas lead us to tailored approaches for each. We present two lossy pairs of methods to eliminate as many mantissa and exponent bits as possible without affecting accuracy. Quantum Mantissa and Quantum Exponent are machine learning compression methods that tap into the gradient descent algorithm to learn the minimal mantissa and exponent bitlengths on a per-layer granularity. They automatically learn that many tensors can use just 1 or 2 mantissa bits and 3 or 4 exponent bits. Overall, the two machine learning methods reduce the footprint by $4.74\times$. Alternatively, BitWave observes changes in the loss function during training to adjust mantissa and exponent bitlengths network-wide, yielding a $3.19\times$ reduction in footprint. Finally, we present an optional method, Gecko, to exploit the naturally emerging, lop-sided exponent distribution to losslessly compress resulting exponents from Quantum Exponent or BitWave and, on average, improve compression rates to $5.64\times$ and $4.56\times$.
Russian Texts Detoxification with Levenshtein Editing
Authors: Ilya Gusev
Date: 2022-04-28 00:00:00
Tasks: Style Transfer

Text detoxification is a style transfer task of creating neutral versions of toxic texts. In this paper, we use the concept of text editing to build a two-step tagging-based detoxification model using a parallel corpus of Russian texts. With this model, we achieved the best style transfer accuracy among all models in the RUSSE Detox shared task, surpassing larger sequence-to-sequence models.
Standardized Evaluation of Machine Learning Methods for Evolving Data Streams
Authors: Johannes Haug, Effi Tramountani, Gjergji Kasneci
Date: 2022-04-28 00:00:00
Tasks: BIG-bench Machine Learning, Drift Detection, feature selection

Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existing work therefore often draws on different heuristics and simulations that do not necessarily produce meaningful and reliable results. Indeed, in the absence of common evaluation standards, it often remains unclear how online learning methods will perform in practice or in comparison to similar work. In this paper, we propose a comprehensive set of properties for high-quality machine learning in evolving data streams. In particular, we discuss sensible performance measures and evaluation strategies for online predictive modelling, online feature selection and concept drift detection. As one of the first works, we also look at the interpretability of online learning methods. The proposed evaluation standards are provided in a new Python framework called float. Float is completely modular and allows the simultaneous integration of common libraries, such as scikit-multiflow or river, with custom code. Float is open-sourced and can be accessed at https://github.com/haugjo/float. In this sense, we hope that our work will contribute to more standardized, reliable and realistic testing and comparison of online machine learning methods.
Personalized Federated Learning with Multiple Known Clusters
Authors: Boxiang Lyu, Filip Hanzely, Mladen Kolar
Date: 2022-04-28 00:00:00
Tasks: Federated Learning, Personalized Federated Learning

We consider the problem of personalized federated learning when there are known cluster structures within users. An intuitive approach would be to regularize the parameters so that users in the same cluster share similar model weights. The distances between the clusters can then be regularized to reflect the similarity between different clusters of users. We develop an algorithm that allows each cluster to communicate independently and derive the convergence results. We study a hierarchical linear model to theoretically demonstrate that our approach outperforms agents learning independently and agents learning a single shared weight. Finally, we demonstrate the advantages of our approach using both simulated and real-world data.
Representative period selection for power system planning using autoencoder-based dimensionality reduction
Authors: Marc Barbar, Dharik S. Mallapragada
Date: 2022-04-28 00:00:00
Tasks: Clustering, Dimensionality Reduction

