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Prabhu Prakash Kagitha

ICLR 2021: Key Research Papers

May 4, 2021 by Prabhu Prakash Kagitha

ICLR 2021 research papers

This year, the International Conference on Learning Representations (ICLR) takes place virtually from May 3rd through May 7th. As usual, it is a premier gathering of professionals researching various deep learning topics with applications in computer vision, natural language processing, speech recognition, robotics, and other fields.  To help you stay aware of the … [Read more...] about ICLR 2021: Key Research Papers

AAAI 2021: Top Research Papers With Business Applications

April 12, 2021 by Prabhu Prakash Kagitha

ErnIe-ViL AAAI 2021

With a record high of 9034 research papers submitted to AAAI 2021 and an acceptance rate of 21%, a total of 1692 papers were presented at the conference. As usual, the Outstanding and Distinguished Papers awards were given to the papers exemplifying the highest standards in technical contribution and exposition. Of course, there were many more papers presented at the conference … [Read more...] about AAAI 2021: Top Research Papers With Business Applications

NeurIPS 2020 Papers: Takeaways for a Deep Learning Engineer –  Computer Vision

January 7, 2021 by Prabhu Prakash Kagitha

NeurIPS 2020 papers

As mentioned in part 1– the most important thing:) – I went through all the titles of NeurIPS 2020 papers (more than 1900!) and read abstracts of 175 papers, and extracted DL engineer relevant insights from the following papers. This is part 2. See part 1 here. If this in-depth educational content is useful for you, you can subscribe to our AI research mailing list to be … [Read more...] about NeurIPS 2020 Papers: Takeaways for a Deep Learning Engineer –  Computer Vision

NeurIPS 2020 Papers: Takeaways for a Deep Learning Engineer

January 6, 2021 by Prabhu Prakash Kagitha

NeurIPS 2020 papers

Advances in Deep Learning research are of great utility for a Deep Learning engineer working on real-world problems as most of the Deep Learning research is empirical with validation of new techniques and theories done on datasets that closely resemble real-world datasets/tasks (ImageNet pre-trained weights are still useful!). But, churning a vast amount of … [Read more...] about NeurIPS 2020 Papers: Takeaways for a Deep Learning Engineer

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