Convolutional and recurrent architechtures made it possible to build effective models working on new data types such as images and sequential data. What is next?
Kindly be explicit in your question. What do you want to know next?
I have seen some papers coming through on Spike Neural Networks, which seems an interesting new architecture for dynamic processes; however, whether it will take off is another issue as there are technological challenges, plus the technology requires time to mature.
Preprint Spiking Neural Networks and online learning: An overview and...
Dear Kudirat, actually Tim got the message right, sorry for an absence of details, I just had an aim to open the discussion almost from scratch, not to narrow down other scientists.
Gflownets- https://medium.com/aiguys/gflownets-the-next-big-idea-in-ai-d2ad78e3a88f
The advancement of convolutional neural networks (CNNs) for images and recurrent neural networks (RNNs) for sequential data has been transformative in deep learning. As we look forward, several exciting directions are emerging in the field of machine learning and neural architectures:
These directions represent just a fraction of the ongoing research in the field. The future of deep learning and neural architectures will likely involve interdisciplinary collaboration, addressing real-world challenges, and pushing the boundaries of what's possible with AI.
The advancement of convolutional neural networks (CNNs) for images and recurrent neural networks (RNNs) for sequential data has been transformative in deep learning. As we look forward, several exciting directions are emerging in the field of machine learning and neural architectures:
These directions represent just a fraction of the ongoing research in the field. The future of deep learning and neural architectures will likely involve interdisciplinary collaboration, addressing real-world challenges, and pushing the boundaries of what's possible with AI.
- Badges
- Science topic