pwc-archive/papers-with-abstracts huggingface dataset:
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data
Authors: Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan Cowen
Date: 2024-03-21 00:00:00
Tasks: Emotion Recognition, Event Detection, Speech Emotion Recognition

The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, from speech emotion recognition to audio event detection, but the community is sparse compared to other ML areas, e.g., computer vision or natural language processing. A major limitation with audio is the available data; with audio being a time-dependent modality, high-quality data collection is time-consuming and costly, making it challenging for academic groups to apply their often state-of-the-art strategies to a larger, more generalizable dataset. In this short white paper, to encourage researchers with limited access to large-datasets, the organizers first outline several open-source datasets that are available to the community, and for the duration of the workshop are making several propriety datasets available. Namely, three vocal datasets, Hume-Prosody, Hume-VocalBurst, an acted emotional speech dataset Modulate-Sonata, and an in-game streamer dataset Modulate-Stream. We outline the current baselines on these datasets but encourage researchers from across audio to utilize them outside of the initial baseline tasks.
A New Massive Multilingual Dataset for High-Performance Language Technologies
Authors: Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer Van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, Jörg Tiedemann
Date: 2024-03-20 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Management, Sentence

We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of ~5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.
Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings
Authors: Gaifan Zhang, Yi Zhou, Danushka Bollegala
Date: 2024-03-20 00:00:00
Tasks: Dimensionality Reduction, Sentence, Sentence Embeddings

Sentence embeddings produced by Pretrained Language Models (PLMs) have received wide attention from the NLP community due to their superior performance when representing texts in numerous downstream applications. However, the high dimensionality of the sentence embeddings produced by PLMs is problematic when representing large numbers of sentences in memory- or compute-constrained devices. As a solution, we evaluate unsupervised dimensionality reduction methods to reduce the dimensionality of sentence embeddings produced by PLMs. Our experimental results show that simple methods such as Principal Component Analysis (PCA) can reduce the dimensionality of sentence embeddings by almost $50\%$, without incurring a significant loss in performance in multiple downstream tasks. Surprisingly, reducing the dimensionality further improves performance over the original high-dimensional versions for the sentence embeddings produced by some PLMs in some tasks.
Visually Grounded Speech Models have a Mutual Exclusivity Bias
Authors: Leanne Nortje, Dan Oneaţă, Yevgen Matusevych, Herman Kamper
Date: 2024-03-20 00:00:00

When children learn new words, they employ constraints such as the mutual exclusivity (ME) bias: a novel word is mapped to a novel object rather than a familiar one. This bias has been studied computationally, but only in models that use discrete word representations as input, ignoring the high variability of spoken words. We investigate the ME bias in the context of visually grounded speech models that learn from natural images and continuous speech audio. Concretely, we train a model on familiar words and test its ME bias by asking it to select between a novel and a familiar object when queried with a novel word. To simulate prior acoustic and visual knowledge, we experiment with several initialisation strategies using pretrained speech and vision networks. Our findings reveal the ME bias across the different initialisation approaches, with a stronger bias in models with more prior (in particular, visual) knowledge. Additional tests confirm the robustness of our results, even when different loss functions are considered.
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction
Authors: Fauzan Farooqui, Thanmay Jayakumar, Pulkit Mathur, Mansi Radke
Date: 2024-03-20 00:00:00
Tasks: Open Information Extraction, POS, Structured Prediction, Word Embeddings

Open Information Extraction (OIE) is a structured prediction (SP) task in Natural Language Processing (NLP) that aims to extract structured $n$-ary tuples - usually subject-relation-object triples - from free text. The word embeddings in the input text can be enhanced with linguistic features, usually Part-of-Speech (PoS) and Syntactic Dependency Parse (SynDP) labels. However, past enhancement techniques cannot leverage the power of pretrained language models (PLMs), which themselves have been hardly used for OIE. To bridge this gap, we are the first to leverage linguistic features with a Seq2Seq PLM for OIE. We do so by introducing two methods - Weighted Addition and Linearized Concatenation. Our work can give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go. In our settings, this shows wide improvements of up to 24.9%, 27.3% and 14.9% on Precision, Recall and F1 scores respectively over the baseline. Beyond this, we address other important challenges in the field: to reduce compute overheads with the features, we are the first ones to exploit Semantic Dependency Parse (SemDP) tags; to address flaws in current datasets, we create a clean synthetic dataset; finally, we contribute the first known study of OIE behaviour in SP models.
Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases
Authors: Tyler Loakman, Chen Tang, Chenghua Lin
Date: 2024-03-20 00:00:00
Tasks: Language Modeling, Language Modelling, Text Generation

Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry. In this article, we present new work on the generation of English tongue twisters - a form of language that is required to be conditioned on a phoneme level to maximize sound overlap, while maintaining semantic consistency with an input topic or phrase and still being grammatically correct. We present TwisterLister, a pipeline for generating phonologically informed tongue twisters from large language models (LLMs) that we use to generate TwistList 2.0, the largest annotated dataset of tongue twisters to date, consisting of 17K+ examples from a combination of human and LLM authors. Our generation pipeline involves the use of a phonologically constrained vocabulary alongside LLM prompting to generate novel, non-derivative tongue twister examples. We additionally present the results of automatic and human evaluation of smaller models trained on our generated dataset to demonstrate the extent to which phonologically motivated language types can be generated without explicit injection of phonological knowledge. Additionally, we introduce a phoneme-aware constrained decoding module (PACD) that can be integrated into an autoregressive language model and demonstrate that this method generates good quality tongue twisters both with and without fine-tuning the underlying language model. We also design and implement a range of automatic metrics for the task of tongue twister generation that is phonologically motivated and captures the unique essence of tongue twisters, primarily based on phonemic edit distance (PED)
A reinforcement learning guided hybrid evolutionary algorithm for the latency location routing problem
Authors: Yuji Zou, Jin-Kao Hao, Qinghua Wu
Date: 2024-03-21 00:00:00
Tasks: Diversity, reinforcement-learning, Reinforcement Learning

The latency location routing problem integrates the facility location problem and the multi-depot cumulative capacitated vehicle routing problem. This problem involves making simultaneous decisions about depot locations and vehicle routes to serve customers while aiming to minimize the sum of waiting (arriving) times for all customers. To address this computationally challenging problem, we propose a reinforcement learning guided hybrid evolutionary algorithm following the framework of the memetic algorithm. The proposed algorithm relies on a diversity-enhanced multi-parent edge assembly crossover to build promising offspring and a reinforcement learning guided variable neighborhood descent to determine the exploration order of multiple neighborhoods. Additionally, strategic oscillation is used to achieve a balanced exploration of both feasible and infeasible solutions. The competitiveness of the algorithm against state-of-the-art methods is demonstrated by experimental results on the three sets of 76 popular instances, including 51 improved best solutions (new upper bounds) for the 59 instances with unknown optima and equal best results for the remaining instances. We also conduct additional experiments to shed light on the key components of the algorithm.
Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them
Authors: Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman
Date: 2024-03-21 00:00:00
Tasks: Transfer Learning

Traditional approaches to neuroevolution often start from scratch. This becomes prohibitively expensive in terms of computational and data requirements when targeting modern, deep neural networks. Using a warm start could be highly advantageous, e.g., using previously trained networks, potentially from different sources. This moreover enables leveraging the benefits of transfer learning (in particular vastly reduced training effort). However, recombining trained networks is non-trivial because architectures and feature representations typically differ. Consequently, a straightforward exchange of layers tends to lead to a performance breakdown. We overcome this by matching the layers of parent networks based on their connectivity, identifying potential crossover points. To correct for differing feature representations between these layers we employ stitching, which merges the networks by introducing new layers at crossover points. To train the merged network, only stitching layers need to be considered. New networks can then be created by selecting a subnetwork by choosing which stitching layers to (not) use. Assessing their performance is efficient as only their evaluation on data is required. We experimentally show that our approach enables finding networks that represent novel trade-offs between performance and computational cost, with some even dominating the original networks.
Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks
Authors: Takuro Kutsuna
Date: 2024-03-08 00:00:00

