Data Science at Home

Join the discussion on our Discord server In this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs. The problem is not as trivial as it seems. Many researchers have failed in generating large images with GANs before. One interesting application of such approach is in medicine for the generation of CT and X-ray images. Enjoy the show!   References Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images https://arxiv.org/abs/1907.01376

Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture. Such architecture is built on top of another important concept already known to the community: self-attention. In this episode I explain what these mechanisms are, how they work and why they are so powerful. Don't forget to subscribe to our Newsletter or join the discussion on our Discord server   References Attention is all you need  https://arxiv.org/abs/1706.03762 The illustrated transformer  https://jalammar.github.io/illustrated-transformer Self-attention for generative models  http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture14-transformers.pdf

Join the discussion on our Discord server   In this episode, I am with Aaron Gokaslan, computer vision researcher, AI Resident at Facebook AI Research. Aaron is the author of OpenGPT-2, a parallel NLP model to the most discussed version that OpenAI decided not to release because too accurate to be published. We discuss about image-to-image translation, the dangers of the GPT-2 model and the future of AI. Moreover, Aaron provides some very interesting links and demos that will blow your mind! Enjoy the show!  References Multimodal image to image translation (not all mentioned in the podcast but recommended by Aaron) Pix2Pix:  https://phillipi.github.io/pix2pix/   CycleGAN: https://junyanz.github.io/CycleGAN/   GANimorph Paper: https://arxiv.org/abs/1808.04325 Code: https://github.com/brownvc/ganimorph   UNIT:https://arxiv.org/abs/1703.00848 MUNIT:https://github.com/NVlabs/MUNIT DRIT: https://github.com/HsinYingLee/DRIT   GPT-2 and related  Try OpenAI's GPT-2: https://talktotransformer.com/ Blogpost: https://blog.usejournal.com/opengpt-2-we-replicated-gpt-2-because-you-can-too-45e34e6d36dc The Original Transformer Paper: https://arxiv.org/abs/1706.03762 Grover: The FakeNews generator and detector: https://rowanzellers.com/grover/

Join the discussion on our Discord server   After reinforcement learning agents doing great at playing Atari video games, Alpha Go, doing financial trading, dealing with language modeling, let me tell you the real story here. In this episode I want to shine some light on reinforcement learning (RL) and the limitations that every practitioner should consider before taking certain directions. RL seems to work so well! What is wrong with it?   Are you a listener of Data Science at Home podcast? A reader of the Amethix Blog?  Or did you subscribe to the Artificial Intelligence at your fingertips newsletter? In any case let’s stay in touch!  https://amethix.com/survey/     References Emergence of Locomotion Behaviours in Rich Environments  https://arxiv.org/abs/1707.02286 Rainbow: Combining Improvements in Deep Reinforcement Learning  https://arxiv.org/abs/1710.02298 AlphaGo Zero: Starting from scratch  https://deepmind.com/blog/article/alphago-zero-starting-scratch

Join the discussion on our Discord server   In this episode I have an amazing conversation with Jimmy Soni and Rob Goodman, authors of “A mind at play”, a book entirely dedicated to the life and achievements of Claude Shannon. Claude Shannon does not need any introduction. But for those who need a refresh, Shannon is the inventor of the information age.  Have you heard of binary code, entropy in information theory, data compression theory (the stuff behind mp3, mpg, zip, etc.), error correcting codes (the stuff that makes your RAM work well), n-grams, block ciphers, the beta distribution, the uncertainty coefficient? All that stuff has been invented by Claude Shannon :)    Articles:  https://medium.com/the-mission/10-000-hours-with-claude-shannon-12-lessons-on-life-and-learning-from-a-genius-e8b9297bee8f https://medium.com/the-mission/on-claude-shannons-103rd-birthday-here-are-103-memorable-claude-shannon-quotes-maxims-and-843de4c716cf?source=your_stories_page--------------------------- http://nautil.us/issue/51/limits/how-information-got-re_invented http://nautil.us/issue/50/emergence/claude-shannon-the-las-vegas-cheat   Claude's papers: https://medium.com/the-mission/a-genius-explains-how-to-be-creative-claude-shannons-long-lost-1952-speech-fbbcb2ebe07f http://www.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf   A mind at play (book links):  http://amzn.to/2pasLMz -- Hardcover https://amzn.to/2oCfVL0 -- Audio

