pwc-archive/papers-with-abstracts huggingface dataset:
Higher-order mutual information reveals synergistic sub-networks for multi-neuron importance
Authors: Kenzo Clauw, Sebastiano Stramaglia, Daniele Marinazzo
Date: 2022-11-01 00:00:00
Tasks: Representation Learning

Quantifying which neurons are important with respect to the classification decision of a trained neural network is essential for understanding their inner workings. Previous work primarily attributed importance to individual neurons. In this work, we study which groups of neurons contain synergistic or redundant information using a multivariate mutual information method called the O-information. We observe the first layer is dominated by redundancy suggesting general shared features (i.e. detecting edges) while the last layer is dominated by synergy indicating local class-specific features (i.e. concepts). Finally, we show the O-information can be used for multi-neuron importance. This can be demonstrated by re-training a synergistic sub-network, which results in a minimal change in performance. These results suggest our method can be used for pruning and unsupervised representation learning.
Wavelet Neural Networks versus Wavelet-based Neural Networks
Authors: Lubomir T. Dechevsky, Kristoffer M. Tangrand
Date: 2022-11-01 00:00:00

This is the first paper in a sequence of studies in which we introduce a new type of neural networks (NNs) -- wavelet-based neural networks (WBNNs) -- and study their properties and potential for applications. We begin this study with a comparison to the currently existing type of wavelet neural networks (WNNs) and show that WBNNs vastly outperform WNNs. One reason for the vast superiority of WBNNs is their advanced hierarchical tree structure based on biorthonormal multiresolution analysis (MRA). Another reason for this is the implementation of our new idea to incorporate the wavelet tree depth into the neural width of the NN. The separation of the roles of wavelet depth and neural depth provides a conceptually and algorithmically simple but highly efficient methodology for sharp increase in functionality of swarm and deep WBNNs and rapid acceleration of the machine learning process.
Optimization of Oblivious Decision Tree Ensembles Evaluation for CPU
Authors: Alexey Mironov, Ilnur Khuziev
Date: 2022-11-01 00:00:00
Tasks: CPU

CatBoost is a popular machine learning library. CatBoost models are based on oblivious decision trees, making training and evaluation rapid. CatBoost has many applications, and some require low latency and high throughput evaluation. This paper investigates the possibilities for improving CatBoost's performance in single-core CPU computations. We explore the new features provided by the AVX instruction sets to optimize evaluation. We increase performance by 20-40% using AVX2 instructions without quality impact. We also introduce a new trade-off between speed and quality. Using float16 for leaf values and AVX-512 instructions, we achieve 50-70% speed-up.
An Empirical Study on Data Leakage and Generalizability of Link Prediction Models for Issues and Commits
Authors: Maliheh Izadi, Pooya Rostami Mazrae, Tom Mens, Arie van Deursen
Date: 2022-11-01 00:00:00
Tasks: Link Prediction, Transfer Learning

To enhance documentation and maintenance practices, developers conventionally establish links between related software artifacts manually. Empirical research has revealed that developers frequently overlook this practice, resulting in significant information loss. To address this issue, automatic link recovery techniques have been proposed. However, these approaches primarily focused on improving prediction accuracy on randomly-split datasets, with limited attention given to the impact of data leakage and the generalizability of the predictive models. LinkFormer seeks to address these limitations. Our approach not only preserves and improves the accuracy of existing predictions but also enhances their alignment with real-world settings and their generalizability. First, to better utilize contextual information for prediction, we employ the Transformer architecture and fine-tune multiple pre-trained models on both textual and metadata information of issues and commits. Next, to gauge the effect of time on model performance, we employ two splitting policies during both the training and testing phases; randomly- and temporally-split datasets. Finally, in pursuit of a generic model that can demonstrate high performance across a range of projects, we undertake additional fine-tuning of LinkFormer within two distinct transfer-learning settings. Our findings support that to simulate real-world scenarios effectively, researchers must maintain the temporal flow of data when training models. Furthermore, the results demonstrate that LinkFormer outperforms existing methodologies by a significant margin, achieving a 48% improvement in F1-measure within a project-based setting. Finally, the performance of LinkFormer in the cross-project setting is comparable to its average performance within the project-based scenario.
Generating Multilingual Gender-Ambiguous Text-to-Speech Voices
Authors: Konstantinos Markopoulos, Georgia Maniati, Georgios Vamvoukakis, Nikolaos Ellinas, Georgios Vardaxoglou, Panos Kakoulidis, Junkwang Oh, Gunu Jho, Inchul Hwang, Aimilios Chalamandaris, Pirros Tsiakoulis, Spyros Raptis
Date: 2022-11-01 00:00:00
Tasks: text-to-speech, Text to Speech

