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#ml A family tree shows how transformers are evolving. (HTML is probably the worst name for a model.) https://arxiv.org/abs/2302.07730

#ml Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer) https://huggingface.co/blog/autoformer

#ml https://www.kdnuggets.com/2023/06/ten-years-ai-review.html

#ml Yeh, Catherine, Yida Chen, Aoyu Wu, Cynthia Chen, Fernanda Viégas, and Martin Wattenberg. 2023. “AttentionViz: A Global View of Transformer Attention.” ArXiv [Cs.HC]. arXiv. http://arxiv.org/abs/2305.03210.

#ml Pérez J, Barceló P, Marinkovic J. Attention is Turing-Complete. J Mach Learn Res. 2021;22: 1–35. Available: https://jmlr.org/papers/v22/20-302.html

#ml https://mlcontests.com/state-of-competitive-machine-learning-2022/ Quote from the report: Successful competitors have mostly converged on a common set of tools — Python, PyData, PyTorch, and gradient-boosted decision trees. Deep learning still has not replaced gradient-boosted decision trees when it comes to tabular data, though it does often seem to add value when ensembled with boosting methods. Transformers continue to dominate in NLP, and start to compete with convolutional neural nets in computer vision. Competitions cover a broad range of research areas including computer vision, NLP, tabular data, robotics, time-series analysis, and many others. Large ensembles remain common among winners, though single-model solutions do win too. There are several active machine learning competition platforms, as well as dozens of purpose-built websites for individual competitions. Competitive machine learning continues to grow in popularity, including in academia. Around 50% of winners are solo winners; 50% of winners are first-time winners; 30% have won more than once before. Some competitors are able to invest significantly into hardware used to train their solutions, though others who use free hardware like Google Colab are also still able to win competitions.

#ml Releasing the Skynet https://github.com/internet-explorer-ssl/internet-explorer

#ml google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models. https://github.com/google-research/tuning_playbook

#ml Haha icecube IceCube - Neutrinos in Deep Ice | Kaggle https://www.kaggle.com/competitions/icecube-neutrinos-in-deep-ice?utm_medium=email&utm_source=gamma&utm_campaign=comp-icecube-2023

#ml https://illustrated-machine-learning.github.io/index.html

#ml Top-10 Things in 2022 | Anima on AI https://anima-ai.org/2022/12/31/top-10-things-in-2022/

#ml GPT writing papers... Both fancy and scary. https://huggingface.co/stanford-crfm/pubmedgpt?text=Neuroplasticity