Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks · HackerPapers discussions

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks — mentioned by jimfleming

Thu, 28 Mar 2019 21:24:45 GMT

Hyperparameter optimization (including architectures) is not really meta-learning. Meta-learning, also known as "learning to learn", is more like MAML[0], RL2[1], L2RL[2], etc. 0. https://arxiv.org/abs/1703.03400 1. https://arxiv.org/abs/1611.02779 2. https://arxiv.org/abs/1611.05763

Paper and discussion history. A mention is not an endorsement.

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks — mentioned by trextrex

Sat, 02 Jun 2018 22:47:19 GMT

Many concerns in this paper, especially about deep learning being data hungry, limited capacity for transfer, and integrating prior knowledge are addressed by recent papers in meta-learning and learning-to-learn [1,2,3 and many many others] both in the supervised and reinforcement learning contexts. In the case of meta-reinforcement learning there has been recent work [5] which seems to indicate that this mechanism is very similar to how learning works in the brain. In fact, my group recently published a paper [4] about learning-to-learn in spiking networks (making it biologically more…

Paper and discussion history. A mention is not an endorsement.

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks — mentioned by TheIronYuppie

Wed, 17 Jan 2018 21:41:20 GMT

I think we may be talking to different customers. I talked with ~200 customers last year, and the most common question was "What do I use ML for?" and the second most common was "How do I get started?" Put another way, the average customer has ~zero ML usage today. I'd guess that 95%+ of all businesses have ML usage today. Further guessing would say that <1% of ML usage actually care about levels of accuracy beyond "it's better than the hacked together set of rules/filters I use today." These are very large businesses with lots of money to spend on a solution. There are many ways to measure…

Paper and discussion history. A mention is not an endorsement.