The gender of any voice user interface is a key element of its perceived identity. Recently, there has been increasing interest in interfaces where the gender is ambiguous rather than clearly identifying as female or male. This work addresses the task of generating novel gender-ambiguous TTS voices in a multi-speaker, multilingual setting. This is accomplished by efficiently sampling from a latent speaker embedding space using a proposed gender-aware method. Extensive objective and subjective evaluations clearly indicate that this method is able to efficiently generate a range of novel, diverse voices that are consistent and perceived as more gender-ambiguous than a baseline voice across all the languages examined. Interestingly, the gender perception is found to be robust across two demographic factors of the listeners: native language and gender. To our knowledge, this is the first systematic and validated approach that can reliably generate a variety of gender-ambiguous voices.
What drives a goalkeepers' decisions?
Authors: Samer Fatayri, Kirill Serykh, Egor Gumin
Date: 2022-11-01 00:00:00
Tasks: Position

In soccer games, the goalkeeper's performance is an important factor to the success of the whole team. Despite the goalkeeper's importance, little attention has been paid to their performance in events and tracking data. Here, we developed a model to predict which movements would be most effective for shot-stopping and compare it to the real-life behavior of goalkeepers. This model evaluates the performance of goalkeepers based on their position and dive radius. We found that contrary to the movements that were considered most effective by our model, real-life goalkeepers' movements were more diverse. We further used our model to develop a tool to analyse goalkeepers' behavior in real-life soccer games. In addition, a simulator function allows team analysts or couches to identify situations that allow further improvement of the reaction of the goalkeeper.
A General Search-based Framework for Generating Textual Counterfactual Explanations
Authors: Daniel Gilo, Shaul Markovitch
Date: 2022-11-01 00:00:00
Tasks: Classification, counterfactual, text-classification, Text Classification

One of the prominent methods for explaining the decision of a machine-learning classifier is by a counterfactual example. Most current algorithms for generating such examples in the textual domain are based on generative language models. Generative models, however, are trained to minimize a specific loss function in order to fulfill certain requirements for the generated texts. Any change in the requirements may necessitate costly retraining, thus potentially limiting their applicability. In this paper, we present a general search-based framework for generating counterfactual explanations in the textual domain. Our framework is model-agnostic, domain-agnostic, anytime, and does not require retraining in order to adapt to changes in the user requirements. We model the task as a search problem in a space where the initial state is the classified text, and the goal state is a text in a given target class. Our framework includes domain-independent modification operators, but can also exploit domain-specific knowledge through specialized operators. The search algorithm attempts to find a text from the target class with minimal user-specified distance from the original classified object.
Generalized Quadratic Embeddings for Nonlinear Dynamics using Deep Learning
Authors: Pawan Goyal, Peter Benner
Date: 2022-11-01 00:00:00
Tasks: Deep Learning

The engineering design process often relies on mathematical modeling that can describe the underlying dynamic behavior. In this work, we present a data-driven methodology for modeling the dynamics of nonlinear systems. To simplify this task, we aim to identify a coordinate transformation that allows us to represent the dynamics of nonlinear systems using a common, simple model structure. The advantage of a common simple model is that customized design tools developed for it can be applied to study a large variety of nonlinear systems. The simplest common model -- one can think of -- is linear, but linear systems often fall short in accurately capturing the complex dynamics of nonlinear systems. In this work, we propose using quadratic systems as the common structure, inspired by the lifting principle. According to this principle, smooth nonlinear systems can be expressed as quadratic systems in suitable coordinates without approximation errors. However, finding these coordinates solely from data is challenging. Here, we leverage deep learning to identify such lifted coordinates using only data, enabling a quadratic dynamical system to describe the system's dynamics. Additionally, we discuss the asymptotic stability of these quadratic dynamical systems. We illustrate the approach using data collected from various numerical examples, demonstrating its superior performance with the existing well-known techniques.
Informed Priors for Knowledge Integration in Trajectory Prediction
Authors: Christian Schlauch, Nadja Klein, Christian Wirth
Date: 2022-11-01 00:00:00
Tasks: Autonomous Driving, Continual Learning, Prediction, Trajectory Prediction

