A review of the gumbel-max trick and its extensions for discrete stochasticity in machine learning
IAM Huijben, W Kool, MB Paulus… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by
its unnormalized (log-) probabilities. Over the past years, the machine learning community …
its unnormalized (log-) probabilities. Over the past years, the machine learning community …
Artificial intelligence (AI)—it's the end of the tox as we know it (and I feel fine)
N Kleinstreuer, T Hartung - Archives of Toxicology, 2024 - Springer
The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has
the potential to transform chemical safety evaluation. Toxicology has evolved from an …
the potential to transform chemical safety evaluation. Toxicology has evolved from an …
Transfer learning in deep reinforcement learning: A survey
Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …
problems. Recent years have witnessed remarkable progress in reinforcement learning …
Attention, please! A survey of neural attention models in deep learning
A de Santana Correia, EL Colombini - Artificial Intelligence Review, 2022 - Springer
In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …
limited ability to process competing sources, attention mechanisms select, modulate, and …
Visual dialog
We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful
dialog with humans in natural, conversational language about visual content. Specifically …
dialog with humans in natural, conversational language about visual content. Specifically …
Evolutionary generative adversarial networks
Generative adversarial networks (GANs) have been effective for learning generative models
for real-world data. However, accompanied with the generative tasks becoming more and …
for real-world data. However, accompanied with the generative tasks becoming more and …
Visual coreference resolution in visual dialog using neural module networks
Visual dialog entails answering a series of questions grounded in an image, using dialog
history as context. In addition to the challenges found in visual question answering (VQA) …
history as context. In addition to the challenges found in visual question answering (VQA) …
Discriminability objective for training descriptive captions
One property that remains lacking in image captions generated by contemporary methods is
discriminability: being able to tell two images apart given the caption for one of them. We …
discriminability: being able to tell two images apart given the caption for one of them. We …
Two causal principles for improving visual dialog
This paper unravels the design tricks adopted by us, the champion team MReaL-BDAI, for
Visual Dialog Challenge 2019: two causal principles for improving Visual Dialog (VisDial) …
Visual Dialog Challenge 2019: two causal principles for improving Visual Dialog (VisDial) …
Utc: A unified transformer with inter-task contrastive learning for visual dialog
Visual Dialog aims to answer multi-round, interactive questions based on the dialog history
and image content. Existing methods either consider answer ranking and generating …
and image content. Existing methods either consider answer ranking and generating …