marktechpost4d
Microsoft Releases GRIN MoE: A Gradient-Informed Mixture of Experts MoE Model for Efficient and Scalable Deep Learning
Artificial intelligence (AI) research has increasingly focused on enhancing the efficiency & scalability of deep learning models. These models have revolutionized natural language processing, computer ...
marktechpost4d
Advancing Membrane Science: The Role of Machine Learning in Optimization and Innovation
Machine Learning in Membrane Science: ML significantly transforms natural sciences, particularly cheminformatics and materials science, including membrane technology. This review focuses on current ML ...
marktechpost4d
LightOn Released FC-AMF-OCR Dataset: A 9.3 Million Images Dataset of Financial Documents with Full OCR Annotations
The release of the FC-AMF-OCR Dataset by LightOn marks a significant milestone in optical character recognition (OCR) and machine learning. This dataset is a technical achievement and a cornerstone ...
marktechpost4d
Contextual Retrieval: An Advanced AI Technique that Reduces Incorrect Chunk Retrieval Rates by up to 67%
The development of Artificial Intelligence (AI) models, especially in specialized contexts, depends on how well they can access and use prior information. For example, legal AI tools need to be ...
marktechpost4d
LASR: A Novel Machine Learning Approach to Symbolic Regression Using Large Language Models
Symbolic regression is an advanced computational method to find mathematical equations that best explain a dataset. Unlike traditional regression, which fits data to predefined models, symbolic ...
marktechpost4d
ZML: A High-Performance AI Inference Stack that can Parallelize and Run Deep Learning Systems on Various Hardware
Inference is the process of applying a trained AI model to new data, which is a fundamental step in many AI applications. As AI applications grow in complexity and scale, traditional inference stacks ...
marktechpost4d
Persona-Plug (PPlug): A Lightweight Plug-and-Play Model for Personalized Language Generation
Personalization is essential in many language tasks, as users with similar needs may prefer different outputs based on personal preferences. Traditional methods involve fine-tuning language models for ...
marktechpost5d
Comprehensive Evaluation of Quantized Instruction-Tuned LLMs: Exploring Quantization Methods for Models Ranging from 7B to 405B Parameters
Large Language Models (LLMs) have gained significant attention due to their impressive performance, with the release of Llama 3.1 in July 2024 being a notable example. However, deploying these models ...
marktechpost5d
Sketch: An Innovative AI Toolkit Designed to Streamline LLM Operations Across Diverse Fields
Large language models (LLMs) have made significant leaps in natural language processing, demonstrating remarkable generalization capabilities across diverse tasks. However, due to inconsistent ...
marktechpost5d
This AI Paper by NVIDIA Introduces NVLM 1.0: A Family of Multimodal Large Language Models with Improved Text and Image Processing Capabilities
Multimodal large language models (MLLMs) focus on creating artificial intelligence (AI) systems that can interpret textual and visual data seamlessly. These models aim to bridge the gap between ...
marktechpost5d
Efficient Long-Term Prediction of Chaotic Systems Using Physics-Informed Neural Operators: Overcoming Limitations of Traditional Closure Models
Predicting the long-term behavior of chaotic systems, such as those used in climate modeling, is essential but requires significant computational resources due to the need for high-resolution ...
marktechpost6d
Jina-Embeddings-v3 Released: A Multilingual Multi-Task Text Embedding Model Designed for a Variety of NLP Applications
Text embedding models have become foundational in natural language processing (NLP). These models convert text into high-dimensional vectors that capture semantic relationships, enabling tasks like ...