… In this work, we primarily focus on the formulation of implicit models based on root-solving,
represented by the DEQ models [2]. The table of notations is arranged in Appendix A. …

… implicit modeling of a simple layer F substantially improves the training speed, memory, and
performance on implicit … However, we argue that the training on many implicit representation …

… models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, …
We present techniques to scale MCMC based EBM training on continuous neural networks, …

… Toward understanding this implicit bias, we prove that SGD with standard mini-batch
noise implicitly prefers flatter minima of pre-training loss in language models, and empirically …

… on the Implicit Differentiation on the Equilibrium state (IDE). Inspired by recent advances in
implicit models [… equation and propose alternative implicit models defined by the equation, we …

… models we now understand how rich and natural implicit bias, often inducing sparsity of some
form, can arise when training … shed light on the implicit bias hidden in the training process …

… Most of the modeling work has focused on the AGL and SL tasks, and this chapter therefore
… This and other productions operate on the declarative chunks acquired over training by the …

… Concretely, in each of the training iteration, the denoising loss L({xt}T … Gφ implicitly for fast
and tractable optimization. As visualized in Figure 3, we impose a linear diffusion model on the …

… as an alternative method for training implicit models. An accurate … With a focus on learning
implicit models, we have empirically … methods to training implicit generative models without the …

… models are amongst the most fundamental of models, eg, many of the basic methods for
generating non-uniform random variates are based on simple implicit models … phase of training, …