Join the discussion on our Discord server As ML plays a more and more relevant role in many domains of everyday life, it’s quite obvious to see more and more attacks to ML systems. In this episode we talk about the most popular attacks against machine learning systems and some mitigations designed by researchers Ambra Demontis and Marco Melis, from the University of Cagliari (Italy). The guests are also the authors of SecML, an open-source Python library for the security evaluation of Machine Learning (ML) algorithms. Both Ambra and Marco are members of research group PRAlab, under the supervision of Prof. Fabio Roli.   SecML Contributors Marco Melis (Ph.D Student, Project Maintainer, https://www.linkedin.com/in/melismarco/) Ambra Demontis (Postdoc, https://pralab.diee.unica.it/it/AmbraDemontis)  Maura Pintor (Ph.D Student, https://it.linkedin.com/in/maura-pintor) Battista Biggio (Assistant Professor, https://pralab.diee.unica.it/it/BattistaBiggio) References SecML: an open-source Python library for the security evaluation of Machine Learning (ML) algorithms https://secml.gitlab.io/. Demontis et al., “Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks,” presented at the 28th USENIX Security Symposium (USENIX Security 19), 2019, pp. 321–338. https://www.usenix.org/conference/usenixsecurity19/presentation/demontis W. Koh and P. Liang, “Understanding Black-box Predictions via Influence Functions,” in International Conference on Machine Learning (ICML), 2017. https://arxiv.org/abs/1703.04730 Melis, A. Demontis, B. Biggio, G. Brown, G. Fumera, and F. Roli, “Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 751–759. https://arxiv.org/abs/1708.06939 Biggio and F. Roli, “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning,” Pattern Recognition, vol. 84, pp. 317–331, 2018. https://arxiv.org/abs/1712.03141 Biggio et al., “Evasion attacks against machine learning at test time,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Part III, 2013, vol. 8190, pp. 387–402. https://arxiv.org/abs/1708.06131 Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in 29th Int’l Conf. on Machine Learning, 2012, pp. 1807–1814. https://arxiv.org/abs/1206.6389 Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma, “Adversarial classification,” in Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, 2004, pp. 99–108. https://dl.acm.org/citation.cfm?id=1014066 Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. "Axiomatic attribution for deep networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017. https://arxiv.org/abs/1703.01365  Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Model-agnostic interpretability of machine learning." arXiv preprint arXiv:1606.05386 (2016). https://arxiv.org/abs/1606.05386 Guo, Wenbo, et al. "Lemna: Explaining deep learning based security applications." Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2018. https://dl.acm.org/citation.cfm?id=3243792 Bach, Sebastian, et al. "On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation." PloS one 10.7 (2015): E0130140. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140 

Join the discussion on our Discord server Scaling technology and business processes are not equal. Since the beginning of the enterprise technology, scaling software has been a difficult task to get right inside large organisations. When it comes to Artificial Intelligence and Machine Learning, it becomes vastly more complicated.  In this episode I propose a framework - in five pillars - for the business side of artificial intelligence.  

Join the discussion on our Discord server In this episode, I am with Aaron Gokaslan, computer vision researcher, AI Resident at Facebook AI Research. Aaron is the author of OpenGPT-2, a parallel NLP model to the most discussed version that OpenAI decided not to release because too accurate to be published. We discuss about image-to-image translation, the dangers of the GPT-2 model and the future of AI. Moreover, Aaron provides some very interesting links and demos that will blow your mind! Enjoy the show!  References Multimodal image to image translation (not all mentioned in the podcast but recommended by Aaron) Pix2Pix:  https://phillipi.github.io/pix2pix/   CycleGAN: https://junyanz.github.io/CycleGAN/   GANimorph Paper: https://arxiv.org/abs/1808.04325 Code: https://github.com/brownvc/ganimorph   UNIT:https://arxiv.org/abs/1703.00848 MUNIT:https://github.com/NVlabs/MUNIT DRIT: https://github.com/HsinYingLee/DRIT   GPT-2 and related  Try OpenAI's GPT-2: https://talktotransformer.com/ Blogpost: https://blog.usejournal.com/opengpt-2-we-replicated-gpt-2-because-you-can-too-45e34e6d36dc The Original Transformer Paper: https://arxiv.org/abs/1706.03762 Grover: The FakeNews generator and detector: https://rowanzellers.com/grover/

Join the discussion on our Discord server Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural networks faster by squeezing the hardware to their needs and making the training pipeline more dense. Enjoy the show!   References Faster Neural Network Training with Data Echoing https://arxiv.org/abs/1907.05550

Join the discussion on our Discord server In this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs. The problem is not as trivial as it seems. Many researchers have failed in generating large images with GANs before. One interesting application of such approach is in medicine for the generation of CT and X-ray images. Enjoy the show!   References Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images https://arxiv.org/abs/1907.01376