The state of the art in enhancing trust in machine learning models with the use of visualizations
Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …
State of the art of visual analytics for explainable deep learning
The use and creation of machine‐learning‐based solutions to solve problems or reduce
their computational costs are becoming increasingly widespread in many domains. Deep …
their computational costs are becoming increasingly widespread in many domains. Deep …
[HTML][HTML] Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …
outperforming human performance at certain tasks. There is no doubt that AI is important to …
Causalm: Causal model explanation through counterfactual language models
Understanding predictions made by deep neural networks is notoriously difficult, but also
crucial to their dissemination. As all machine learning–based methods, they are as good as …
crucial to their dissemination. As all machine learning–based methods, they are as good as …
The impact of multiple parallel phrase suggestions on email input and composition behaviour of native and non-native english writers
We present an in-depth analysis of the impact of multi-word suggestion choices from a
neural language model on user behaviour regarding input and text composition in email …
neural language model on user behaviour regarding input and text composition in email …
Investigating gender bias in language models using causal mediation analysis
Many interpretation methods for neural models in natural language processing investigate
how information is encoded inside hidden representations. However, these methods can …
how information is encoded inside hidden representations. However, these methods can …
A unified understanding of deep nlp models for text classification
The rapid development of deep natural language processing (NLP) models for text
classification has led to an urgent need for a unified understanding of these models …
classification has led to an urgent need for a unified understanding of these models …
Attention flows: Analyzing and comparing attention mechanisms in language models
JF DeRose, J Wang, M Berger - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Advances in language modeling have led to the development of deep attention-based
models that are performant across a wide variety of natural language processing (NLP) …
models that are performant across a wide variety of natural language processing (NLP) …
Visual human–computer interactions for intelligent vehicles and intelligent transportation systems: The state of the art and future directions
Research on intelligent vehicles has been popular in the past decade. To fill the gap
between automatic approaches and man-machine control systems, it is indispensable to …
between automatic approaches and man-machine control systems, it is indispensable to …
Sequence-to-sequence learning with latent neural grammars
Y Kim - Advances in Neural Information Processing …, 2021 - proceedings.neurips.cc
Sequence-to-sequence learning with neural networks has become the de facto standard for
sequence modeling. This approach typically models the local distribution over the next …
sequence modeling. This approach typically models the local distribution over the next …