machine_learning

Visualising probabilistic graphical models Also related models, such as Neural nets 2018-03-29 – 2023-02-02

Last-layer Bayes neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2023-02-02

Transformer networks The transformer-powered subtitle recommendation for this article was “Our most terrifyingly effective weapon against the forces of evil is our ability to laugh at them.” 2017-12-20 – 2023-02-02

Implementing neural nets 2016-10-14 – 2023-01-27

Quantified self Instrumentation and analytics for body and soul. 2022-01-11 – 2023-01-22

Automatic programming 2016-10-14 – 2023-01-15

Variational message-passing algorithms in graphical models Cleaving reality at the joint, then summing it at the marginal 2014-11-25 – 2023-01-12

Factor graphs 2019-12-16 – 2023-01-12

Goodhart’s Law 2019-12-22 – 2023-01-11

Graph neural nets 2020-09-16 – 2022-12-19

Physics-informed neural networks 2019-10-15 – 2022-12-09

Deep sets invariant and equivariant functions 2022-11-24 – 2022-12-08

Machine learning for physical sciences Turbulent mixing at the boundary between two disciplines with differing inertia and viscosity 2017-05-15 – 2022-12-07

Practical text generation and writing assistants 2019-01-08 – 2022-12-06

Density ratio tricks 2022-12-06

Nonparametrically learning dynamical systems 2018-08-13 – 2022-12-06

Adverse advice selection 2022-01-25 – 2022-11-15

Interaction effects and subgroups are probably what we want to estimate 2022-01-25 – 2022-11-06

Deep learning as a dynamical system 2018-08-13 – 2022-10-30

The edge of chaos Computation, evolution, competition and other past-times of faculty 2016-12-01 – 2022-10-30

Natural language processing software Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move. 2018-01-11 – 2022-10-23

Generative art with language+diffusion models 2022-09-16 – 2022-10-20

Neural tangent kernel 2020-12-09 – 2022-10-14

Multi-objective optimisation 2021-07-14 – 2022-10-10

Continuous and equilibrium probabilistic graphical models 2014-08-05 – 2022-10-10

Φ-Flow A modern python computational fluid dynamics library for ML research 2022-06-24 – 2022-10-04

Learning graphical models from data Also, causal discovery, structure discovery 2017-09-20 – 2022-10-01

Voice transcriptions and speech recognition 2019-01-08 – 2022-09-25

Ensemble Kalman methods for training neural networks Data assimilation for network weights 2022-09-20

Laplace approximations in inference Lightweight uncertainties, especially for heavy neural nets 2021-07-28 – 2022-09-06

Causal inference in highly parameterized ML 2020-09-18 – 2022-09-02

Gaussian belief propagation Least squares at maximal elaboration 2014-11-25 – 2022-09-01

Scattering transforms 2022-03-16 – 2022-08-31

Hydrology, applied Rivers, aquifers and other wet things that can flood your house 2020-09-07 – 2022-08-30

Neural PDE operator learning 2019-10-15 – 2022-08-29

Machine learning for partial differential equations 2017-05-15 – 2022-08-29

Elliptical belief propagation Generalized least generalized squares 2022-08-22 – 2022-08-23

Javascript machine learning 2017-01-13 – 2022-08-19

Technological singularities Incorporating hard AI take-offs, game-over high scores, the technium, deus-ex-machina, deus-ex-nube, nerd raptures and so forth 2016-12-01 – 2022-08-10

Neural nets with implicit layers Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem 2020-12-08 – 2022-08-09

Ablation studies, lesion studies, and complex systems 2016-10-26 – 2022-08-06

Causal inference on DAGs Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-08-06

Instumental variables and two stage regression Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-08-06

Neural net attention mechanisms On brilliance through selective ignorance 2017-12-20 – 2022-08-05

Neural learning for spatiotemporal systems 2020-09-16 – 2022-07-28

Bayes linear regression and basis-functions in Gaussian process regression a.k.a Fixed Rank Kriging, weight space GPs 2022-02-22 – 2022-07-27

Integrated Nested Laplace Approximation 2021-07-28 – 2022-07-26

Differentiable PDE solvers 2017-05-15 – 2022-07-20

Learnable coarse-graining Approximate meso-scale physics 2020-08-24 – 2022-07-13

Multi fidelity models Data-driven multi-scale sampling 2020-08-24 – 2022-07-12

Hydrology, applied Rivers, aquifers and other wet things that can flood your house 2020-09-07 – 2022-06-30

