Sampling in discrete spaces, with critical applications in simulation and optimization, has recently been boosted by significant advances in gradient-based approaches that exploit modern accelerators like GPUs. However, two key challenges are hindering further advancement in research on discrete sampling.

In real-world applications, though, out-of-distribution tasks are highly prevalent, e.g., bin

We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo.

Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery.

Tensor decompositions (TDs) serve as a powerful tool for analyzing multiway data.

By plugging in Eq. (4), we have X Next, we derive the dynamics of the membrane potential. For the E neurons, we proceed similarly. We cite a theorem from [46]. We apply Thm. 1 to our minimax objective, for the maximization problem with The last two terms are related to nonlinear neural activations. Next we show that the energy function is decreasing.