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Blake Camp proslijedio/la je Tweet
The brain is great at exploiting its high dimensionality to avoid something that a lot of neural networks are bad at, ‘catastrophic forgetting.’ We automatically learn new skills without messing up old ones.https://twitter.com/xulunasun/status/1223646256074899456 …
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"Cycle firing encodes hypothetical experience, including multiple possible futures"...awesomehttps://twitter.com/MariRSosa/status/1222917960131497991 …
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Disagree, curriculum is a better word for this.https://twitter.com/hardmaru/status/1222743841301614597 …
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The senility of our senior citizens is no laughing matter, and in that regard I sympathize. But, this is utter lunacy. Comical, embarrassing, downright weird.https://twitter.com/ABC/status/1222600255369359362 …
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Blake Camp proslijedio/la je Tweet
Another day at the
@sprekeler Lab: “Blitzforschung“ = 5min blackboard presentation of a paper
. Yesterday‘s gem: Backpropamine by @ThomasMiconi,@jeffclune et al. (2019; ICLR). Dopamine-inspired modulation of differential plasticity = Bridging
timescales in Meta-Learning
pic.twitter.com/h51LCyudhZ
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The word "itself" is bound to show up in ALOT of AI papers this year.https://twitter.com/hardmaru/status/1221979704095199232 …
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Blake Camp proslijedio/la je Tweet
Procedural Content Generation via Reinforcement Learning “A new approach to procedural content generation in games, where level design is framed as a game (as a sequential task problem), and the content generator itself is learned.” https://arxiv.org/abs/2001.09212 pic.twitter.com/2B6P6tlh9d
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Blake Camp proslijedio/la je Tweet
As far as current machine learning is concerned, generalization originates from the ability to learn the latent manifold on which the training data lies, i.e. the ability to interpolate between training samples (local generalization, by definition)
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been thinking about this for a while, very cool work here from deepmind. we probably shouldn't be waiting to process more information in the forward pass before sending gradients backwards.https://twitter.com/DeepMind/status/1219305774570180615 …
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"... generally intelligent learners. Just thinking about environments as something that can be optimized by learning algorithms is interesting and opens many new research directions." (2)
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https://arxiv.org/pdf/1905.10985.pdf … "In my opinion, a more promising direction is to explicitly optimize environments to be effective for learning, instead of hoping we can create environments that create dynamics that lead to coevolutionary arms races that produce..." (1)
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Blake Camp proslijedio/la je Tweet
1/ This shows how far the field has regressed in its understanding of probability. It's not a controversial opion, it's the opinion of someone who hasn't understood that a prior over weights in a neural network induces a prior over functions.https://twitter.com/carlesgelada/status/1208618401729568768 …
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"We show that an –approximate meta-gradient can be computed via implicit MAML using O˜(log(1/)) gradient evaluations and O˜(1) memory, meaning the memory required does not grow with number of gradient steps."
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"Sec-ond, implicit MAML is agnostic to the inner optimization method used, as long as it can find an approximate solution to the inner-level optimization problem."
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"First, the inner optimization path need not be stored nor differentiated through, thereby making implicit MAML memory efficient and scalable to a large number of inner optimization steps."
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This paper is so, so good. Massive implications. Meta-Learning with Implicit Gradients: https://arxiv.org/abs/1909.04630
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Blake Camp proslijedio/la je Tweet
But that doesn't mean learning is selected against! Quite the opposite. It means learning species will usually out compete non-learning species, and they will develop more hardwired behavior at a faster rate. So, learning will be strongly selected for.
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Blake Camp proslijedio/la je Tweet
How can we predict and control the collective behaviour of artificial agents? Classical game theory isn't much help when there are >2 agents. In our
@iclr_conf paper, we find markets impose useful structure on interactions between gradient-based learners: https://arxiv.org/abs/2001.04678 pic.twitter.com/IeLMcb9f2z
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