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← Deep generative models have had less impact than discriminative models…
17 connected korrents · 14 moments on record from 10 Jun 2014 to 12 Aug 2026.
Everything filed under neural networks
neural networks
Everything filed under pruning
pruning
Everything filed under reinforcement learning
reinforcement learning
Everything filed under scaling laws
scaling laws
Everything filed under robotics
robotics Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: Deep generative models have had less impact than discriminative models because of intractable probabilistic computations and difficulty using piecewise linear units. Deep generative models have had less impact than discriminative models because of intractable probabilistic computations and difficulty using piecewise linear units. Last stated 12 years ago
10 Jun 2014
YB
Yoshua Bengio — holds since 2014-06-10 — tap for who they are
Same subject: Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it. — tap to centre the map on it Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: The saving pruning offers is only ever at inference, because the sparse architectures it produces are hard to train from the start. — tap to centre the map on it The saving pruning offers is only ever at inference, because the sparse architectures it produces are hard to train from the start. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
Same subject: Depth is not free: past a point, deeper neural networks are harder to train rather than simply better. — tap to centre the map on it Depth is not free: past a point, deeper neural networks are harder to train rather than simply better. Last stated 11 years ago
10 Dec 2015
JS
Jian Sun — holds since 2015-12-10 — tap for who they are
KH
Kaiming He — holds since 2015-12-10 — tap for who they are
Same subject: It is the diversity of a robot data set, not its size, that produces generalization: dropping the most diverse slice hurts far more than dropping a random fifth. — tap to centre the map on it It is the diversity of a robot data set, not its size, that produces generalization: dropping the most diverse slice hurts far more than dropping a random fifth. Last stated 4 weeks ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: Deep properties of a model are inherited through the previous generation's data, which is why AI systems from different companies end up correlated with one another. — tap to centre the map on it Deep properties of a model are inherited through the previous generation's data, which is why AI systems from different companies end up correlated with one another. Last stated 4 weeks ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: Current techniques restrict pre-trained representation power because standard language models are unidirectional. — tap to centre the map on it Current techniques restrict pre-trained representation power because standard language models are unidirectional. Last stated 8 years ago
11 Oct 2018
KT
Kristina Toutanova — holds since 2018-10-11 — tap for who they are
MC
Ming-Wei Chang — holds since 2018-10-11 — tap for who they are
KL
Kenton Lee — holds since 2018-10-11 — tap for who they are
JD
Jacob Devlin — holds since 2018-10-11 — tap for who they are
Same subject: A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models. — tap to centre the map on it A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models. Last stated 8 years ago
11 Oct 2018
KT
Kristina Toutanova — holds since 2018-10-11 — tap for who they are
MC
Ming-Wei Chang — holds since 2018-10-11 — tap for who they are
KL
Kenton Lee — holds since 2018-10-11 — tap for who they are
JD
Jacob Devlin — holds since 2018-10-11 — tap for who they are
Same subject: We have almost no automated techniques for making a system transfer what it learns, and none of the few we have are used in modern deep learning. — tap to centre the map on it We have almost no automated techniques for making a system transfer what it learns, and none of the few we have are used in modern deep learning. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. — tap to centre the map on it A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
Same subject: A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work. — tap to centre the map on it A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
Same subject: Batteries will replace transmission as the cheapest way to keep the lights on, and the grid will shrink rather than grow. — tap to centre the map on it Batteries will replace transmission as the cheapest way to keep the lights on, and the grid will shrink rather than grow. Last stated 9 months ago
8 Dec 2025
CH
Casey Handmer — holds since 2023-10-11 — tap for who they are
CH
Casey Handmer — holds since 2025-12-08 — tap for who they are
Same subject: A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. — tap to centre the map on it A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. Last stated a month ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. — tap to centre the map on it A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. Last stated 10 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model. — tap to centre the map on it Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model. Last stated a month ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source. — tap to centre the map on it Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source. Last stated 3 years ago
24 Jun 2023
YB
Yoshua Bengio — holds since 2023-06-24 — tap for who they are
Same subject: An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job. — tap to centre the map on it An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job. Last stated 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Anything nature shaped can be learned efficiently by a classical neural network, because evolutionary processes leave structure behind. — tap to centre the map on it Anything nature shaped can be learned efficiently by a classical neural network, because evolutionary processes leave structure behind. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
At the centre
Deep generative models have had less impact than discriminative models because of intractable probabilistic computations and difficulty using piecewise linear units.
Holds Read this korrent →
Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it.
Holds RS Richard Sutton
The saving pruning offers is only ever at inference, because the sparse architectures it produces are hard to train from the start.
Holds MC Michael CarbinJF Jonathan Frankle
Depth is not free: past a point, deeper neural networks are harder to train rather than simply better.
Holds JS Jian SunKH Kaiming He
It is the diversity of a robot data set, not its size, that produces generalization: dropping the most diverse slice hurts far more than dropping a random fifth.
Holds CF Chelsea Finn
Deep properties of a model are inherited through the previous generation's data, which is why AI systems from different companies end up correlated with one another.
Holds RG Ryan Greenblatt
Current techniques restrict pre-trained representation power because standard language models are unidirectional.
Holds KT Kristina ToutanovaMC Ming-Wei ChangKL Kenton LeeJD Jacob Devlin
A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models.
Holds KT Kristina ToutanovaMC Ming-Wei ChangKL Kenton LeeJD Jacob Devlin
We have almost no automated techniques for making a system transfer what it learns, and none of the few we have are used in modern deep learning.
Holds RS Richard Sutton
A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it.
Holds MC Michael CarbinJF Jonathan Frankle
A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work.
Holds MC Michael CarbinJF Jonathan Frankle
Batteries will replace transmission as the cheapest way to keep the lights on, and the grid will shrink rather than grow.
Holds CH Casey Handmer
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds DD Dmitri Dolgov
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model.
Holds DD Dmitri Dolgov
Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source.
Holds
An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job.
Holds
Anything nature shaped can be learned efficiently by a classical neural network, because evolutionary processes leave structure behind.
Holds DH Demis Hassabis