Overparameterization in large models Improper learning, benign overfitting, double descent 2018-04-04 – 2022-05-27

Model interpretation and explanation Colorizing black boxes 2016-09-01 – 2022-05-06

(Discrete-measure)-valued stochastic processes 2019-10-10 – 2022-05-04

Measure-valued stochastic processes Moving masses 2020-10-16 – 2022-05-03

Causal graphical model reading group 2022 Causal inference 2022-04-01 – 2022-04-29

SLAM Simultaneous Location and Mapping 2014-11-25 – 2022-04-28

Vecchia factoring of GP likelihoods Ignore some conditioning in the dependencies and attain a sparse cholesky factor for the precision matrix 2022-04-27

Hierarchical models DAGs, multilevel models, random coefficient models, mixed effect models, structural equation models… 2015-06-07 – 2022-04-21

Model fairness 2018-11-29 – 2022-04-21

Particle belief propagation Graphical inference using empirical distribution estimates 2014-07-25 – 2022-04-08

Particle Markov Chain Monte Carlo Particle systems as MCMC proposals 2014-07-25 – 2022-04-08

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2022-04-07

Partition-valued random variates 2022-04-01

Belief propagation 2014-11-25 – 2022-03-31

Measure-valued random variates Including completely random measures and many generalizations 2020-10-16 – 2022-03-30

Reservoir Computing 2022-03-28

Learning Gaussian processes which map functions to functions 2020-12-07 – 2022-02-25

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2022-02-25

Moral calculus 2014-08-04 – 2022-02-19

Differentiable learning of automata 2016-10-14 – 2022-02-19

Neurons Neural networks made of real neurons, in functioning brains 2014-11-03 – 2022-02-14

Probabilistic graphical models 2014-08-05 – 2022-02-08

Random graphical models Causality in amongst confusion 2021-10-26 – 2022-02-07

Neural nets for “implicit representations” 2021-01-21 – 2022-02-01

Neural nets with basis decomposition layers 2021-03-09 – 2022-02-01

Here’s how I would do art with machine learning if I had to 2016-06-06 – 2022-02-01

Running neural nets backwards 2022-01-29

Statistics and machine learning 2011-04-15 – 2022-01-27

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2022-01-26

Causality via potential outcomes Neyman-Rubin, counterfactuals, conditional treatment effects, and related tricks 2016-10-26 – 2021-12-10

Convolutional subordinator processes 2021-03-08 – 2021-12-01

Gaussian Processes as stochastic differential equations Imposing time on things 2019-09-18 – 2021-11-25

Ensembling neural nets Monte Carlo 2020-12-14 – 2021-11-25

Convolutional neural networks 2017-11-10 – 2021-11-21

Neural nets for “implicit representations” 2021-01-21 – 2021-11-16

External validity and dataset shift Also transfer learning, learning under covariate shift, transferable learning, domain adaptation etc 2020-10-17 – 2021-11-03

Probably Approximately Correct 2014-11-24 – 2021-10-27

Neural music synthesis 2016-01-15 – 2021-10-14

Random neural networks 2017-02-17 – 2021-10-12

Missing data Imputation, estimation despite etc 2021-10-07

Regularising neural networks Generalisation for street fighters 2017-02-12 – 2021-09-24

Economics of automation When to the robots come for my job? 2021-09-20

Approximate Bayesian Computation Posterior updates without likelihood 2020-08-25 – 2021-09-20

Biological basis of language 2018-01-11 – 2021-09-16

Natural language processing Automatic processing of labguage 2018-01-11 – 2021-09-16

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

Convolutional stochastic processes Moving averages of noise 2021-03-01 – 2021-08-16

Grammar induction 2011-05-30 – 2021-08-09

Generalised linear models 2016-03-24 – 2021-08-05

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

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

Moral philosophy 2014-08-04 – 2021-07-01

Distributed sensing, swarm sensing, adaptive social learning 2014-10-13 – 2021-06-28

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

Voice fakes 2018-09-06 – 2021-06-17

Neural net kernels 2019-09-16 – 2021-05-24

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

Compressing neural nets pruning, compacting and otherwise fitting a good estimate into fewer parameters 2016-10-14 – 2021-05-07

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

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

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

Stochastic processes on manifolds 2021-03-01

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

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

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

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

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 …Causalan Bayesian networks 2020-11-01 – 2020-09-01

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

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

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

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

Likelihood free inference 2020-04-22

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

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

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

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

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

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

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

Bio computing 2016-05-29 – 2019-10-14

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

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

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

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

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

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