In this paper, we first identify activation shift, a simple but remarkable phenomenon in a neural network in which the preactivation value of a neuron has non-zero mean that depends on the angle between the weight vector of the neuron and the mean of the activation vector in the previous layer. We then propose linearly constrained weights (LCW) to reduce the activation shift in both fully connected and convolutional layers. The impact of reducing the activation shift in a neural network is studied from the perspective of how the variance of variables in the network changes through layer operations in both forward and backward chains. We also discuss its relationship to the vanishing gradient problem. Experimental results show that LCW enables a deep feedforward network with sigmoid activation functions to be trained efficiently by resolving the vanishing gradient problem. Moreover, combined with batch normalization, LCW improves generalization performance of both feedforward and convolutional networks.
A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery
Authors: Larry Han, Yi Zhang, Meena Nathan, John E. Mayer, Jr., Sara K. Pasquali, Katya Zelevinsky, Rui Duan, Sharon-Lise T. Normand
Date: 2024-03-21 00:00:00
Tasks: Causal Inference, STS, Transfer Learning, valid

Congenital heart defects (CHD) are the most prevalent birth defects in the United States and surgical outcomes vary considerably across the country. The outcomes of treatment for CHD differ for specific patient subgroups, with non-Hispanic Black and Hispanic populations experiencing higher rates of mortality and morbidity. A valid comparison of outcomes within racial/ethnic subgroups is difficult given large differences in case-mix and small subgroup sizes. We propose a causal inference framework for outcome assessment and leverage advances in transfer learning to incorporate data from both target and source populations to help estimate causal effects while accounting for different sources of risk factor and outcome differences across populations. Using the Society of Thoracic Surgeons' Congenital Heart Surgery Database (STS-CHSD), we focus on a national cohort of patients undergoing the Norwood operation from 2016-2022 to assess operative mortality and morbidity outcomes across U.S. geographic regions by race/ethnicity. We find racial and ethnic outcome differences after controlling for potential confounding factors. While geography does not have a causal effect on outcomes for non-Hispanic Caucasian patients, non-Hispanic Black patients experience wide variability in outcomes with estimated 30-day mortality ranging from 5.9% (standard error 2.2%) to 21.6% (4.4%) across U.S. regions.
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
Authors: Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, Jie Jiang
Date: 2024-03-21 00:00:00
Tasks: Binary Classification, Click-Through Rate Prediction, Prediction

Click-through rate (CTR) prediction is a crucial area of research in online advertising. While binary cross entropy (BCE) has been widely used as the optimization objective for treating CTR prediction as a binary classification problem, recent advancements have shown that combining BCE loss with an auxiliary ranking loss can significantly improve performance. However, the full effectiveness of this combination loss is not yet fully understood. In this paper, we uncover a new challenge associated with the BCE loss in scenarios where positive feedback is sparse: the issue of gradient vanishing for negative samples. We introduce a novel perspective on the effectiveness of the auxiliary ranking loss in CTR prediction: it generates larger gradients on negative samples, thereby mitigating the optimization difficulties when using the BCE loss only and resulting in improved classification ability. To validate our perspective, we conduct theoretical analysis and extensive empirical evaluations on public datasets. Additionally, we successfully integrate the ranking loss into Tencent's online advertising system, achieving notable lifts of 0.70% and 1.26% in Gross Merchandise Value (GMV) for two main scenarios. The code is openly accessible at: https://github.com/SkylerLinn/Understanding-the-Ranking-Loss.
trust-Region Adaptive Frequency for Online Continual Learning
Authors: Yajing Kong1 · Liu Liu1 · Maoying Qiao2 · Zhen Wang1 · Dacheng Tao1
Date: 2023-04-18 00:00:00
Proceeding: journal 2023 4
Tasks: Continual Learning

In the paradigm of online continual learning, one neural network is exposed to a sequence of tasks, where the data arrive in an online fashion and previously seen data are not accessible. Such online fashion causes insufficient learning and severe forgetting on past tasks issues, preventing a good stability-plasticity trade-off, where ideally the network is expected to have high plasticity to adapt to new tasks well and have the stability to prevent forgetting on old tasks simultaneously. To solve these issues, we propose a trust-region adaptive frequency approach, which alternates between standard-process and intra process updates. Specifically, the standard-process replays data stored in a coreset and interleaves the data with current data, and the intra-process updates the network parameters based on the coreset. Furthermore, to improve the unsatisfactory performance stemming from online fashion, the frequency of the intra-process is adjusted based on a trust region, which is measured by the confidence score of current data. During the intra-process, we distill the dark knowledge to retain useful learned knowledge. Moreover, to store more representative data in the coreset, a confidence-based coreset selection is presented in an online manner. The experimental results on standard benchmarks show that the proposed method significantly outperforms state-of-art continual learning algorithms
Circuit Transformer: A Transformer That Preserves Logical Equivalence
Authors: Xihan Li, Xing Li, Lei Chen, Xing Zhang, Mingxuan Yuan, Jun Wang
Date: 2024-03-14 00:00:00
Tasks: Hallucination

