# feature_construction

Simulation-based inference
2014-12-23
– 2023-11-23Quantization
2016-03-29
– 2023-11-02Non-negative matrix factorisation
2019-10-14
– 2023-10-26Low-rank matrices
2014-08-05
– 2023-09-29Matrix inverses
2014-08-05
– 2023-09-29Nearly-low-rank Hermitian matrices
a.k.a. perturbations of the identity, low-rank-plus-diagonal matrices
2014-08-05
– 2023-09-13Approximate matrix factorisations and decompositions
Sometimes even exact
2014-08-05
– 2023-08-23Neural vector embeddings
Hyperdimensional Computing, Vector Symbolic Architectures, Holographic Reduced Representations
2017-12-20
– 2023-06-13Noise contrastive estimation
Also “negative sampling”.
2020-04-22
– 2023-06-06Matrix norms, divergences, metrics
2016-06-03
– 2023-05-29Singular Value Decomposition
The ML workhorse
2014-08-05
– 2023-05-23Matrix square roots
Whitening, preconditioning etc
2014-08-05
– 2023-05-13Model order reduction
2015-03-22
– 2023-04-21Deep sets
invariant and equivariant functions
2022-11-24
– 2023-03-21(Nearly-)Convex relation of nonconvex problems
2018-04-04
– 2023-03-17Density ratio tricks
2022-12-06Randomised linear algebra
2016-08-16
– 2022-10-22Laplace approximations in inference
Lightweight uncertainties, especially for heavy neural nets
2021-07-28
– 2022-09-06Semantics
Compressed representations of reality for syntactic agents; which might be what meaning means
2014-12-29
– 2022-08-28Integrated Nested Laplace Approximation
2021-07-28
– 2022-07-26Overparameterization in large models
Improper learning, benign overfitting, double descent
2018-04-04
– 2022-05-27Reservoir Computing
2022-03-28Random neural networks
2017-02-17
– 2021-10-12Approximate Bayesian Computation
Posterior updates without likelihood
2020-08-25
– 2021-09-20Learning summary statistics
2020-04-22
– 2021-07-15Randomized low dimensional projections
2021-03-12
– 2021-05-24Infinite width limits of neural networks
2020-12-09
– 2021-05-11Random embeddings and hashing
2016-12-05
– 2020-12-01Randomised regression
2017-01-13
– 2020-12-01Data 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
– 2020-09-11Emulators and surrogate models via ML
Shortcuts in scientific simulation using ML
2020-08-12
– 2020-08-26Learning of manifolds
Also topological data analysis; other hip names to follow
2014-08-19
– 2020-06-23Learnable indexes and hashes
2018-01-12
– 2020-02-18Phase retrieval
I’ve got the power. / Like the crack of the whip/ I snap attack/ Front to back
2017-01-16
– 2019-11-07Fourier interpolation
2019-06-19Clustering
2015-05-22
– 2016-06-07