Informed machine learning methods allow the integration of prior knowledge into learning systems. This can increase accuracy and robustness or reduce data needs. However, existing methods often assume hard constraining knowledge, that does not require to trade-off prior knowledge with observations, but can be used to directly reduce the problem space. Other approaches use specific, architectural changes as representation of prior knowledge, limiting applicability. We propose an informed machine learning method, based on continual learning. This allows the integration of arbitrary, prior knowledge, potentially from multiple sources, and does not require specific architectures. Furthermore, our approach enables probabilistic and multi-modal predictions, that can improve predictive accuracy and robustness. We exemplify our approach by applying it to a state-of-the-art trajectory predictor for autonomous driving. This domain is especially dependent on informed learning approaches, as it is subject to an overwhelming large variety of possible environments and very rare events, while requiring robust and accurate predictions. We evaluate our model on a commonly used benchmark dataset, only using data already available in a conventional setup. We show that our method outperforms both non-informed and informed learning methods, that are often used in the literature. Furthermore, we are able to compete with a conventional baseline, even using half as many observation examples.
Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features
Authors: Alexandra Vioni, Georgia Maniati, Nikolaos Ellinas, June Sig Sung, Inchul Hwang, Aimilios Chalamandaris, Pirros Tsiakoulis
Date: 2022-11-01 00:00:00
Tasks: POS, Prediction, Self-Supervised Learning, text-to-speech, Text to Speech

Current state-of-the-art methods for automatic synthetic speech evaluation are based on MOS prediction neural models. Such MOS prediction models include MOSNet and LDNet that use spectral features as input, and SSL-MOS that relies on a pretrained self-supervised learning model that directly uses the speech signal as input. In modern high-quality neural TTS systems, prosodic appropriateness with regard to the spoken content is a decisive factor for speech naturalness. For this reason, we propose to include prosodic and linguistic features as additional inputs in MOS prediction systems, and evaluate their impact on the prediction outcome. We consider phoneme level F0 and duration features as prosodic inputs, as well as Tacotron encoder outputs, POS tags and BERT embeddings as higher-level linguistic inputs. All MOS prediction systems are trained on SOMOS, a neural TTS-only dataset with crowdsourced naturalness MOS evaluations. Results show that the proposed additional features are beneficial in the MOS prediction task, by improving the predicted MOS scores' correlation with the ground truths, both at utterance-level and system-level predictions.
Recurrent Neural Networks and Universal Approximation of Bayesian Filters
Authors: Adrian N. Bishop, Edwin V. Bonilla
Date: 2022-11-01 00:00:00
Tasks: Time Series, Time Series Analysis

We consider the Bayesian optimal filtering problem: i.e. estimating some conditional statistics of a latent time-series signal from an observation sequence. Classical approaches often rely on the use of assumed or estimated transition and observation models. Instead, we formulate a generic recurrent neural network framework and seek to learn directly a recursive mapping from observational inputs to the desired estimator statistics. The main focus of this article is the approximation capabilities of this framework. We provide approximation error bounds for filtering in general non-compact domains. We also consider strong time-uniform approximation error bounds that guarantee good long-time performance. We discuss and illustrate a number of practical concerns and implications of these results.
Rating Triggers for Collateral-Inclusive XVA via Machine Learning and SDEs on Lie Groups
Authors: Kevin Kamm, Michelle Muniz
Date: 2022-11-01 00:00:00

In this paper, we model the rating process of an entity by using a geometrical approach. We model rating transitions as an SDE on a Lie group. Specifically, we focus on calibrating the model to both historical data (rating transition matrices) and market data (CDS quotes) and compare the most popular choices of changes of measure to switch from the historical probability to the risk-neutral one. For this, we show how the classical Girsanov theorem can be applied in the Lie group setting. Moreover, we overcome some of the imperfections of rating matrices published by rating agencies, which are computed with the cohort method, by using a novel Deep Learning approach. This leads to an improvement of the entire scheme and makes the model more robust for applications. We apply our model to compute bilateral credit and debit valuation adjustments of a netting set under a CSA with thresholds depending on ratings of the two parties.
Entity Matching by Pool-based Active Learning
Authors: Youfang Han, Chunping Li
Date: 2022-11-01 00:00:00
Tasks: Active Learning

The goal of entity matching is to find the corresponding records representing the same real-world entity from different data sources. At present, in the mainstream methods, rule-based entity matching methods need tremendous domain knowledge. The machine-learning based or deep-learning based entity matching methods need a large number of labeled samples to build the model, which is difficult to achieve in some applications. In addition, learning-based methods are easy to over-fitting, so the quality requirements of training samples are very high. In this paper, we present an active learning method ALMatcher for the entity matching tasks. This method needs to manually label only a small number of valuable samples, and use these samples to build a model with high quality. This paper proposes a hybrid uncertainty as query strategy to find those valuable samples for labeling, which can minimize the number of labeled training samples meanwhile meet the task requirements. The proposed method has been validated on seven data sets in different fields. The experiment shows that ALMatcher uses only a small number of labeled samples and achieves better results compared to existing approaches.
SADT: Combining Sharpness-Aware Minimization with Self-Distillation for Improved Model Generalization
Authors: Masud An-Nur Islam Fahim, Jani Boutellier
Date: 2022-11-01 00:00:00
Tasks: Data Augmentation