Power sector capacity expansion models (CEMs) that are used for studying future low-carbon grid scenarios must incorporate detailed representation of grid operations. Often CEMs are formulated to model grid operations over representative periods that are sampled from the original input data using clustering algorithms. However, such representative period selection (RPS) methods are limited by the declining efficacy of the clustering algorithm with increasing dimensionality of the input data and do not consider the relative importance of input data variations on CEM outcomes. Here, we propose a RPS method that addresses these limitations by incorporating dimensionality reduction, accomplished via neural network based autoencoders, prior to clustering. Such dimensionality reduction not only improves the performance of the clustering algorithm, but also facilitates using additional features, such as estimated outputs produced from parallel solutions of simplified versions of the CEM for each disjoint period in the input data (e.g. 1 week). The impact of incorporating dimensionality reduction as part of RPS methods is quantified through the error in outcomes of the corresponding reduced-space CEM vs. the full space CEM. Extensive numerical experimentation across various networks and range of technology and policy scenarios establish the superiority of the dimensionality-reduction based RPS methods.
Process-BERT: A Framework for Representation Learning on Educational Process Data
Authors: Alexander Scarlatos, Christopher Brinton, Andrew Lan
Date: 2022-04-28 00:00:00
Tasks: Representation Learning

Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn. One can use process data for many downstream tasks such as learning outcome prediction and automatically delivering personalized intervention. However, analyzing process data is challenging since the specific format of process data varies a lot depending on different learning/testing scenarios. In this paper, we propose a framework for learning representations of educational process data that is applicable across many different learning scenarios. Our framework consists of a pre-training step that uses BERT-type objectives to learn representations from sequential process data and a fine-tuning step that further adjusts these representations on downstream prediction tasks. We apply our framework to the 2019 nation's report card data mining competition dataset that consists of student problem-solving process data and detail the specific models we use in this scenario. We conduct both quantitative and qualitative experiments to show that our framework results in process data representations that are both predictive and informative.
Bona fide Riesz projections for density estimation
Authors: P. del Aguila Pla, Michael Unser
Date: 2022-04-28 00:00:00
Tasks: Density Estimation

The projection of sample measurements onto a reconstruction space represented by a basis on a regular grid is a powerful and simple approach to estimate a probability density function. In this paper, we focus on Riesz bases and propose a projection operator that, in contrast to previous works, guarantees the bona fide properties for the estimate, namely, non-negativity and total probability mass $1$. Our bona fide projection is defined as a convex problem. We propose solution techniques and evaluate them. Results suggest an improved performance, specifically in circumstances prone to rippling effects.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Authors: Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian Theis
Date: 2022-04-28 00:00:00
Tasks: Decoder, Drug Discovery, Transfer Learning

Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA HTS is required to enrich single-cell data meaningfully. We introduce chemCPA, a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with an architecture surgery for transfer learning and demonstrate how training on existing bulk RNA HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating drug discovery.
Predicting batch queue job wait times for informed scheduling of urgent HPC workloads
Authors: Nick Brown, Gordon Gibb, Evgenij Belikov, Rupert Nash
Date: 2022-04-28 00:00:00
Tasks: Scheduling

There is increasing interest in the use of HPC machines for urgent workloads to help tackle disasters as they unfold. Whilst batch queue systems are not ideal in supporting such workloads, many disadvantages can be worked around by accurately predicting when a waiting job will start to run. However there are numerous challenges in achieving such a prediction with high accuracy, not least because the queue's state can change rapidly and depend upon many factors. In this work we explore a novel machine learning approach for predicting queue wait times, hypothesising that such a model can capture the complex behaviour resulting from the queue policy and other interactions to generate accurate job start times. For ARCHER2 (HPE Cray EX), Cirrus (HPE 8600) and 4-cabinet (HPE Cray EX) we explore how different machine learning approaches and techniques improve the accuracy of our predictions, comparing against the estimation generated by Slurm. We demonstrate that our techniques deliver the most accurate predictions across our machines of interest, with the result of this work being the ability to predict job start times within one minute of the actual start time for around 65\% of jobs on ARCHER2 and 4-cabinet, and 76\% of jobs on Cirrus. When compared against what Slurm can deliver, this represents around 3.8 times better accuracy on ARCHER2 and 18 times better for Cirrus. Furthermore our approach can accurately predicting the start time for three quarters of all job within ten minutes of the actual start time on ARCHER2 and 4-cabinet, and for 90\% of jobs on Cirrus. Whilst the driver of this work has been to better facilitate placement of urgent workloads across HPC machines, the insights gained can be used to provide wider benefits to users and also enrich existing batch queue systems and inform policy too.
Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes
Authors: Pablo Mosteiro, Emil Rijcken, Kalliopi Zervanou, Uzay Kaymak, Floortje Scheepers, Marco Spruit
Date: 2022-04-28 00:00:00
Tasks: BIG-bench Machine Learning

