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Found 27 papers, 9 papers with code
Effector: A Python package for regional explanations
Effector implements well-established global effect methods, assesses the heterogeneity of each method and, based on that, provides regional effects.
C-XGBoost: A tree boosting model for causal effect estimation
Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data.
Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning
Despite the recent increase in research activity, deep-learning models have not yet been widely accepted in several real-world settings, such as medicine.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Consequently, RAMs fit one component per subregion of each feature instead of one component per feature.
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
RHALE quantifies the heterogeneity by considering the standard deviation of the local effects and automatically determines an optimal variable-size bin-splitting.
Multiscale and Multilayer Contrastive Learning for Domain Generalization
During the past decade, deep neural networks have led to fast-paced progress and significant achievements in computer vision problems, for both academia and industry.
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques
To boost the performance of these base models, we propose a feature-bagging technique that considers only a subset of features at a time, and we further apply a transformation that is based on nested rotation computed from Principal Component Analysis (PCA) to improve the effectiveness and generalization of the approach.
Towards Fair Face Verification: An In-depth Analysis of Demographic Biases
This paper presents an in-depth analysis, with a particular emphasis on the intersectionality of these demographic factors.
CNN Feature Map Augmentation for Single-Source Domain Generalization
In search of robust and generalizable machine learning models, Domain Generalization (DG) has gained significant traction during the past few years.
Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation
The proposed NNCI methodology is applied to some of the most well established neural network-based models for treatment effect estimation with the use of observational data.
FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations
To overcome these limitations, this work introduces FLAC, a methodology that minimizes mutual information between the features extracted by the model and a protected attribute, without the use of attribute labels.
Ranked #1 on
HairColor/Bias-conflicting
on CelebA
CNNs with Multi-Level Attention for Domain Generalization
In the present work, we focus on this problem of Domain Generalization and propose an alternative neural network architecture for robust, out-of-distribution image classification.
Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
Our objective in this work is to propose a benchmark for evaluating DG algorithms, in addition to introducing a novel architecture for tackling DG in biosignal classification.
Partially Oblivious Neural Network Inference
Oblivious inference is the task of outsourcing a ML model, like neural-networks, without disclosing critical and sensitive information, like the model's parameters.
DALE: Differential Accumulated Local Effects for efficient and accurate global explanations
In this paper, we propose a novel ALE approximation, called Differential Accumulated Local Effects (DALE), which can be used in cases where the ML model is differentiable and an auto-differentiable framework is accessible.
An evaluation framework for comparing causal inference models
In this paper, we propose to complement the evaluation of causal inference models using concrete statistical evidence, including the performance profiles of Dolan and Mor{\'e}, as well as non-parametric and post-hoc statistical tests.
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds
The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome.
A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks
Deep Learning systems have achieved great success in the past few years, even surpassing human intelligence in several cases.
Multi-layer Representation Learning for Robust OOD Image Classification
Convolutional Neural Networks have become the norm in image classification.
Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned
The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurement of in-meal eating behavior.
An improved neural network model for treatment effect estimation
Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions.
Self-Supervised Feature Learning of 1D Convolutional Neural Networks with Contrastive Loss for Eating Detection Using an In-Ear Microphone
A common bottleneck for developing and training machine learning algorithms is obtaining labeled data for training supervised algorithms, and in particular ground truth annotations.
Recognition of food-texture attributes using an in-ear microphone
Our approach performs very well in recognizing crispiness (0. 95 weighted accuracy on new subjects and 0. 93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness.
A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches
The increased worldwide prevalence of obesity has sparked the interest of the scientific community towards tools that objectively and automatically monitor eating behavior.
Span error bound for weighted SVM with applications in hyperparameter selection
Experiments on 14 benchmark data sets and data sets with importance scores for the training instances show that: (a) the condition for the existence of span in weighted SVM is satisfied almost always; (b) the span-rule is the most effective method for weighted SVM hyperparameter selection; (c) the span-rule is the best predictor of the test error in the mean square error sense; and (d) the span-rule is efficient and, for certain problems, it can be calculated faster than $K$-fold cross-validation.
Learning Local Feature Aggregation Functions with Backpropagation
This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem).
