Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...

Abstract: This paper presents the design of a framework for loading a pre-trained model in PyTorch on embedded devices to run local inference. Currently, TensorFlow Lite is the most widely used ...

ABSTRACT: This paper explores the application of various time series prediction models to forecast graphical processing unit (GPU) utilization and power draw for machine learning applications using ...

I found that PyTorch torch.nn.Conv2d produces results that differ from TensorFlow, PaddlePaddle, and MindSpore under the same inputs, weights, bias, and hyperparameters. This seems to be a numerical ...

Cybersecurity researchers have discovered vulnerable code in legacy Python packages that could potentially pave the way for a supply chain compromise on the Python Package Index (PyPI) via a domain ...

According to @soumithchintala, referencing @itsclivetime's remarks on X, repeated claims of over 5% speedup versus cuDNN on KernelBench should be met with caution, as many developers have reported ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

Evaluates Python SAST, DAST, IAST and LLM-based security tools that power AI development and vibe coding LOS ALTOS, CA, UNITED STATES, November 6, 2025 /EINPresswire ...

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...

With the PyTorch backend now supporting jit (see issue #4), it is crucial to establish a comprehensive benchmark suite. This suite will be used to evaluate and compare the performance of the PyTorch ...