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Found 9 papers, 1 papers with code
A Learning Paradigm for Interpretable Gradients
This paper studies interpretability of convolutional networks by means of saliency maps.
Opti-CAM: Optimizing saliency maps for interpretability
Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps.
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
Recent advances in vision-and-language modeling have seen the development of Transformer architectures that achieve remarkable performance on multimodal reasoning tasks.
ChaLearn Looking at People: Inpainting and Denoising challenges
Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence.
Sparse matrix products for neural network compression
Over-parameterization of neural networks is a well known issue that comes along with their great performance.
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach
Moreover, we show how the process provides interesting insights toward the behavior of RNN learned on data, enlarging the scope of this work to the one of explainability of deep learning models.
Explaining Black Boxes on Sequential Data using Weighted Automata
Understanding how a learned black box works is of crucial interest for the future of Machine Learning.
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Explainability and interpretability are two critical aspects of decision support systems.
