The Arc Institute has launched the Virtual Cell Challenge, which requires participants to develop a model that predicts the effects of gene silencing in cells, a process known as context generalization. The challenge aims to simul...
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The text discusses the need for domain-specific architectures (DSAs) for AI inference due to the increasing demand for AI inference. It emphasizes the importance of efficiency in reducing energy and capital requirements and highli...
The text discusses the step-by-step discovery of state-of-the-art positional encoding in transformer models, starting with the problem statement and moving on to the desirable properties of an optimal encoding scheme. It then expl...
0A first principles analysis of Smartglasses
2024-07-19 •
The text is a deep dive into the use cases and technical challenges of AR glasses. It discusses the potential of AR glasses, the challenges they face, and the future of AR glasses.
0Layer Normalization as fast as possible
2023-12-11 •
The text discusses the importance of LayerNorm and RMSNorm in deep learning, and how to implement a fast LayerNorm kernel for big models in the browser on the GPU. It explains the challenges of parallelizing the computation of eac...
0Running LLMs in the Browser with Rust + WebGPU
2023-04-26 •
With WebGPU now available on stable Chrome, the possibilities for web-based applications are expanding. The author has developed a pre-pre-alpha library called laserbeak, which allows users to run large machine learning models loc...
0A case for client-side machine learning
2022-11-01 •
The post discusses the future of machine learning, focusing on the shift from cloud-based to client-side machine learning. It examines the feasibility of client-side machine learning, the potential for dynamic machine learning, an...
0A Rust based ML model analyzer
2022-10-18 •
The author explores Rust & Machine Learning, building an ONNX inference framework in Rust, and creating a visualizer called Steelix. Steelix is ONNX only and supports a limited set of operators, but can produce a DOT file for any ...
The text explores the common ways of quantifying time series similarity in a neuroscientific setting, focusing on the transformation from the timeseries output of the fMRI scan to a matrix of similarity values. It covers covarianc...