Tap a claim on the ring to put it at the centre.
← Deep generative models have had less impact than discriminative models…
17 connected korrents · 18 moments from 1 Jun 2009 to 18 Sept 2026.
Everything filed under neural networks
neural networks
Everything filed under pruning
pruning
Everything filed under computer vision
computer vision
Everything filed under LLMs
LLMs
Everything filed under GANs
GANs
Everything filed under design
design
Everything filed under transformers
transformers 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: An adversarial GAN loss should only supplement a larger unsupervised or semi-supervised generative model rather than stand alone as a primary approach. — tap to centre the map on it An adversarial GAN loss should only supplement a larger unsupervised or semi-supervised generative model rather than stand alone as a primary approach. Last stated 6 years ago
4 Feb 2021
GW
Gwern — holds since 2021-02-04 — tap for who they are
Same subject: As models get better at following instructions, techniques like finetuning and constrained sampling for structured outputs will become less necessary — tap to centre the map on it As models get better at following instructions, techniques like finetuning and constrained sampling for structured outputs will become less necessary Last stated 3 years ago
16 Jan 2024
CH
Chip Huyen — holds since 2024-01-16 — tap for who they are
Same subject: Generative AI has failed to produce the learning gains it was promised to deliver and instead shows evidence of harming learning. — tap to centre the map on it Generative AI has failed to produce the learning gains it was promised to deliver and instead shows evidence of harming learning. Last stated 4 months ago
24 Jun 2026
DM
Dan Meyer — holds since 2026-06-24 — tap for who they are
Same subject: Large language models are broad and impressive but not precise, and are not fundamentally about deep computation. — tap to centre the map on it Large language models are broad and impressive but not precise, and are not fundamentally about deep computation. Last stated 8 months ago
23 Feb 2026
SW
Stephen Wolfram — holds since 2026-02-23 — 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 a year ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Generative AI models lack the capacity for personal growth or change through the act of creation. — tap to centre the map on it Generative AI models lack the capacity for personal growth or change through the act of creation. Last stated 9 months ago
26 Jan 2026
BS
Brandon Sanderson — holds since 2026-01-26 — 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: 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 11 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: Most agent harnesses need some pruning during context compaction anyway for cost reasons, which Jev might help with. — tap to centre the map on it Most agent harnesses need some pruning during context compaction anyway for cost reasons, which Jev might help with. Last stated a month ago
18 Sept 2026
AR
Armin Ronacher — holds since 2026-09-18 — tap for who they are
Same subject: Neuroplasticity is as much the removal of connections as the growth of new ones, so pruning is a form of learning rather than a form of decay. — tap to centre the map on it Neuroplasticity is as much the removal of connections as the growth of new ones, so pruning is a form of learning rather than a form of decay. Last stated 3 months ago
20 Jul 2026
TW
Tommy Wood — holds since 2026-07-20 — tap for who they are
Same subject: Sequence transduction can use a simple architecture based solely on attention, without recurrence or convolutions. — tap to centre the map on it Sequence transduction can use a simple architecture based solely on attention, without recurrence or convolutions. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. — tap to centre the map on it A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: Looped/recurrent-depth transformer architectures can improve model quality at a fixed compute budget when the model is large enough. — tap to centre the map on it Looped/recurrent-depth transformer architectures can improve model quality at a fixed compute budget when the model is large enough. Last stated 2 months ago
9 Sept 2026
SR
Sebastian Raschka — holds since 2026-09-09 — tap for who they are
Same subject: AlexNet's real breakthrough was not computer vision but the discovery of a universal function approximator that can learn any function. — tap to centre the map on it AlexNet's real breakthrough was not computer vision but the discovery of a universal function approximator that can learn any function. Last stated 3 months ago
26 Jul 2026
JH
Jensen Huang — holds since 2026-07-26 — tap for who they are
Same subject: Atlas is the first model to unify pixel generation and pixel reconstruction, two tracks computer vision has kept apart for over half a century. — tap to centre the map on it Atlas is the first model to unify pixel generation and pixel reconstruction, two tracks computer vision has kept apart for over half a century. Last stated 2 months ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — tap for who they are
Same subject: Growth of internet image data can enable more sophisticated models for indexing and interacting with multimedia. — tap to centre the map on it Growth of internet image data can enable more sophisticated models for indexing and interacting with multimedia. Last stated 17 years ago
1 Jun 2009
FL
Fei-Fei Li — holds since 2009-06-01 — 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 →
An adversarial GAN loss should only supplement a larger unsupervised or semi-supervised generative model rather than stand alone as a primary approach.
Holds
As models get better at following instructions, techniques like finetuning and constrained sampling for structured outputs will become less necessary
Holds
Generative AI has failed to produce the learning gains it was promised to deliver and instead shows evidence of harming learning.
Holds
Large language models are broad and impressive but not precise, and are not fundamentally about deep computation.
Holds
Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it.
Holds
Generative AI models lack the capacity for personal growth or change through the act of creation.
Holds
The saving pruning offers is only ever at inference, because the sparse architectures it produces are hard to train from the start.
Holds
Depth is not free: past a point, deeper neural networks are harder to train rather than simply better.
Holds
Batteries will replace transmission as the cheapest way to keep the lights on, and the grid will shrink rather than grow.
Holds
Most agent harnesses need some pruning during context compaction anyway for cost reasons, which Jev might help with.
Holds
Neuroplasticity is as much the removal of connections as the growth of new ones, so pruning is a form of learning rather than a form of decay.
Holds
Sequence transduction can use a simple architecture based solely on attention, without recurrence or convolutions.
Holds
A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution.
Holds
Looped/recurrent-depth transformer architectures can improve model quality at a fixed compute budget when the model is large enough.
Holds
AlexNet's real breakthrough was not computer vision but the discovery of a universal function approximator that can learn any function.
Holds
Atlas is the first model to unify pixel generation and pixel reconstruction, two tracks computer vision has kept apart for over half a century.
Holds
Growth of internet image data can enable more sophisticated models for indexing and interacting with multimedia.
Holds