Category: Uncategorized

Some interesting excerpt of this

…

As glimpses of meta-learning, I was especially fascinated with Ng’s lectures and labs for:

• Face Recognition, reusing pre-trained models to ‘transfer’ its weights to a new application.
• Neural Style Transfer, teasing the cost function to balance content with style activations.
• Jazz Solo, tricking a pre-trained model to generate a likely sequence of input data.
• Debiasing Word Vectors, detecting and correcting sexual bias with analogies.
• Language Translation, enhancing by managing the attention placed nodes.

My fascination has motivated me to learn about various meta-learning approaches, such as:

• AlphaGo Zero demonstrating that good simulations of the problem domain (like the game Go) can be surrogates for generating labeled data, using Generative Adversarial Networks (GAN).
• Capsule Networks (CapsNet) introduced by Hinton to correct flaws in image classifier.
• CoDeepNEAT optimization of DNN typology, components, and hyperparameters via genetic evolution algorithms.

The insight is that one should eagerly explore the new secret sauce for DL — meta-learning.

A nice compliation

A nice summary

YOLOv3 is out. See paper here and code here. The YOLO paper series is always fun to read. 🙂 Some highlights are

• It is worse than RetinaNet but is 3.8 times faster
• Comparable as SSD but 3 times faster
• One thing interesting thing mentioned in the paper is that focal cost doesn’t seem to help

See this for details

This is some notes from an Q&A of Yann Lecun appeared in the March 2018 issue of Communications of ACM.

What are some of the things going on at FAIR that most interest or excite you?

• … One is marrying reasoning with learning. A lot of learning has to do with perceptions, which are relatively simple things that people can do without thinking too much. But we haven’t yet found good recipes for training systems to do tasks that require a little bit of reasoning. There is some work in that direction, but it’s not where we want it.
• …unsupervised learning—teaching machines to learn by observing the world, say by watching videos or looking at images without being told what objects are in these images
• …autonomous AI systems whose behavior is not directly controlled by a person. In other words, they are designed not just to do one particular task, but to make decisions and adapt to different circumstances on their own.