Table 9) and identifying reflections (Error #20 in Table 13) are also noted.

We now illustrate where example's lie on the plot. We highlight in Figure 10, that well-estimated samples are in the middle as they oscillate around 0.5. We also wish to highlight two types of samples that we DO NOT find in practice.

All training loops and ES are implemented in JAX (Bradbury et al., 2018).

Grids, groups, graphs, geodesics, and gauges.

Our datasets and code are available via the following links: Github: https://github.com/NREL/BuildingsBench As described in Sec. 3 and Sec. 4, Buildings-900K and the BuildingsBench benchmark datasets are B.1 Motivation Q: For what purpose was the dataset created? It specifically addresses a lack of appropriately sized and diverse datasets for pretraining STLF models. We emphasize that the EULP was not originally developed for studying STLF. Rather, it was developed as a general resource to "...help electric utilities, grid operators, manufacturers, Q: Who created the dataset (e.g., which team, research group) and on behalf of which entity Q: Who funded the creation of the dataset?

In this section we provide full descriptions of the diffusion processes introduced in Sec. 3 .

A.3 Other important related works Fundamental sample complexity limits of IL:

The number of layers is 12 for GPT2 and randomly initialized model and 24 for iGPT. Note that these notations are sometimes used interchangeably as long as it doesn't significantly The activation to be analyzed are outputs from all layers . CKA about is shown in Figure 1. The design of the diagram is based on a previous study [35]. Figure 11: Activation we consider to compute CKA.

In addition, it has the same task to predict mortality. A summary of the covariate features can be found in Table 5.

Salem et al., 2018, Y eom et al., 2018, Song and Mittal, 2021] the adversary determines whether a E.g., if the inputs are images, then the adversary must be able to guess a Chen et al., 2021] the adversary aims to steal the trained model functionality. In this attack, the adversary only has black-box access with no prior knowledge of the model parameters or training data, and the outcome of the attack is a model that is approximately the same as the target model. Model-inversion attacks [Fredrikson et al., 2015] are perhaps the closest to our Fredrikson et al. [2015] showed that a face-recognition model can be used to reconstruct images of a certain person. This is done by using gradient descent for obtaining an input that maximizes the output probability that the face-recognition model assigns to a specific class. In Zhang et al. [2020], the authors leverage partial public information to learn That is, they generate images where the target model outputs a high probability for the considered class (as in Fredrikson et al. [2015]), but also encourage realistic images using GAN.