Project Open Se Cura is an open-source framework introduced by Google to enhance the development of secure and efficient AI systems. It aims to bridge the gap between hardware breakthroughs and advances in machine learning models and software development. The collaborative effort with partners like VeriSilicon, Antmicro, and lowRISC focuses on creating open-source design tools…

NetEase Youdao has released an open-source text-to-speech (TTS) engine called “Yi Mo Sheng.” It offers web and script interfaces, allowing for batch result generation, making it suitable for applications requiring emotional synthesis of voices. The engine supports over 2,000 timbres, Chinese and English languages, and includes a unique emotion synthesis feature. Another competitor in the…

A recent research paper presents a deep learning-based classifier for age-related macular degeneration (AMD) stages using retinal optical coherence tomography (OCT) scans. The model accurately classifies macula-centered 3D volumes into Normal, early/intermediate AMD (iAMD), atrophic (GA), and neovascular (nAMD) stages. The study highlights the significance of accurate AMD staging for timely treatment initiation and emphasizes…

Researchers from MIT investigated the scaling behavior of large chemical language models, including generative pre-trained transformers (GPT) for chemistry and graph neural network force fields (GNNs). They introduced the concept of neural scaling, examining the impact of model and data size on pre-training loss. The study also explored hyperparameter optimization using a technique called Training…

Dynamic view synthesis is a technique used in computer vision and graphics to reconstruct dynamic 3D scenes from videos. Traditional methods have limitations in terms of rendering speed and quality. However, a new approach called 4K4D has been introduced, which utilizes a 4D point cloud representation and a hybrid appearance model to achieve faster rendering…

A team of researchers from Jiaotong University, Peking University, and Microsoft have developed a method called LeMa that improves the mathematical reasoning abilities of large language models (LLMs) by teaching them to learn from mistakes. They fine-tune the LLMs using mistake-correction data pairs generated by GPT-4. LeMa consistently improves performance across various LLMs and tasks,…

In this research, a Gaussian Mixture Model (GMM) is proposed as a reverse transition operator in the Denoising Diffusion Implicit Models (DDIM) framework. By constraining the GMM parameters to match the first and second order central moments of the forward marginals, samples of equal or better quality than the original DDIM with Gaussian kernels can…

Large Language Models (LLMs) with billions of parameters have revolutionized AI but are computationally intensive. This study supports the use of ReLU activation in LLMs as it minimally affects performance but reduces computation and weight transfer. Alternative activation functions like GELU or SiLU are popular but more computationally demanding.

This work proposes a novel architecture to detect user-defined flexible keywords in real-time. The approach involves constructing acoustic embeddings of keywords using graphene-to-phone conversion, and converting phone-to-embedding by looking up the embedding dictionary built during training. The key benefit is the incorporation of both text and audio embedding.

Behavioral testing in NLP evaluates system capabilities by analyzing input-output behavior. However, current tests for Machine Translation are limited and manually created. To overcome this, our proposal suggests using Large Language Models (LLMs) to generate diverse source sentences for testing MT model behavior in various scenarios. Verification ensures expected performance.