Hacker News
- Technical Debt in Machine Learning Systems (2015) [pdf] http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf 5 comments
- Compiler Auto-Vectorization with Imitation Learning [pdf] http://papers.nips.cc/paper/9604-compiler-auto-vectorization-with-imitation-learning.pdf 12 comments
- Foundations for Efficient and Expressive Differentiable Programming [pdf] http://papers.nips.cc/paper/8221-backpropagation-with-callbacks-foundations-for-efficient-and-expressive-differentiable-programming.pdf 14 comments
- New Hardware for Massive Neural Networks (1988) [pdf] https://papers.nips.cc/paper/22-new-hardware-for-massive-neural-networks.pdf 8 comments
- Attention Is All You Need https://papers.nips.cc/paper/7181-attention-is-all-you-need 30 comments
- Teaching Machines to Read and Comprehend [pdf] http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf 3 comments
- Hidden Technical Debt in Machine Learning Systems (2015) [pdf] http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf 2 comments
- 2013 NIPS Proceedings – Advances in Neural Information Processing Systems http://papers.nips.cc/book/advances-in-neural-information-processing-systems-26-2013 18 comments
- [Q] - what does "regret" in model selection mean? https://papers.nips.cc/paper/2019/file/433371e69eb202f8e7bc8ec2c8d48021-Paper.pdf 5 comments reinforcementlearning
- Alexnet paper explanation of the dataset preparation https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf 4 comments deeplearning
- Double Q-Learning http://papers.nips.cc/paper/3964-double-q-learning.pdf 4 comments reinforcementlearning
- Sources on Bayesian Deep Learning? https://papers.nips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision 6 comments deeplearning
- [R] The Importance of Sampling in Meta-Reinforcement Learning http://papers.nips.cc/paper/8140-the-importance-of-sampling-inmeta-reinforcement-learning.pdf 5 comments reinforcementlearning