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records are valuable resources capturing unique information, but are seldom used to their full potential. We explore conventional and deep machine learning methods to assess violence risk in psychiatric patients using practitioner notes. The performance of our best models is comparable to the currently used questionnaire-based method, with an area under the Receiver Operating Characteristic curve of approximately 0.8. We find that the deep-learning model BERTje performs worse than conventional machine learning methods. We also evaluate our data and our classifiers to understand the performance of our models better. This is particularly important for the applicability of evaluated classifiers to new data, and is also of great interest to practitioners, due to the increased availability of new data in electronic format.
On tuning a mean-field model for semi-supervised classification
Authors: Emílio Bergamim, Fabricio Breve
Date: 2022-04-28 00:00:00

Semi-supervised learning (SSL) has become an interesting research area due to its capacity for learning in scenarios where both labeled and unlabeled data are available. In this work, we focus on the task of transduction - when the objective is to label all data presented to the learner - with a mean-field approximation to the Potts model. Aiming at this particular task we study how classification results depend on $\beta$ and find that the optimal phase depends highly on the amount of labeled data available. In the same study, we also observe that more stable classifications regarding small fluctuations in $\beta$ are related to configurations of high probability and propose a tuning approach based on such observation. This method relies on a novel parameter $\gamma$ and we then evaluate two different values of the said quantity in comparison with classical methods in the field. This evaluation is conducted by changing the amount of labeled data available and the number of nearest neighbors in the similarity graph. Empirical results show that the tuning method is effective and allows NMF to outperform other approaches in datasets with fewer classes. In addition, one of the chosen values for $\gamma$ also leads to results that are more resilient to changes in the number of neighbors, which might be of interest to practitioners in the field of SSL.
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications
Authors: Xuchuang Wang, Hong Xie, John C. S. Lui
Date: 2022-04-28 00:00:00
Tasks: Edge-computing, Multi-Armed Bandits

Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MMAB models mostly assume when more than one player pulls the same arm, they either have a collision and obtain zero rewards, or have no collision and gain independent rewards, both of which are usually too restrictive in practical scenarios. In this paper, we propose an MMAB with shareable resources as an extension to the collision and non-collision settings. Each shareable arm has finite shareable resources and a "per-load" reward random variable, both of which are unknown to players. The reward from a shareable arm is equal to the "per-load" reward multiplied by the minimum between the number of players pulling the arm and the arm's maximal shareable resources. We consider two types of feedback: sharing demand information (SDI) and sharing demand awareness (SDA), each of which provides different signals of resource sharing. We design the DPE-SDI and SIC-SDA algorithms to address the shareable arm problem under these two cases of feedback respectively and prove that both algorithms have logarithmic regrets that are tight in the number of rounds. We conduct simulations to validate both algorithms' performance and show their utilities in wireless networking and edge computing.
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification
Authors: Georgios P. Spithourakis, Ivan Vulić, Michał Lis, Iñigo Casanueva, Paweł Budzianowski
Date: 2022-04-28 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Speaker Identification, Speaker Verification, Spoken Dialogue Systems

Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date of birth. In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French. Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research.
COSTI: a New Classifier for Sequences of Temporal Intervals
Authors: Jakub Michał Bilski, Agnieszka Jastrzębska
Date: 2022-04-28 00:00:00
Tasks: Classification, Time Series, Time Series Analysis

