Low-Rank GEMM: Efficient Matrix Multiplication via Low-Rank Approximation with FP8 Acceleration

PrismSSL is a newly released Python library that consolidates various self-supervised learning methods across multiple modalities, including audio, vision, and graphs, into a single modular codebase. It allows users to easily install, configure, and run pretext training with minimal code, while also enabling the reproduction of benchmarks and extension of the framework with new methods.

The SciPy library has announced a significant update to its spatial.transform module, which now supports differentiable 3D transformations compatible with various array libraries, including JAX, PyTorch, and CuPy. This overhaul addresses previous limitations related to GPU acceleration and automatic differentiation, enhancing its applicability in machine learning workflows.

TorchQuantumDistributed (tqd) has been introduced as a PyTorch-based library designed for accelerator-agnostic differentiable quantum state vector simulation at scale, facilitating the study of learnable parameterized quantum circuits with high qubit counts.

Recent advancements in seismic wave propagation simulations have highlighted the use of neural operators, which significantly accelerate the process of full waveform inversion. This method, leveraging machine learning, offers a faster alternative to traditional computational techniques like finite difference or finite element methods.