- 【博客】OpenAI post about Generative Models: an example of excellence in R&D
The last post of this week will be a share of a Blog post from the excellent organization OpenAI. OpenAI is excellent because of its quality overall, but importantly because it is completely open and open source about Artificial Intelligence (AI) research and development of algorithms; the researchers comprising it are all (or almost I suppose…) PhDs in the fields of Computer Science, Machine/Deep Learning, AI or related scientific subjects like Data Science/Statistics (the cautioned informed reader knows full well that Machine/Deep learning aren’t really independent fields of study, so there aren’t really PhDs in those fields, but only in Computer Science, but I hope that shouldn’t be understood literally).
OpenAI features an excellent online presence and its Blog posts are readership that is highly to recommend. Today I would like to share one such posts that I encountered while doing my daily research briefing in AI subject matters. It is about generative models, and the flurry of activity these models are creating within the AI research communities is nothing short of justified excitement. These models are demonstrating important capacities to improve significantly machine/deep learning pipelines of all sorts, but the main excitement has revolved around Computer Vision and Natural Language Processing applications. The emergence og GANS (generative adversarial networks), for instance, have found potential in social media environments and other settings. But generative models are a broad category of models and conceptual frameworks with usability in the fields I mentioned earlier in the first paragraph. To list them like it is found in the Wikipedia entry would also be useful for purposes here (with the corresponding links) :
Examples of generative models include:
Gaussian mixture model and other types of mixture model
Hidden Markov model
Probabilistic context-free grammar
Averaged one-dependence estimators
Latent Dirichlet allocation
Restricted Boltzmann machine
Generative adversarial networks
2.【博客 & 代码】Infinite Mixture Models with Nonparametric Bayes and the Dirichlet Process
Imagine you’re a budding chef. A data-curious one, of course, so you start by taking a set of foods (pizza, salad, spaghetti, etc.) and ask 10 friends how much of each they ate in the past day.
Your goal: to find natural groups of foodies, so that you can better cater to each cluster’s tastes. For example, your fratboy friends might love wings and beer, your anime friends might love soba and sushi, your hipster friends probably dig tofu, and so on.
So how can you use the data you’ve gathered to discover different kinds of groups?
3.【论文】On the Origin of Deep Learning
This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep learning like convolutional neural networks, deep belief networks, and recurrent neural networks, and extends to popular recent models like variational autoencoder and generative adversarial nets. In addition to a review of these models, this paper primarily focuses on the precedents of the models above, examining how the initial ideas are assembled to construct the early models and how these preliminary models are developed into their current forms. Many of these evolutionary paths last more than half a century and have a diversity of directions. For example, CNN is built on prior knowledge of biological vision system; DBN is evolved from a trade-off of modeling power and computation complexity of graphical models and many
nowadays models are neural counterparts of ancient linear models. This paper reviews these evolutionary paths and offers a concise thought flow of how these models are developed, and aims to provide a thorough background for deep learning. More importantly, along with the path, this paper summarizes the gist behind these milestones and proposes many directions to guide the future research of deep learning.
4.【博客】Up to Speed on Deep Learning: March Update
Continuing our series of deep learning updates, we pulled together some of the awesome resources that have emerged since our last post. In case you missed it, here are our past updates: November, September part 2 & October part 1, September part 1, August part 2, August part 1, July part 2, July part 1, June, and the original set of 20+ resources we outlined in April. As always, this list is not comprehensive, so let us know if there’s something we should add, or if you’re interested in discussing this area further.
Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research.