machine_learning

Machine learning for partial differential equations 2017-05-15 – 2021-04-13

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

Hydrology models Rivers, aquifers and all that 2020-09-07 – 2021-04-13

Machine learning for physical sciences Turbulent mixing at the boundary between two disciplines with differing inertia and viscosity 2017-05-15 – 2021-04-09

Statistics and machine learning 2011-04-15 – 2021-04-08

pytorch #torched 2018-05-04 – 2021-04-06

Infinite width limits of neural networks 2020-12-09 – 2021-03-29

Model interpretation and explanation Colorizing black boxes 2016-09-01 – 2021-03-15

Diagramming and visualising graphical models My need for this is conditionally dependent upon my deadline, given the subject matter 2018-03-29 – 2021-03-15

Neural nets with implicit layers Also, declarative networks 2020-12-08 – 2021-03-15

Statistics, computational complexity thereof 2020-11-03 – 2021-03-10

Here’s how I would do art with machine learning if I had to 2016-06-06 – 2021-03-09

Neural nets with basis decomposition layers 2021-03-09

Stochastic processes which represent measures over the reals 2020-10-16 – 2021-03-08

Convolutional subordinator processes 2021-03-08

Mind reading by computer The ultimate inverse problem 2017-07-01 – 2021-03-03

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

Convolutional Gaussian processes 2021-03-01

Convolutional stochastic processes 2021-03-01

Causal inference in the continuous limit 2021-02-17

Neural net attention mechanisms On brilliance through selective ignorance 2017-12-20 – 2021-02-10

Voice transcriptions and speech recognition 2019-01-08 – 2021-01-30

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

Auditory features descriptors, maps, representations for audio 2019-11-13 – 2021-01-14

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

Probabilistic neural nets Machinelearnese for “Bayesian” 2020-12-14

Bayesian deep learning 2017-01-11 – 2020-12-10

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

Multi-output Gaussian process regression 2020-12-07

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

Variational inference by message-passing in graphical models 2014-11-25 – 2020-11-25

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

External validity Transfer learning, dataset shift, learning under covariate shift, transferable learning, domain adaptation etc 2020-10-17 – 2020-11-09

Causal inference on DAGs Confounding! This scientist performed miracle graph surgery during an intervention and you won’t believe what happened next 2016-10-26 – 2020-11-04

Moral philosophy 2014-08-04 – 2020-11-03

Efficient factoring of GP likelihoods 2020-10-16 – 2020-10-26

Audiovisuals Synesthetic and other cross-media audio stunts 2020-10-26

Grammatical inference 2011-05-30 – 2020-10-13

Natural language processing Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move. 2018-01-11 – 2020-10-01

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

Causal inference in highly parameterized ML 2020-09-18

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

Neural network activation functions 2017-01-12 – 2020-09-07

Causal graphical model reading group 2020 An introduction to conditional independence DAGs and their use for causal inference. 2020-08-30 – 2020-09-03

Causal Bayesian networks Staged tree models, probability trees …Causlan Bayesian networks 2020-11-01 – 2020-09-01

Emulators and surrogate models Shortcuts in scientific simulation using ML 2020-08-12 – 2020-08-26

Simulation based inference a.k.a. Approximate Bayesian Computation or Likelihood-Free-Inference 2020-08-25

Multi fidelity models 2020-08-24

Model fairness 2018-11-29 – 2020-08-21

Bushfire models 2020-09-07 – 2020-08-19

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

Deep fakery 2020-06-15

Generative neural net models 2020-12-10 – 2020-06-14

Probabilistic neural nets 2017-01-11 – 2020-06-14

Voice fakes 2018-09-06 – 2020-05-27

Directed graphical models 2017-09-20 – 2020-05-13

AV controller interfaces 2019-12-10 – 2020-05-11

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2020-05-01

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

Likelihood free inference 2020-04-22

Learning summary statistics 2020-04-22

Learning graphical models from data What is independent of what? 2017-09-20 – 2020-04-11

Analysis/resynthesis of audio 2016-01-15 – 2020-04-09

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

Recurrent neural networks 2016-06-16 – 2020-01-23

Learning in adaptive systems On staring into scopophilic abysses 2019-10-19 – 2020-01-22

Factor graphs 2019-12-16

Controllerism Making thing happen by waving your arms about on stage 2014-11-17 – 2019-12-10

Audio source separation 2019-11-04 – 2019-11-26

Tunings 2015-10-29 – 2019-11-18

Machine listening Statistical models for audio 2014-10-10 – 2019-11-12

Javascript machine learning 2017-01-13 – 2019-11-09

ISMIR 2019 Music Nerds in Delft 2019-11-04 – 2019-11-09

Inference on graphical models Given what I know about what I know, what do I know? 2017-09-20 – 2019-10-28

Undirected graphical models 2017-09-20 – 2019-10-28

Bio computing 2016-05-29 – 2019-10-14

Probabilistic graphical models over continuous index sets 2014-08-05 – 2019-09-25

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

Hierarchical models DAGs, multilevel models, random coefficient models, mixed effect models… 2015-06-07 – 2019-08-19

Compressing neural nets 2016-10-14 – 2019-05-27

Overparameterization a.k.a. improper learning 2018-04-04 – 2018-12-11

Machine vision 2015-01-03 – 2018-11-14

Gesture recognition 2014-10-17 – 2018-11-12

Moral calculus 2014-08-04 – 2017-11-21

Distributed sensing and swarm sensing 2014-10-13 – 2017-10-12

Probabilistic graphical models 2014-08-05 – 2017-09-11

Brains Neural networks made of real neurons, in functioning brains 2014-11-03 – 2017-08-15

Quantum-probabilistic graphical models 2017-08-07

Marketing psychology 2017-04-27 – 2017-05-29

Entity embeddings 2017-04-01

Musical metrics and manifolds 2014-09-26 – 2017-03-27

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

Genetic algorithms 2011-04-06 – 2016-12-28

Greatest hits 2016-11-19 – 2016-12-12

UNSW Stats reading group 2016 - Causal DAGs An introduction to conditional independence DAGs and their use for causal data. 2016-10-17 – 2016-10-21

Inference from disorder 2016-10-19

Generalised linear models 2016-03-24 – 2016-08-31

Probably Approximately Correct 2014-11-24 – 2016-05-29

Indirect inference 2014-12-23 – 2015-12-15

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