​[[{“value”:” Stanford University is renowned for its advancements in artificial intelligence, which have contributed significantly to cutting-edge research and innovations in the field. Its AI courses, taught by leading experts, offer comprehensive and practical knowledge, equipping students with the skills to tackle real-world challenges and… Read More »Top Artificial Intelligence AI Courses from Stanford Shobha Kakkar Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Transfer learning is particularly beneficial when there is a distribution shift between the source and target datasets and a scarcity of labeled samples in the target dataset. By leveraging knowledge from a related source domain, a pre-trained model can capture general relevant patterns and… Read More »Boosting Classification Accuracy: Integrating Transfer Learning and Data Augmentation for Enhanced Machine Learning Performance Mahmoud Ghorbel Artificial Intelligence Category – MarkTechPost

​[[{“value”:” It is challenging to implement RAG and AI agents effectively in multiple steps. The output of an LLM can be drastically altered by tweaking just a few parameters, such as the definition of a function call or the retrieval parameters. When you write prompts… Read More »Hamming AI: An AI Startup that Provides Fastest Way to Make Your Prompts, RAG, and AI Agents More Reliable Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Luma has introduced Dream Machine, an innovative AI model designed to create high-quality, realistic, fantastical videos from text instructions and images. Built on a scalable, efficient, and multimodal transformer architecture, Dream Machine represents a significant leap in AI technology, specifically tailored for video generation.… Read More »Luma Releases Dream Machine: Transforming Video Creation with AI-Generated High-Quality, Realistic, and Fantastical Scenes from Text and Images Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Stable Diffusion can generate an image based on your input. There are many models that are similar in architecture and pipeline, but their output can be quite different. There are many ways to adjust their behavior, such as when you give a prompt, the… Read More »Fine-Tuning Stable Diffusion with LoRA Kanwal Mehreen MachineLearningMastery.com

​[[{“value”:” Transformer-based generative Large Language Models (LLMs) have shown considerable strength in a broad range of Natural Language Processing (NLP) tasks. Numerous applications benefit from its wide applicability; however, for most developers, the expense of training and implementing these models is frequently prohibitive. For this,… Read More »This AI Paper from China Propose ‘Magnus’: Revolutionizing Efficient LLM Serving for LMaaS with Semantic-Based Request Length Prediction Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Detecting personally identifiable information PII in documents involves navigating various regulations, such as the EU’s General Data Protection Regulation (GDPR) and various U.S. financial data protection laws. These regulations mandate the secure handling of sensitive data, including customer identifiers, financial records, and other personal… Read More »Gretel AI Releases a New Multilingual Synthetic Financial Dataset on HuggingFace 🤗 for AI Developers Tackling Personally Identifiable Information PII Detection Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:”*Equal Contributors Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. However, these approaches assume a priori knowledge of the atypical speech disorder being adapted for — the diagnosis of which requires expert… Read More »Hypernetworks for Personalizing ASR to Atypical Speech Apple Machine Learning Research

​Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. However, keeping the knowledge in LLMs up-to-date remains a challenge once pretraining is complete. It is thus essential to design effective methods to both update obsolete… Read More »Time Sensitive Knowledge Editing through Efficient Finetuning Apple Machine Learning Research

​Voice assistants increasingly use on-device Automatic Speech Recognition (ASR) to ensure speed and privacy. However, due to resource constraints on the device, queries pertaining to complex information domains often require further processing by a search engine. For such applications, we propose a novel Transformer based… Read More »Transformer-based Model for ASR N-Best Rescoring and Rewriting Apple Machine Learning Research