Methods for improving deep neural network training times and model generalizability consist of various data augmentation, regularization, and optimization approaches, which tend to be sensitive to hyperparameter settings and make reproducibility more challenging. This work jointly considers two recent training strategies that address model generalizability: sharpness-aware minimization, and self-distillation, and proposes the novel training strategy of Sharpness-Aware Distilled Teachers (SADT). The experimental section of this work shows that SADT consistently outperforms previously published training strategies in model convergence time, test-time performance, and model generalizability over various neural architectures, datasets, and hyperparameter settings.
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics
Authors: Indu Kant Deo, Rui Gao, Rajeev Jaiman
Date: 2022-11-01 00:00:00
Tasks: Decoder

There is a critical need for efficient and reliable active flow control strategies to reduce drag and noise in aerospace and marine engineering applications. While traditional full-order models based on the Navier-Stokes equations are not feasible, advanced model reduction techniques can be inefficient for active control tasks, especially with strong non-linearity and convection-dominated phenomena. Using convolutional recurrent autoencoder network architectures, deep learning-based reduced-order models have been recently shown to be effective while performing several orders of magnitude faster than full-order simulations. However, these models encounter significant challenges outside the training data, limiting their effectiveness for active control and optimization tasks. In this study, we aim to improve the extrapolation capability by modifying network architecture and integrating coupled space-time physics as an implicit bias. Reduced-order models via deep learning generally employ decoupling in spatial and temporal dimensions, which can introduce modeling and approximation errors. To alleviate these errors, we propose a novel technique for learning coupled spatial-temporal correlation using a 3D convolution network. We assess the proposed technique against a standard encoder-propagator-decoder model and demonstrate a superior extrapolation performance. To demonstrate the effectiveness of 3D convolution network, we consider a benchmark problem of the flow past a circular cylinder at laminar flow conditions and use the spatio-temporal snapshots from the full-order simulations. Our proposed 3D convolution architecture accurately captures the velocity and pressure fields for varying Reynolds numbers. Compared to the standard encoder-propagator-decoder network, the spatio-temporal-based 3D convolution network improves the prediction range of Reynolds numbers outside of the training data.
Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges
Authors: Enwei Zhu, Yiyang Liu, Ming Jin, Jinpeng Li
Date: 2022-11-01 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Many recent named entity recognition (NER) studies criticize flat NER for its non-overlapping assumption, and switch to investigating nested NER. However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly. This study proposes a new subtask, nested-from-flat NER, which corresponds to a realistic application scenario: given data annotated with flat entities only, one may still desire the trained model capable of recognizing nested entities. To address this task, we train span-based models and deliberately ignore the spans nested inside labeled entities, since these spans are possibly unlabeled entities. With nested entities removed from the training data, our model achieves 54.8%, 54.2% and 41.1% F1 scores on the subset of spans within entities on ACE 2004, ACE 2005 and GENIA, respectively. This suggests the effectiveness of our approach and the feasibility of the task. In addition, the model's performance on flat entities is entirely unaffected. We further manually annotate the nested entities in the test set of CoNLL 2003, creating a nested-from-flat NER benchmark. Analysis results show that the main challenges stem from the data and annotation inconsistencies between the flat and nested entities.
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Authors: Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama
Date: 2022-11-01 00:00:00
Tasks: Adversarial Robustness, Pseudo Label

Adversarial training (AT) with imperfect supervision is significant but receives limited attention. To push AT towards more practical scenarios, we explore a brand new yet challenging setting, i.e., AT with complementary labels (CLs), which specify a class that a data sample does not belong to. However, the direct combination of AT with existing methods for CLs results in consistent failure, but not on a simple baseline of two-stage training. In this paper, we further explore the phenomenon and identify the underlying challenges of AT with CLs as intractable adversarial optimization and low-quality adversarial examples. To address the above problems, we propose a new learning strategy using gradually informative attacks, which consists of two critical components: 1) Warm-up Attack (Warm-up) gently raises the adversarial perturbation budgets to ease the adversarial optimization with CLs; 2) Pseudo-Label Attack (PLA) incorporates the progressively informative model predictions into a corrected complementary loss. Extensive experiments are conducted to demonstrate the effectiveness of our method on a range of benchmarked datasets. The code is publicly available at: https://github.com/RoyalSkye/ATCL.