Decoding subjective emotional arousal during a naturalistic VR experience from EEG using LSTMs
Emotional arousal (EA) denotes a heightened state of activation that has both subjective and
physiological aspects. The neurophysiology of subjective EA, among other mind-brain-body …
physiological aspects. The neurophysiology of subjective EA, among other mind-brain-body …
How do convolutional neural networks learn design?
In this paper, we aim to understand the design principles in book cover images which are
carefully crafted by experts. Book covers are designed in a unique way, specific to genres …
carefully crafted by experts. Book covers are designed in a unique way, specific to genres …
Pyramidal recurrent unit for language modeling
S Mehta, R Koncel-Kedziorski, M Rastegari… - arXiv preprint arXiv …, 2018 - arxiv.org
LSTMs are powerful tools for modeling contextual information, as evidenced by their
success at the task of language modeling. However, modeling contexts in very high …
success at the task of language modeling. However, modeling contexts in very high …
Structuring neural networks for more explainable predictions
Abstract Machine learning algorithms such as neural networks are more useful, when their
predictions can be explained, eg in terms of input variables. Often simpler models are more …
predictions can be explained, eg in terms of input variables. Often simpler models are more …
Learning explanations from language data
arXiv:1808.04127v1 [cs.CL] 13 Aug 2018 Page 1 arXiv:1808.04127v1 [cs.CL] 13 Aug 2018
2018 EMNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) …
2018 EMNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) …
Iterative recursive attention model for interpretable sequence classification
Natural language processing has greatly benefited from the introduction of the attention
mechanism. However, standard attention models are of limited interpretability for tasks that …
mechanism. However, standard attention models are of limited interpretability for tasks that …
Dual recurrent attention units for visual question answering
Visual Question Answering (VQA) requires AI models to comprehend data in two domains,
vision and text. Current state-of-the-art models use learned attention mechanisms to extract …
vision and text. Current state-of-the-art models use learned attention mechanisms to extract …
Layer-wise relevance propagation for explainable recommendations
H Bharadhwaj - arXiv preprint arXiv:1807.06160, 2018 - arxiv.org
In this paper, we tackle the problem of explanations in a deep-learning based model for
recommendations by leveraging the technique of layer-wise relevance propagation. We use …
recommendations by leveraging the technique of layer-wise relevance propagation. We use …
Visualizing and understanding deep neural networks in CTR prediction
L Guo, H Ye, W Su, H Liu, K Sun, H Xiang - arXiv preprint arXiv …, 2018 - arxiv.org
Although deep learning techniques have been successfully applied to many tasks,
interpreting deep neural network models is still a big challenge to us. Recently, many works …
interpreting deep neural network models is still a big challenge to us. Recently, many works …
Detecting diabetes risk from social media activity
This work explores the detection of individuals' risk of type 2 diabetes mellitus (T2DM)
directly from their social media (Twitter) activity. Our approach extends a deep learning …
directly from their social media (Twitter) activity. Our approach extends a deep learning …