For example, a prediction market on whether GPT -4 will be able to consistently solve "easy" Sudoku puzzles from the LA Times has remained open for several months at the time of

A typical way to learn a latent variable model is maximizing the marginal likelihood [21].

Typical examples are hyperparameters selection [5, 38, 17, 6], data augmentation [11, 42], implicit deep learning [3] or neural architecture search [33]. Figure 1: Convergence curves of the two proposed methods on a toy problem.

Mitigating these biases relies on exposing and removingnuisance components.

In the "modern era", such model-parallel training techniques trace their roots back to AlexNet [14] and early influential systems such as DistBelief [6] and Project Adam [3].