Hyponatremia: Predict whether a hyponatremia lab comes back as normal (>=135 mmol/L), mild (>=130 and <135 mmol/L), moderate (>=125 and <130 mmol/L), or severe (<125 mmol/L). We consider all lab results coded as LOINC/LG11363-5, LOINC/2951-2, or LOINC/2947-0. Anemia: Predict whether an anemia lab comes back as normal (>=120 g/L), mild (>=110 and <120 g/L), moderate (>=70 and <110 g/L), or severe (<70 g/L). We consider all lab results coded as LOINC/LP392452-1. Please note that for the results of our baseline experiments in Section 5, we reframe these lab value tasks as binary classification tasks, where a label is "negative" if the result is normal and "positive" otherwise.

Q6 How many instances are there in total (of each type, if appropriate)?

For completeness, in the following we include a datasheet based on the format of [1]. For what purpose was the dataset created? Was there a specific task in mind? Who created the dataset (e.g., which team, research group) and on behalf of which entity What do the instances that comprise the dataset represent (e.g., documents, photos, people, The dataset contains episodes of human demonstrations for mobile device control. How many instances are there in total (of each type, if appropriate)?

We thank reviewers for their valuable comments. We respond to the main concerns below. Similar to that in Zhang et al. [31], we chose 10k block ResNet to stress the We will rephrase L243 to better express this. Derivative of weights depend on this term due to the chain rule. We will make this explicit in the revised manuscript.

First, a random patch of the image is selected and resized to224 224 with a random horizontal flip, followed byacolor distortion, consisting ofarandom sequence ofbrightness, contrast, saturation, hue adjustments, and anoptional grayscale conversion. FinallyGaussian blur and solarization are appliedtothepatches. Optimization We use theLARS optimizer [70] with a cosine decay learning rate schedule [71], without restarts, over1000epochs, with awarm-up period of10epochs. Wesetthebase learning rate to 0.2, scaled linearly [72] with the batch size (LearningRate = 0.2 BatchSize/256). Forthetargetnetwork,the exponential moving average parameterτ starts fromτbase = 0.996and is increased to one during training.

We propose a novel approach to learning the generative neural fields represented by linear combinations of implicit basis networks.