Classification of sequences of temporal intervals is a part of time series analysis which concerns series of events. We propose a new method of transforming the problem to a task of multivariate series classification. We use one of the state-of-the-art algorithms from the latter domain on the new representation to obtain significantly better accuracy than the state-of-the-art methods from the former field. We discuss limitations of this workflow and address them by developing a novel method for classification termed COSTI (short for Classification of Sequences of Temporal Intervals) operating directly on sequences of temporal intervals. The proposed method remains at a high level of accuracy and obtains better performance while avoiding shortcomings connected to operating on transformed data. We propose a generalized version of the problem of classification of temporal intervals, where each event is supplemented with information about its intensity. We also provide two new data sets where this information is of substantial value.
Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task
Authors: Jakub Michał Bilski, Agnieszka Jastrzębska
Date: 2022-04-28 00:00:00
Tasks: Classification, Time Series, Time Series Analysis, Time Series Classification

Time series classification is one of the very popular machine learning tasks. In this paper, we explore the application of Hidden Markov Model (HMM) for time series classification. We distinguish between two modes of HMM application. The first, in which a single model is built for each class. The second, in which one HMM is built for each time series. We then transfer both approaches for classifier construction to the domain of Fuzzy Cognitive Maps. The identified four models, HMM NN (HMM, one per series), HMM 1C (HMM, one per class), FCM NN, and FCM 1C are then studied in a series of experiments. We compare the performance of different models and investigate the impact of their hyperparameters on the time series classification accuracy. The empirical evaluation shows a clear advantage of the one-model-per-series approach. The results show that the choice between HMM and FCM should be dataset-dependent.
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction
Authors: Takayuki Katsuki, Kohei Miyaguchi, Akira Koseki, Toshiya Iwamori, Ryosuke Yanagiya, Atsushi Suzuki
Date: 2022-04-28 00:00:00
Tasks: Time Series, Time Series Analysis

We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them.
Regotron: Regularizing the Tacotron2 architecture via monotonic alignment loss
Authors: Efthymios Georgiou, Kosmas Kritsis, Georgios Paraskevopoulos, Athanasios Katsamanis, Vassilis Katsouros, Alexandros Potamianos
Date: 2022-04-28 00:00:00
Tasks: text-to-speech, Text to Speech

Recent deep learning Text-to-Speech (TTS) systems have achieved impressive performance by generating speech close to human parity. However, they suffer from training stability issues as well as incorrect alignment of the intermediate acoustic representation with the input text sequence. In this work, we introduce Regotron, a regularized version of Tacotron2 which aims to alleviate the training issues and at the same time produce monotonic alignments. Our method augments the vanilla Tacotron2 objective function with an additional term, which penalizes non-monotonic alignments in the location-sensitive attention mechanism. By properly adjusting this regularization term we show that the loss curves become smoother, and at the same time Regotron consistently produces monotonic alignments in unseen examples even at an early stage (13\% of the total number of epochs) of its training process, whereas the fully converged Tacotron2 fails to do so. Moreover, our proposed regularization method has no additional computational overhead, while reducing common TTS mistakes and achieving slighlty improved speech naturalness according to subjective mean opinion scores (MOS) collected from 50 evaluators.
DOTIN: Dropping Task-Irrelevant Nodes for GNNs
Authors: Shaofeng Zhang, Feng Zhu, Junchi Yan, Rui Zhao, Xiaokang Yang
Date: 2022-04-28 00:00:00
Tasks: Graph Classification, Graph Learning

Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced the pooling strategy to reduce the size of graphs for learning, such that the scalability and efficiency can be improved. However, these pooling-based methods are mainly tailored to a single graph-level task and pay more attention to local information, limiting their performance in multi-task settings which often require task-specific global information. In this paper, departure from these pooling-based efforts, we design a new approach called DOTIN (\underline{D}r\underline{o}pping \underline{T}ask-\underline{I}rrelevant \underline{N}odes) to reduce the size of graphs. Specifically, by introducing $K$ learnable virtual nodes to represent the graph embeddings targeted to $K$ different graph-level tasks, respectively, up to 90\% raw nodes with low attentiveness with an attention model -- a transformer in this paper, can be adaptively dropped without notable performance decreasing. Achieving almost the same accuracy, our method speeds up GAT by about 50\% on graph-level tasks including graph classification and graph edit distance (GED) with about 60\% less memory, on D\&D dataset. Code will be made publicly available in https://github.com/Sherrylone/DOTIN.