We thank the reviewers for their insightful feedback; we address each review below. R1: "...it is known how to solve the BFE optimisation problem by double loop algorithms" "...what is meant by'they are run once..."' "...meaningless for pairwise marginals..." Agreed. We included the pairwise marginals just for completeness. "Ising model...expect that the estimation quality will degrade with the (average) interaction strength." "The experiments have in my view a preliminary character" We agree our experiments are on small datasets.

We observe interesting patterns from the results presented in Table 2.

We thank the reviewers for their insightful feedback; we address each review below. R1: "...it is known how to solve the BFE optimisation problem by double loop algorithms" "...what is meant by'they are run once..."' "...meaningless for pairwise marginals..." Agreed. We included the pairwise marginals just for completeness. "Ising model...expect that the estimation quality will degrade with the (average) interaction strength." "The experiments have in my view a preliminary character" We agree our experiments are on small datasets.

Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision. A quantized model executes some or all of the operations on tensors with reduced precision rather than full precision (floating point) values. This allows for a more compact model representation and the use of high performance vectorized operations on many hardware platforms. PyTorch supports INT8 quantization compared to typical FP32 models allowing for a 4x reduction in the model size and a 4x reduction in memory bandwidth requirements. Hardware support for INT8 computations is typically 2 to 4 times faster compared to FP32 compute.

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. Click here to join our Slack community! Whether you are a machine learning researcher or first-time user of machine learning toolkits, here are some reasons to try out PyG for machine learning on graph-structured data.

With the release of the new version of Pytorch 1.12, Pytorch has come up with a new DataFrame library for data visualization or preprocessing named Torcharrow. Torcharrow is a Pytorch library for data processing and visualization with support for processing tabular data and is more suitable for deep learning data. Torcharrow has come up with the ability of faster processing of data by lighter usage of the processing unit. In this article let us get a brief overview of the latest preprocessing library of Pytorch 1.12 named Torcharrow. Pytorch, an open-sourced machine learning and deep learning framework based on the torch library is used in various applications like computer vision and Natural Language processing.

Ashish Ranjan Jha received his Bachelors degree in Electrical Engineering from IIT Roorkee (India), Masters degree in Computer Science from EPFL (Switzerland) and an MBA degree from Quantic School of Business (Washington). He has received distinction in all 3 of his degrees. He has worked for large technology companies like Oracle, Sony as well as the more recent tech unicorns such as Revolut, mostly focussed around Artificial Intelligence. He currently works as a Machine Learning Engineer. Ashish has several years of working experience and specialisation in the field of Machine Learning, and Python is his go-to tool.

PyTorch is a widely used, open source deep learning platform used for easily writing neural network layers in Python enabling a seamless workflow from research to production. Based on Torch, PyTorch has become a powerful machine learning framework favored by esteemed researchers around the world, and now adopted fully by Facebook. The new PyTorch 1.10.0 release is composed of over 3,400 commits since 1.9, made by 426 contributors. PyTorch 1.10 updates are focused on improving training and performance of PyTorch, and developer usability. You can check the blogpost that shows the new features here.

Model training has been and will be in the foreseeable future one of the most frustrating things machine learning developers face. It takes quite a long time and people can't really do anything about it. If you have the luxury (especially at this moment of time) of having multiple GPUs, you are likely to find Distributed Data Parallel (DDP) helpful in terms of model training. DDP performs model training across multiple GPUs, in a transparent fashion. You can have multiple GPUs on a single machine, or multiple machines separately.