First place in ABC 2018: Classify bird gender from GPS trajectories.
problem Predicting the gender of shearwaters from GPS navigation data.
method Ensemble of Gradient Boosting Classifiers (CatBoost, LightGBM, XGBoost) with feature engineering.
result Ranked first among 74 teams in the Animal Behavior Challenge.
Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed metho…
System accurately identifies birds in real-world settings.
problem Identifying birds in diverse, realistic environments.
method Trained kNN and SVM classifiers on crowd-sourced audio data.
result Both classifiers perform similarly, with kNN offering flexibility.
Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of different species. However, few works have considered the multi-label structure in birdsong. We propose to use an ensemble of classifier chain…
Paper proposes GN-GloVe to learn gender-neutral word embeddings.
problem Inherit strong gender stereotypes in embeddings trained on human-generated corpora.
method Proposes a novel training procedure to isolate gender information in word vectors.
result GN-GloVe successfully isolates gender information without sacrificing functionality.
Early-bird tickets can be identified early in training, reducing costs.
problem Costly deep network training.
method Low-cost training schemes (early stopping, low-precision) and mask distance.
result Efficient training methods using EB tickets achieve up to 4.7x energy savings.
Biodiversity monitoring using audio recordings is achievable at a truly global scale via large-scale deployment of inexpensive, unattended recording stations or by large-scale crowdsourcing using recording and species recognition on mobile devices. The ability, however, to reliably identify vocalising animal species is…
Study uses deep learning to predict gender and analyze HPV vaccine perceptions on Twitter.
problem Analyzing gender differences in public perceptions on HPV vaccine using social media data.
method Convolutional neural network model trained on Twitter text for gender prediction, then applied to HPV vaccine related tweets.
result Identified gender differences in public perceptions on HPV vaccine, consistent with previous studies.
Develops counterfactual visual explanations to show how images could change to classify differently.
problem Creating understandable explanations for vision system predictions.
method Selects a distractor image and identifies spatial regions to modify for different classification.
result Users trained with counterfactual explanations perform better in fine-grained bird classification.
Computer Vision and machine learning methods were previously used to reveal screen presence of genders in TV and movies. In this work, using head pose, gender detection, and skin color estimation techniques, we demonstrate that the gender disparity in TV in a South Asian country such as Bangladesh exhibits unique chara…
Study shows gender bias in occupation classification tasks.
problem Gender bias in machine learning for occupation classification.
method Analyzed impact of explicit gender indicators in semantic representations of biographies.
result True positive rates differ between genders, correlating with existing gender imbalances.
Deep learning improves gender classification from handwriting.
problem Classifying gender from handwritten text.
method Convolutional Neural Network (CNN) for feature extraction and gender classification.
result Deep learning approach outperforms human examiners in gender classification accuracy.
Novel algorithm improves poultry welfare by classifying chicken behaviors.
problem Classifying chicken behaviors from sparse and noisy data.
method Time series dictionary learning algorithm.
result Robustly learns from weakly labeled data sources.
Optimal transport learns Riemannian metrics for evolving probability measures.
problem Learning metrics for evolving probability measures on Riemannian manifolds.
method Neural parametrization of a metric tensor via optimal transport, alternating optimization scheme.
result Improved trajectory inference on scRNA and bird migration data.
Study evaluates gender bias in relation extraction systems.
problem Gender bias in relation extraction systems.
method Created WikiGenderBias dataset, evaluated systems for bias, analyzed bias mitigation techniques.
result NRE systems exhibit gender bias in predictions.
Concept bottleneck models enable concept manipulation for model interpretation.
problem Training models to directly predict labels without intermediate concepts.
method Train models to predict intermediate concepts at training time, then use these concepts to predict labels.
result Concept bottleneck models achieve competitive accuracy with standard models while enabling concept manipulation.
Improved neural model predicts gender from tweets.
problem Predicting gender from Twitter text.
method RNN model with attention, LSA-reduced n-gram features.
result Improved model achieves state-of-the-art performance on English tweets.
Reduces gender classification bias by learning race-invariant face representations.
problem Societal bias in gender recognition systems.
method Adversarially trained autoencoder model to learn race-invariant face representations.
result Achieved a significant drop of over 40% in racial bias surrogate metric with race invariant representations.
Study predicts gender from brain FC at multiple scales using deep learning and Bayesian methods.
problem Predicting gender from brain functional connectivity.
method Deep learning and Bayesian deep learning applied to brain FC data from 1003 healthy adults.
result Bayesian deep learning provides accurate predictions and uncertainty information.
Debiasing techniques can worsen gender bias in text classification, but a tweak improves both.
problem Debiasing techniques can inadvertently increase gender bias in text classification.
method Investigated traditional debiasing techniques and found they worsen bias. Suggested a minor adjustment.
result A minor adjustment to debiasing techniques can reduce gender bias while maintaining high classification accuracy.
Study quantifies gender bias in language models across 7 languages.
problem Measuring gender bias in language models across multiple languages.
method Curated dataset of politicians, multilingual language models, probing language models.
result Larger language models do not show significant gender bias compared to smaller ones.
Study measures gender bias in machine translation using multiple reference points.
problem Measuring and identifying gender bias in machine translation.
method Used an optimal non-biased translator, reference points from occupational statistics and survey.
result Found bias against both genders, but more against women, and found occupations have a greater effect than adjectives.
The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings traine…
New method neutralizes gender bias in word embeddings without losing semantic information.
problem Gender biases in word embeddings trained on human-generated corpora.
method Latent Disentanglement and Counterfactual Generation with siamese auto-encoder and gradient reversal layer.
result Our method outperforms existing debiasing methods in preserving semantic information and neutralizing gender biases.
