Найти похожее

#ML The new AI spring: a deflationary view It's actually fun to watch philosophers fighting each other. The author is trying to deflate the inflated expectations on AI by looking into why inflated expectations are harming our society. It's not exactly based on evidence but still quite interesting to read. | SpringerLink https://link.springer.com/article/10.1007/s00146-019-00912-z

#ML Machine Learning, Kolmogorov Complexity, and Squishy Bunnies http://www.theorangeduck.com/page/machine-learning-kolmogorov-complexity-squishy-bunnies

#ML [D] Convolution Neural Network Visualization - Made with Unity 3D and lots of Code / source - stefsietz (IG) https://www.reddit.com/r/MachineLearning/comments/leq2kf/d_convolution_neural_network_visualization_made/?utm_medium=android_app&utm_source=share

#ML “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI TL;DR: - Data quality is crucial in any AI especially for those with high-stakes. - Many data work are overlooked easily: politics (some data entries are not recorded or misrecorded), human in the loop of data quality interventions for cleaning and wrangling but upstream data creation shall be controlled well too, etc - Data Cascades: how the issues are cascading from upstream to downstream should be clear. > Data Cascades: compounding events causing negative, downstream effects from data issues, resulting in technical debt over time. https://research.google/pubs/pub49953/

#ML Visualizing Weights https://distill.pub/2020/circuits/visualizing-weights

#ML https://www.nature.com/articles/s41593-019-0520-2?fbclid=IwAR1-L-MZRAuHO1YTFhAu5_zETTjgkHpEg5-HgGywEGpITbYQpU2Yld5IzrU By computational neuroscientist

#ML Sarcasm Detection with Sentiment Semantics Enhanced Multi-level Memory Network - ScienceDirect https://www.sciencedirect.com/science/article/abs/pii/S0925231220304689 Sheldon, this is your thing! (Didn't read the paper. I just find this title a bit amusing.)

#ML http://akosiorek.github.io/ml/2018/03/14/what_is_wrong_with_vaes.html - instabilities - “last-mile” effort in optimization is too high

#ML An interesting idea on time series predictions. Instead of predicting the exact time series, the author proposed a method to predict the future using ordinal patterns. The figure shows how to disintegrate the time series into 8 overlapping short-term series (each with three numbers). To transform the short-term series into patterns, we write down the permutation pattern (for size of the series D=3, we have only 6 possible permutations). Then we will use the permutation patterns in the past to predict the patterns in the future. BTW, this paper used the price of bitcoins as an example to test this method. This method will not be super amazing. The point of this paper is to propose a simple method to predict the future using very limited resource. This is the paper: https://royalsocietypublishing.org/doi/10.1098/rsos.201011 Short-term prediction through ordinal patterns

#ML http://jibencaozuo.com/ PaperClip made a platform for everyone to play with artificial neural networks. My impression: it looks nice. The interactions can be better but I am sure the next iteration will be much better.

#ML https://podcasts.google.com/?feed=aHR0cHM6Ly9sZXhmcmlkbWFuLmNvbS9mZWVkL3BvZGNhc3Qv&ep=14&episode=aHR0cHM6Ly9sZXhmcmlkbWFuLmNvbS8_cD00NTAz

#ML Description of tables http://feedproxy.google.com/~r/blogspot/gJZg/~3/jeMkmAfQxOk/totto-controlled-table-to-text.html