Contract theory studies the setting where a principal seeks to design a contract for rewarding an agent on the basis of the uncertain outcomes caused by the agent's private actions [

Proposition 2.5 is a direct consequence of the following lemma (remember that Lemma A.1 (Smooth functions conserved through a given flow.) . Assume that @h () ()=0 for all 2 . Let us first show the direct inclusion. Now let us show the converse inclusion. We recall (cf Example 2.10 and Example 2.11) that linear and Assumption 2.9, which we recall reads as: Theorem 2.14, let us show that (9) holds for standard ML losses.

Understanding the fundamental principles behind the success of deep neural networks is one of the most important open questions in the current literature.

In practice, when training denoisers, we sample multiple noisy samples for each clean image and minimize the Mean Squared Error (MSE) loss for recovering the clean image.

In practice, when training denoisers, we sample multiple noisy samples for each clean image and minimize the Mean Squared Error (MSE) loss for recovering the clean image.

GPN applies a personalized page rank (PPR) module to diffuse the evidence among neighboring nodes.

SmoothHess is applied post-hoc, requires no modifications to the ReLU network architecture, and the extent of smoothing can be controlled explicitly.

We present a PT AS for learning random constant-depth networks.