Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) community has taken an increased interest in supporting climate scientists' efforts on various tasks such as climate model emulation, downscaling, and prediction tasks. Many of those tasks have been addressed on datasets created with single climate models. However, both the climate science and ML communities have suggested that to address those tasks at scale, we need large, consistent, and ML-ready climate model datasets. Here, we introduce ClimateSet, a dataset containing the inputs and outputs of 36 climate models from the Input4MIPs and CMIP6 archives. In addition, we provide a modular dataset pipeline for retrieving and preprocessing additional climate models and scenarios.

We graciously thank the reviewers for their helpful comments. We clarify some details of the article below. In fact, this article shows that even if FE isn't as mature as homomorphic We do detail and reference many notions from cryptology. ML community may not be familiar with those new concepts, and we sought to introduce them carefully and rigorously. In return, classical notions of ML do not need to be referenced as much because they are well established.

We thank the reviewers for their insightful comments, which we will incorporate into the revised version. We adopt the s2v in our paper since it satisfies these requirements. We will elaborate on the details in our revision. The results are presented in Table 2. Despite the noisiness of the full So our GLN could be further improved with better design choices. We emphasize that the proposed GLN is general enough which is compatible with other parametrizations.

We thank the reviewers for their thorough and positive reviews. We will of course incorporate all the suggested edits by the reviewers as well as more clarifications. We will restate the theorem statement so it would state precisely what is proven. In this paper, we chose to derive the generalization bounds using Graph dimension and VC-dimension. Thank you for your positive review.

The Alps Research Infrastructure leverages GH200 technology at scale, featuring 10,752 GPUs. Accessing Alps provides a significant computational advantage for researchers in Artificial Intelligence (AI) and Machine Learning (ML). While Alps serves a broad range of scientific communities, traditional HPC services alone are not sufficient to meet the dynamic needs of the ML community. This paper presents an initial investigation into extending HPC service capabilities to better support ML workloads. We identify key challenges and gaps we have observed since the early-access phase (2023) of Alps by the Swiss AI community and propose several technological enhancements. These include a user environment designed to facilitate the adoption of HPC for ML workloads, balancing performance with flexibility; a utility for rapid performance screening of ML applications during development; observability capabilities and data products for inspecting ongoing large-scale ML workloads; a utility to simplify the vetting of allocated nodes for compute readiness; a service plane infrastructure to deploy various types of workloads, including support and inference services; and a storage infrastructure tailored to the specific needs of ML workloads. These enhancements aim to facilitate the execution of ML workloads on HPC systems, increase system usability and resilience, and better align with the needs of the ML community. We also discuss our current approach to security aspects. This paper concludes by placing these proposals in the broader context of changes in the communities served by HPC infrastructure like ours.

A resnet-18 is used as a feature extractor for each frame. The timeseries of features is then fed to an LSTM to predict the wind speed. A specialized dataset is also collected for the aforementioned task.

After Rebuttal: Thank you for the responses. I that believe the paper will be even stronger with the inclusion of the stochastic gradient-variant. This is a very valuable theorem, which will be useful for other theoreticians working in this field. On the other hand, to the best of my knowledge, this is the first paper that uses a stochastic Runge-Kutta integrator for sampling from strongly log-concave densities with explicit guarantees. The authors further show that their proposed numerical scheme improves upon the existing guarantees when applied to the overdamped Langevin dynamics.