Explaining deep neural networks and beyond: A review of methods and applications
With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
Explainable AI: A review of applications to neuroimaging data
Deep neural networks (DNNs) have transformed the field of computer vision and currently
constitute some of the best models for representations learned via hierarchical processing in …
constitute some of the best models for representations learned via hierarchical processing in …
Edge learning using a fully integrated neuro-inspired memristor chip
Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …
scenes and owners. Current technologies for training neural networks require moving …
What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …
A survey on explainable artificial intelligence (xai): Toward medical xai
Recently, artificial intelligence and machine learning in general have demonstrated
remarkable performances in many tasks, from image processing to natural language …
remarkable performances in many tasks, from image processing to natural language …
Unmasking Clever Hans predictors and assessing what machines really learn
Current learning machines have successfully solved hard application problems, reaching
high accuracy and displaying seemingly intelligent behavior. Here we apply recent …
high accuracy and displaying seemingly intelligent behavior. Here we apply recent …
Mixed-modality speech recognition and interaction using a wearable artificial throat
Researchers have recently been pursuing technologies for universal speech recognition
and interaction that can work well with subtle sounds or noisy environments. Multichannel …
and interaction that can work well with subtle sounds or noisy environments. Multichannel …
Speaker gender recognition based on deep neural networks and ResNet50
AA Alnuaim, M Zakariah, C Shashidhar… - Wireless …, 2022 - Wiley Online Library
Several speaker recognition algorithms failed to get the best results because of the wildly
varying datasets and feature sets for classification. Gender information helps reduce this …
varying datasets and feature sets for classification. Gender information helps reduce this …
[HTML][HTML] A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Current deep learning methods are regarded as favorable if they empirically perform well on
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …
Ecosystem-level analysis of deployed machine learning reveals homogeneous outcomes
C Toups, R Bommasani, K Creel… - Advances in …, 2024 - proceedings.neurips.cc
Abstract Machine learning is traditionally studied at the model level: researchers measure
and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific …
and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific …