![]() ![]() SpotTune: transfer learning through adaptive fine-tuning 6. Variational Auto-Encoders (VAEs) greatly influenced my research as they allow to stochastically sampling from a learned distribution. I reached this paper pretty late, but it is a masterpiece of Machine Learning. It discusses a new Deep Learning module, called SHA-RNN, that allows to model long-term dependencies of data with fewer parameters and without LSTMs.Įven if you do not work in the NLP field, it might be useful to read it!Īuto-encoding variational bayes 5. Not only is this paper useful, but also funny to read! This paper develops a mechanistic model to explain exactly this. How do they shape our opinions’ share and distributions? How do filtering algorithms influence people’s polarization? However, our attention is affected by cognitive constraints.Īlgorithms such as those that control the Facebook and Instagram timelines can distil information for us. We have access to an increasing amount of information, which is easily accessible through or devices, and we are able to communicate with other people without being affected by geographical constraints. Modern technology has greatly influenced how we access information, form opinions and interact with other people. ![]() Modelling opinion dynamics in the age of algorithmic personalization 3. I think that, by increasing awareness on the environmental cost of our models, we might think twice about the trade-off between complexity and models' performance. long-term, uncertain impact).Īs a bonus, I also suggest GreenAI 2, which explains the complexity and efficiency of recent Deep Learning models. Industry, Buildings and cities) and strategies (e.g. These discoveries are organized by field (e.g. Specifically, 21 researchers wrote an article that collects recent trends and discoveries that might help alleviating climate change. Many have focused on the increasing cost of Deep Learning models, while others have concentrated on their potentialities. Thanks (mainly) to Greta Thunberg, mass media amply discussed climate change. Tackling Climate Change with Machine Learning 1. Nicola Perra listed the top 10 articles for the number of engagements from the NetScience twitter profile, while the community of ‘r/MachineLearning’ listed the best paper of 2019 for the Machine Learning community. It is the fourth time I write this feature, and I really enjoy going through my lists of 2018, 20. ![]() 2019 is almost ended, so it is time to wrap-up the annual list of the best scientific papers of the year. ![]()
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