Neural Nets

Implementing neural nets 2016-10-14 – 2021-09-16

pytorch #torched 2018-05-04 – 2021-09-14

Neural nets with implicit layers Also, declarative networks 2020-12-08 – 2021-09-07

Recurrent neural networks 2016-06-16 – 2021-09-06

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2021-08-24

Causal inference in highly parameterized ML 2020-09-18 – 2021-08-17

Neural network activation functions 2017-01-12 – 2021-08-02

Here’s how I would do art with machine learning if I had to 2016-06-06 – 2021-07-26

Neural net attention mechanisms On brilliance through selective ignorance 2017-12-20 – 2021-07-26

Learning summary statistics 2020-04-22 – 2021-07-15

Multi-task ML 2021-07-14

ML on small devices Putting intelligence on chips small enough to be in disconcerting places 2016-10-14 – 2021-07-13

Overparameterization a.k.a. improper learning 2018-04-04 – 2021-07-08

Tensorflow The framework to use for deep learning if you groupthink like Google 2016-07-11 – 2021-07-07

ML Koans Passing through the NAND-gate 2021-06-23

Ensembling neural nets Monte Carlo 2020-12-14 – 2021-06-15

Deep generative models 2020-12-10 – 2021-06-08

Technological singularities Incorporating hard AI take-offs, game-over high scores, the technium, god-in-the-cloud, deus-ex-machina, nerd raptures and so forth 2016-12-01 – 2021-06-01

Infinite width limits of neural networks 2020-12-09 – 2021-05-11

Compressing neural nets 2016-10-14 – 2021-05-07

ML benchmarks and their pitfalls On marginal efficiency gain in paperclip manufacture 2020-08-16 – 2021-04-13

Neural nets with basis decomposition layers 2021-03-09

Memory in machine learning 2021-03-03 – 2021-03-03

Causal inference in the continuous limit 2021-02-17

Neural nets for “implicit representations” 2021-01-21 – 2021-01-21

Statistical mechanics of statistics 2016-12-01 – 2021-01-06

Why does deep learning work? Are we in the pocket of Big VRAM? 2017-05-30 – 2020-12-14

Garbled highlights from NeurIPS 2020 2020-09-17 – 2020-12-11

Nonparametrically learning dynamical systems 2018-08-13 – 2020-12-08

Regularising neural networks Generalisation for street fighters 2017-02-12 – 2020-12-01

Graph neural nets 2020-09-16 – 2020-11-24

Big data ML best practice 2020-09-16 – 2020-09-21

Dimensionality reduction Wherein I teach myself, amongst other things, feature selection, how a sparse PCA works, and decide where to file multidimensional scaling 2015-03-22 – 2020-09-11

Neural nets designing the fanciest usable differentiable loss surface 2016-10-14 – 2020-09-09

Statistics and ML in python 2015-04-27 – 2020-08-27

Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23

Deep fakery 2020-06-15

Convolutional neural networks 2017-11-10 – 2020-04-24

Learning Gamelan 2016-04-05 – 2020-04-06

Deep learning as a dynamical system 2018-08-13 – 2020-04-02

Nonparametrically learning spatiotemporal systems 2020-09-16 – 2020-04-02

Teaching computers to write music 2016-06-06 – 2020-03-25

Learnable indexes and hashes 2018-01-12 – 2020-02-18

Gradient descent, first-order, stochastic a.k.a. SGD, as seen in deep learning 2020-01-30 – 2020-02-07

Gradient descent, Newton-like, stochastic 2020-01-23

Gradient descent, Higher order 2019-10-26

Differentiable learning of automata 2016-10-14 – 2019-09-11

Gradient descent, Newton-like 2019-02-05 – 2019-09-03

Entity embeddings 2017-04-01

Random neural networks 2017-02-17 – 2017-02-19

Garbled highlights from NIPS 2016 2016-12-05 – 2017-02-03

Pattern machine 2011-06-27 – 2015-11-24