Beyond mimicking under-represented emotions: deep data augmentation with emotional subspace constraints for EEG-based emotion recognition
In recent years, using Electroencephalography (EEG) to recognize emotions has garnered
considerable attention. Despite advancements, limited EEG data restricts its potential. Thus …
considerable attention. Despite advancements, limited EEG data restricts its potential. Thus …
In defense of core-set: A density-aware core-set selection for active learning
Active learning enables the efficient construction of a labeled dataset by labeling informative
samples from an unlabeled dataset. In a real-world active learning scenario, the use of …
samples from an unlabeled dataset. In a real-world active learning scenario, the use of …
AIMEE: An Exploratory Study of How Rules Support AI Developers to Explain and Edit Models
In real-world applications when deploying Machine Learning (ML) models, initial model
development includes close analysis of the model results and behavior by a data scientist …
development includes close analysis of the model results and behavior by a data scientist …
Understanding deep learning via decision boundary
This article discovers that the neural network (NN) with lower decision boundary (DB)
variability has better generalizability. Two new notions, algorithm DB variability and-data DB …
variability has better generalizability. Two new notions, algorithm DB variability and-data DB …
Artificial Intelligence‐Assisted Decision‐Making Method for Legal Judgment Based on Deep Neural Network
W Ma - Mobile Information Systems, 2022 - Wiley Online Library
With the arrival of the third revolution of artificial intelligence, the applications of artificial
intelligence in the fields of automatic driving, image recognition, smart home, machine …
intelligence in the fields of automatic driving, image recognition, smart home, machine …
PICE: Polyhedral Complex Informed Counterfactual Explanations
Polyhedral geometry can be used to shed light on the behaviour of piecewise linear neural
networks, such as ReLU-based architectures. Counterfactual explanations are a popular …
networks, such as ReLU-based architectures. Counterfactual explanations are a popular …
Linking in Style: Understanding learned features in deep learning models
Convolutional neural networks (CNNs) learn abstract features to perform object
classification, but understanding these features remains challenging due to difficult-to …
classification, but understanding these features remains challenging due to difficult-to …
Optimizing few-shot learning based on variational autoencoders
Despite the importance of few-shot learning, the lack of labeled training data in the real
world makes it extremely challenging for existing machine learning methods because this …
world makes it extremely challenging for existing machine learning methods because this …
Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoods
The black-box nature of machine learning models limits their use in case-critical
applications, raising faithful and ethical concerns that lead to trust crises. One possible way …
applications, raising faithful and ethical concerns that lead to trust crises. One possible way …
Dataset Ownership Verification in Contrastive Pre-trained Models
High-quality open-source datasets, which necessitate substantial efforts for curation, has
become the primary catalyst for the swift progress of deep learning. Concurrently, protecting …
become the primary catalyst for the swift progress of deep learning. Concurrently, protecting …