Implementing Boolean functions with circuits consisting of logic gates is fundamental in digital computer design. However, the implemented circuit must be exactly equivalent, which hinders generative neural approaches on this task due to their occasionally wrong predictions. In this study, we introduce a generative neural model, the "Circuit Transformer", which eliminates such wrong predictions and produces logic circuits strictly equivalent to given Boolean functions. The main idea is a carefully designed decoding mechanism that builds a circuit step-by-step by generating tokens, which has beneficial "cutoff properties" that block a candidate token once it invalidate equivalence. In such a way, the proposed model works similar to typical LLMs while logical equivalence is strictly preserved. A Markov decision process formulation is also proposed for optimizing certain objectives of circuits. Experimentally, we trained an 88-million-parameter Circuit Transformer to generate equivalent yet more compact forms of input circuits, outperforming existing neural approaches on both synthetic and real world benchmarks, without any violation of equivalence constraints.
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
Authors: Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, Hao Zhou
Date: 2024-03-05 00:00:00
Tasks: All, Language Modeling, Language Modelling, Protein Language Model

Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pre-training on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins. The source codes of ESM-AA are publicly released at https://github.com/zhengkangjie/ESM-AA.
Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
Authors: Timothée Ly, Julien Ferry, Marie-José Huguet, Sébastien Gambs, Ulrich Aivodji
Date: 2024-03-18 00:00:00
Tasks: Sensitivity

Differentially-private (DP) mechanisms can be embedded into the design of a machine learning algorithm to protect the resulting model against privacy leakage. However, this often comes with a significant loss of accuracy due to the noise added to enforce DP. In this paper, we aim at improving this trade-off for a popular class of machine learning algorithms leveraging the Gini impurity as an information gain criterion to greedily build interpretable models such as decision trees or rule lists. To this end, we establish the smooth sensitivity of the Gini impurity, which can be used to obtain thorough DP guarantees while adding noise scaled with tighter magnitude. We illustrate the applicability of this mechanism by integrating it within a greedy algorithm producing rule list models, motivated by the fact that such models remain understudied in the DP literature. Our theoretical analysis and experimental results confirm that the DP rule lists models integrating smooth sensitivity have higher accuracy that those using other DP frameworks based on global sensitivity, for identical privacy budgets.
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis
Authors: Md. Ashraf Uddin, Sunil Aryal, Mohamed Reda Bouadjenek, Muna Al-Hawawreh, Md. Alamin Talukder
Date: 2024-03-17 00:00:00
Tasks: Classification, Intrusion Detection, Multi-class Classification

With the increased use of network technologies like Internet of Things (IoT) in many real-world applications, new types of cyberattacks have been emerging. To safeguard critical infrastructures from these emerging threats, it is crucial to deploy an Intrusion Detection System (IDS) that can detect different types of attacks accurately while minimizing false alarms. Machine learning approaches have been used extensively in IDS and they are mainly using flat multi-class classification to differentiate normal traffic and different types of attacks. Though cyberattack types exhibit a hierarchical structure where similar granular attack subtypes can be grouped into more high-level attack types, hierarchical classification approach has not been explored well. In this paper, we investigate the effectiveness of hierarchical classification approach in IDS. We use a three-level hierarchical classification model to classify various network attacks, where the first level classifies benign or attack, the second level classifies coarse high-level attack types, and the third level classifies a granular level attack types. Our empirical results of using 10 different classification algorithms in 10 different datasets show that there is no significant difference in terms of overall classification performance (i.e., detecting normal and different types of attack correctly) of hierarchical and flat classification approaches. However, flat classification approach misclassify attacks as normal whereas hierarchical approach misclassify one type of attack as another attack type. In other words, the hierarchical classification approach significantly minimises attacks from misclassified as normal traffic, which is more important in critical systems.