Research Papers Attention Is All You Need: Paper Summary and Insights In 2017, Vaswani et al. published a groundbreaking paper titled "Attention Is All You Need" at the Neural Information Processing Systems (NeurIPS) conference. This article at OpenGenus summarizes this paper and present the key insights.
Machine Learning (ML) Concrete Problems in AI Safety In this article we shall explore a research paper titled “Concrete Problems in AI Safety” by Dario Amodei and others. This has been a very influential paper.
Machine Learning (ML) Adversarial Sample Transferability in Machine Learning: Attacks We have discussed about what adversarial machine learning is and what transferability attacks are. The ideas are from Ian Goodfellow.
Machine Learning (ML) Can Machine Learning (ML) be secure? We answer the question "Can Machine Learning (ML) or Artificial Intelligence (AI) be secure?". We explore different types of attacks on Machine learning, defense strategies and more
Machine Learning (ML) Summary: The Case for Learned Index Structures This outlines and evaluates the potential of a new approach to build indexes that is the merge of Algorithms/ Data Structures and Machine Learning.
Machine Learning (ML) Adversarial Examples are not Bugs but are Features of AI Adversarial Vulnerability is a direct result of our models' sensitivity to well-generalizing features in the data. We explored the research paper.
Machine Learning (ML) The Indirect Convolution Algorithm Indirect Convolution is as efficient as the GEMM primitive without the overhead of im2col transformations - instead of reshuffling the data, an indirection buffer is introduced.
Machine Learning (ML) Summary: Learning to See by Moving Research question: Is it also possible to learn useful features for a diverse set of visual tasks using any other form of supervision?
Machine Learning (ML) Summary: High Performance Convolutional Neural Networks for Document Processing We have explored the paper "High Performance Convolutional Neural Networks for Document Processing" by Microsoft Research. It explores techniques to compute convolution layer in CNN faster.
Machine Learning (ML) Understanding Deep Image Representations by Inverting Them We have explore the problem that given an encoding of an image, to which extent is it possible to reconstruct the image itself? We have explored the paper "Understanding Deep Image Representations by Inverting Them".