Will Tebbutt
Will Tebbutt
There are a variety of interesting optimisations that can be performed on kernels of the form ```julia k(x, z) = w_1 * k_1(x, z) + w_2 * k_2(x, z) +...
Consider a kernel of the form ```julia (k)(x::Vector, x′::Vector) = prod(map(k.ks, x, x′)) ``` where `k.ks` is a collection of kernels which can be applied to single-dimensions, and the dimensionality...
MWE (less connection to a database with a large table): ```julia using LibPQ # This works fine if you run it _before_ doing LibPQ stuff. # Allocates about 28GB of...
Starts to address #250 . Entirely fixes the bug as described in that issue (Float64s, each call to `getindex` allocates nothing). The changes include: 1. ~~introducing a layer of indirection...
MWE (I've used a connection to a private DB in this issue, so you'll have to construct your own DB in order to run locally): ```julia using BenchmarkTools, JSON3, LibPQ,...
The `inplace=false` kwarg is only mentioned in the "Minimizing a function" tutorial, so is a little hard to find (I always have to ctrl-f to find it, and was just...
Resolves #603
[This](https://github.com/JuliaStats/Distributions.jl/blob/c9d6c28f415025bf489ac3bec2f8eec46b0eefbd/src/genericrand.jl#L48) fallback method for `rand` in `Distributions.jl` hits [this](https://github.com/JuliaDiff/ChainRules.jl/blob/f13e0a45d10bb13f48d6208e9c9d5b4a52b96732/src/rulesets/Random/random.jl#L25) rule, which is declared non-differentiable. This results in a silent failure, where there ought to be an error if the given...
There are basically two reasons to implement rules: 1. to define AD. For example, you do have to tell an AD system _somewhere_ how to differentiate addition and multiplication of...
There's some linear algebra optimisations implemented [here](https://github.com/GiggleLiu/BackwardsLinalg.jl) that we don't have. Nice blog post [here](https://giggleliu.github.io/2019/04/02/einsumbp.html)