NVIDIA has accelerated its GPU, CPU & AI roadmap significantly as stated by CEO, Jensen Huang, during the latest earnings call.

NVIDIA Will Be Launching Next-Gen GPUs, CPUs & AI Solutions Much Faster Than Everyone Else, Shifts To A 1-Year Cadence Instead of 2-Year

NVIDIA’s current roadmap includes the likes of Hopper H200 and its follow-up Blackwell in B100 & B200 GPUs. The company also previously teased X100 GPUs though we know from recent reports that the actual next-gen architecture comes as the Rubin “R100” series which looks like a major breakthrough for the company based on the specs, performance, and efficiency data that has been laid out.

I found this on NewsBreak: #Design

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Leveraging advanced computational techniques in physical sciences has become vital for accelerating scientific discovery. This involves integrating large language models (LLMs) and simulations to enhance hypothesis generation, experimental design, and data analysis. Automating these processes aims to streamline and democratize access to cutting-edge research tools, pushing the boundaries of scientific knowledge and improving efficiency across various scientific domains.

Researchers face a significant challenge in effectively simulating observational feedback and integrating it with theoretical models in physical sciences. Traditional methods often need a universal approach that can be applied across various scientific fields, leading to inefficiencies and limiting the potential for innovative discoveries. The need for a more comprehensive and adaptable framework is evident to address this issue and advance scientific inquiry.

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The codes to replicate the simulations of the paper: Available at: https://arxiv.org/abs/2405.12832 and also: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4835325

For now, we just added the codes to…

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In this paper, we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability and performance. Traditional multilayer perceptrons (MLPs) and even recent advancements like Spl-KAN \cite{kan} face challenges related to interpretability, training speed, robustness, computational efficiency, and performance. Wav-KAN addresses these limitations by incorporating wavelet functions into the Kolmogorov-Arnold network structure, enabling the network to capture both high-frequency and low-frequency components of the input data efficiently. Wavelet-based approximations employ orthogonal or semi-orthogonal basis and also maintains a balance between accurately representing the underlying data structure and avoiding overfitting to the noise. Analogous to how water conforms to the shape of its container, Wav-KAN adapts to the data structure, resulting in enhanced accuracy, faster training speeds, and increased robustness compared to Spl-KAN and MLPs. Our results highlight the potential of Wav-KAN as a powerful tool for developing interpretable and high-performance neural networks, with applications spanning various fields. This work sets the stage for further exploration and implementation of Wav-KAN in frameworks such as PyTorch, TensorFlow, and also it makes wavelet in KAN in wide-spread usage like nowadays activation functions like ReLU, sigmoid in universal approximation theory (UAT).