HAIFA, ISRAEL—Flint scrapers and handaxes; the bones of fallow deer, gazelle, and ancient horses; and […]
Factorio trained a generation of engineers in the exact skill the AI software factory now demands. The agent is the item on the belt, not the machine.
In Wu et. al, the MLE objective is computationally infeasible due to the high number of images in the dataset. Non-parametric Softmax Negative Log-Likelihood With large n, the denominator in (2) is hard to compute. Therefore, they use NCE (Noise-Contrastive Estimation). The NCE Objective Essentially, they approximate the difficult loss in (3) with the easier to compute loss in (7). However, we end up estimating the denominator anyways in (8). Why not just approximate the denominator in (2) with (8)? I asked Claude about this and it said something about it being a biased estimator, but I didn't really get that. I'm also a little confused on the connection of the original NCE formulation as being a way to estimate density and the way it is used here; do we do this because NCE loss is easier to compute and as m (the number of noise samples) increases, we get the gradients of NCE loss and gradients of NLL loss to match? submitted by /u/No_Balance_9777 [link] [Kommentare]
Advocates for the use of trigger warnings suggest that they can help people avoid or emotionally prepare people for encountering content related to a past trauma. But research indicates the warnings only heighten anticipatory anxiety.