This study examines gender bias in Dutch newspapers from 1950-1990 using word embeddings.
problem Examining gender bias in historical newspapers.
method Word embeddings to measure bias changes over time.
result Clear differences in gender bias and changes within newspapers over time.
Paper improves gender detection on social media using deep learning.
problem Traditional classifiers struggle with social media data volume.
method Ensemble deep learning with multi-model architectures.
result Improved gender detection accuracy on social media posts.
Machine learning improves risk assessment for gender-based violence victims.
problem Accurately predicting recidivism risk in gender-based crime victims.
method Applied machine learning techniques to create models predicting recidivism risk.
result Proposed ML method outperforms classical statistical methods.
Project improves gender-balanced pronoun resolution with BERT.
problem Challenging task in natural language understanding, especially for gendered pronouns.
method BERT-based approach to gender-balanced pronoun resolution.
result Achieved 92% F1 score with lower gender bias.
Study confirms improved performance of Self-Critique and Adapt method.
problem Improving performance of MAML++ method.
method Self-Critique and Adapt (SCA) method.
result SCA method improves performance of MAML++.
Predicts user age and gender on Tumblr using rich content.
problem Challenges in targeting specific demographic groups on Tumblr.
method Graph based and deep learning models, including network embedding, label propagation, CNN, and MLP.
result Significantly improved accuracy for age and gender predictions.
Paper finds gender classification accuracy varies by skin type, not ethnicity.
problem Unequal performance of face classification services across skin types and genders.
method Stability experiments, image manipulation, and post-hoc explanation techniques.
result Lip, eye, and cheek structure differences, not skin type, cause gender classification discrepancies.
Develops NFCF to reduce gender bias in social media recommendation systems.
problem Reduces gender bias in collaborative filtering systems on social media data.
method Pre-training and fine-tuning neural collaborative filtering with bias correction techniques.
result Achieves better performance and fairness in gender de-biased recommendations.
The gender wage gap varies significantly among women based on socio-economic characteristics.
problem Understanding the extent of gender inequality in earnings among U.S. women.
method High-dimensional wage regression and double lasso analysis of 2016 American Community Survey data.
result The gender wage gap varied substantially across women and was driven by marital status, having children, race, occupation, industry, and educational attainment.
Machine learning algorithms are optimized to model statistical properties of the training data. If the input data reflects stereotypes and biases of the broader society, then the output of the learning algorithm also captures these stereotypes. In this paper, we initiate the study of gender stereotypes in {\em word emb…
Predicts gender and age from mobile phone data for marketing.
problem Enhance marketing offers by predicting customer demographics.
method Machine learning algorithms applied to CDRs, CRM, and billing info.
result 85.6% accuracy in gender prediction, 65.5% in age prediction.
Reduces gender bias in patient notes while maintaining medical classification accuracy.
problem Bias in natural language processing of patient notes.
method Identifying and removing gendered language using BERT-based classifiers, then augmenting data to maintain performance.
result Minimal degradation in health condition classification tasks with data augmentation.
This text is intended to become in the long run Chapter 3 of our long saga dedicated to Riemann, Ahlfors and Rohlin. Yet, as its contents evolved as mostly independent (due to our inaptitude to interconnect both trends as strongly as we wished), it seemed preferable to publish it separately. More factually, our account…
Mitigates gender bias amplification in model predictions.
problem Gender bias amplification in model predictions.
method Posterior regularization to mitigate bias.
result Almost removes gender bias amplification in model predictions.
Dynamic weights improve multimodal emotion and gender recognition.
problem Improving performance in multiple classification tasks with a single model.
method Dynamic joint loss weights for multimodal emotion and gender recognition.
result Lower joint loss and better generalizability than static weights.
SCN learns and compensates for data bias in citizen science projects.
problem Data bias in citizen science datasets.
method End-to-end learning scheme that learns and compensates for data shift.
result SCN outperforms models that ignore data bias and handles massive high-dimensional data.
Study finds optimal board gender diversity for emissions performance.
problem Association between board gender diversity and emissions performance.
method Panel regressions, machine learning, explainable AI.
result Optimal board gender diversity for emissions performance is approximately 35%.
Study identifies clusters of EU countries with similar young mortality patterns.
problem Identify clusters of EU countries with similar mortality patterns in young population.
method Symbolic data analysis (SDA) with age, gender, and main causes of death dimensions.
result Identified clusters of EU countries with similar mortality patterns in young population.
New corpus improves coreference resolution by removing gender and number cues.
problem Challenges in resolving ambiguous pronoun references.
method Developed a new annotated corpus, introduced antecedent switching technique.
result Models perform poorly on ambiguous pronoun references, but antecedent switching improves performance.
Paper proposes a new classifier for gender detection in mobile telematics.
problem Detecting gender through mobile telematics data.
method Choquet fuzzy integral vertical bagging classifier combining random forest and rough set theory.
result Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.
Develops fair machine learning models resistant to sensitive perturbations.
problem Ensuring model performance is invariant to sensitive attributes like gender and ethnicity.
method Distributionally robust optimization to enforce individual fairness.
result Demonstrates effectiveness on tasks prone to bias.
Study proposes an alternative method to measure societal biases using smoothed co-occurrence relations.
problem Measuring societal biases using word embeddings can introduce irrelevant concepts.
method Proposes an alternative approach using smoothed first-order co-occurrence relations.
result First-order approach shows higher correlations with actual gender bias statistics.
Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Speci…
Study improves animal audio classification using data augmentation.
problem Improving automated animal audio classification accuracy.
method Exploits different data augmentation techniques for training CNNs.
result Best recognition rates on animal audio classification datasets.