Browsing: ML News
As LLMs become increasingly integral to various AI tasks, their massive parameter sizes lead to high memory requirements and bandwidth consumption. While quantization-aware training (QAT) offers…
Evaluating conversational AI assistants, like GitHub Copilot Chat, is challenging due to their reliance on language models and chat-based interfaces. Existing metrics for conversational quality need…
Survey on Machine Learning-Powered Augmented Reality in Education: ML advances augmented reality (AR) across various educational fields, enhancing object visualizations and interaction capabilities. This survey outlines…
Evaluating the effectiveness of Large Language Model (LLM) compression techniques is a crucial challenge in AI. Compression methods like quantization aim to optimize LLM efficiency by…
Large Language Models (LLMs) have made significant strides in artificial intelligence, but their ability to process complex structured data, particularly graphs, remains challenging. In our interconnected…
Researchers are struggling with the challenge of causal discovery in heterogeneous time-series data, where a single causal model cannot capture diverse causal mechanisms. Traditional methods for…
Telecommunications involves the transmission of information over distances to communicate. It encompasses various technologies like radio, television, satellite, and the internet, enabling voice, data, and video…
Deep Visual Proteomics: Integrating AI and Mass Spectrometry for Cellular Phenotyping: Deep Visual Proteomics (DVP) revolutionizes the analysis of cellular phenotypes by combining advanced microscopy, AI,…
Machine learning, particularly deep neural networks, focuses on developing models that accurately predict outcomes and quantify the uncertainty associated with those predictions. This dual focus is…
Scaling Transformer-based models to over 100 billion parameters has led to groundbreaking results in natural language processing. These large language models excel in various applications, but…