New paper on arXiv! And I think it's a good'un 😄
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
August 29, 2025 at 3:07 PM
New paper on arXiv! And I think it's a good'un 😄
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Didn’t listen, good decision
April 27, 2025 at 8:31 AM
Didn’t listen, good decision
So simple!
Normally we order our minibatches like
a, b, c, ...., [shuffle], new_a, new_b, new_c, ....
but instead, if we do
a, b, c, ...., [reverse], ...., c, b, a, [shuffle], new_a, new_b, ....
The RMSE of stochastic gradient descent reduces from O(h) to O(h²)
arxiv.org/abs/2504.04274
Normally we order our minibatches like
a, b, c, ...., [shuffle], new_a, new_b, new_c, ....
but instead, if we do
a, b, c, ...., [reverse], ...., c, b, a, [shuffle], new_a, new_b, ....
The RMSE of stochastic gradient descent reduces from O(h) to O(h²)
arxiv.org/abs/2504.04274
April 10, 2025 at 8:48 AM
So simple!
Normally we order our minibatches like
a, b, c, ...., [shuffle], new_a, new_b, new_c, ....
but instead, if we do
a, b, c, ...., [reverse], ...., c, b, a, [shuffle], new_a, new_b, ....
The RMSE of stochastic gradient descent reduces from O(h) to O(h²)
arxiv.org/abs/2504.04274
Normally we order our minibatches like
a, b, c, ...., [shuffle], new_a, new_b, new_c, ....
but instead, if we do
a, b, c, ...., [reverse], ...., c, b, a, [shuffle], new_a, new_b, ....
The RMSE of stochastic gradient descent reduces from O(h) to O(h²)
arxiv.org/abs/2504.04274
Was revisiting the Neural ODEs paper the other day and greatly enjoying.
But I found this super confusing, it’s not an A=B+A statement
But I found this super confusing, it’s not an A=B+A statement
March 24, 2025 at 2:13 PM
Was revisiting the Neural ODEs paper the other day and greatly enjoying.
But I found this super confusing, it’s not an A=B+A statement
But I found this super confusing, it’s not an A=B+A statement
Thinking about it more, I think the sharp jumps are an artefact of the plotting. The plotting function will linearly interpolate but you can actually probabilistically interpolate using the smoothing equations
February 28, 2025 at 3:15 PM
Thinking about it more, I think the sharp jumps are an artefact of the plotting. The plotting function will linearly interpolate but you can actually probabilistically interpolate using the smoothing equations