The provided text is a technical article covering the implementation and explanation of a multilayer neural network from scratch. It discusses the foundations, implementation, training, hyperparameter tuning, and conclusions about the network, along with sections on activation, loss function, backpropagation, and dataset. It also includes code for implementation and examples of mathematical notation and equations…

This article discusses three measures of distance: Earth Mover’s Distance (EMD) for image search, Word Mover’s Distance (WMD) for document retrieval, and Concept Mover’s Distance (CMD) for analyzing concepts within texts. The measures progress from tangible to abstract, impacting their analytical power. The CMD, utilizing an “ideal pseudo document,” distinguishes itself by presuming likeness analytically,…

The article introduces the use of Directed Acyclic Graphs (DAG) and backdoor criterion in causal inference for experimental settings to select good control variables. It explains the process through a data science problem of influencing sustainable behavior and includes examples and simulated experiments in R to demonstrate the application. The article emphasizes the importance of…

The text discusses the author’s experience with AI-generated image models, particularly focusing on diffusion models for image generation from text prompts. The author highlights the theoretical foundations of these models, their training process, and conditioning on input like text prompts. They refer to key research papers and discuss applications of the models, emphasizing their generative…

Researchers from multiple universities have developed Gemini, a comprehensive framework for optimizing performance, energy efficiency, and monetary cost (MC) in DNN chiplet accelerators. Gemini employs innovative encoding and mapping strategies, a dynamic programming-based graph partition algorithm, and a Simulated-Annealing-based approach for optimization. Experimentation demonstrates Gemini’s superiority over existing state-of-the-art designs.

Rust Burn is a new deep learning framework developed in Rust, prioritizing flexibility, performance, and ease of use. It leverages hardware-specific features, such as Nvidia’s Tensor Cores, for fast performance. With a broad feature set and a growing developer community, it shows potential to address existing framework limitations and become a versatile deep learning solution.

The review explores the evolution and challenges of Large Language Models (LLMs) such as ChatGPT, highlighting their transition from traditional statistical models to neural network-based ones like the Transformer architecture. It delves into the training, fine-tuning, evaluation, utilization, and future advancements of LLMs, emphasizing ethical considerations and societal impact. For more details, refer to the…

The increasing use of cloud-hosted large language models raises privacy concerns. Secure Multi-Party Computing (SMPC) is a solution, but applying it to Privacy-Preserving Inference (PPI) for Transformer models causes performance issues. SecFormer is introduced to balance performance and efficiency in PPI, demonstrating improvements in privacy and performance for large language models.