​[[{“value”:” One of the cornerstone challenges in machine learning, time series forecasting has made groundbreaking contributions to several domains. However, forecasting models can’t generalize the distribution shift that changes with time because time series data is inherently non-stationary. Based on the assumptions about the inter-instance… Read More »This Machine Learning Paper Presents a General Data Generation Process for Non-Stationary Time Series Forecasting Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Artificial Intelligence (AI) and Deep Learning, with a focus on Natural Language Processing (NLP), have seen substantial changes in the last few years. The area has advanced quickly in both theoretical development and practical applications, from the early days of Recurrent Neural Networks (RNNs)… Read More »Google DeepMind Introduces Two Unique Machine Learning Models, Hawk And Griffin, Combining Gated Linear Recurrences With Local Attention For Efficient Language Models Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In recent years, the AI community has witnessed a significant surge in developing large language models (LLMs) such as ChatGPT, Bard, and Claude. These models have demonstrated exceptional capabilities, from enhancing dialogue systems to improving logical reasoning and coding. However, their vast size and… Read More »Redefining Compact AI: MBZUAI’s MobiLlama Delivers Cutting-Edge Performance in Small Language Models Domain Muhammad Athar Ganaie Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Sponsored Content     In my decade of teaching online, the most significant inspiration has been that online learning democratizes access to education globally. Regardless of your ethnic background, income level, and geographical location—as long as you can surf the web—you can find an… Read More »Best Free Resources to Learn Data Analysis and Data Science MLM Team MachineLearningMastery.com

​[[{“value”:” In the rapidly evolving field of artificial intelligence, the development and application of large language models (LLMs) stand at the forefront of innovation, offering unparalleled data processing and analysis capabilities. These sophisticated models, characterized by their vast parameter spaces, have demonstrated exceptional proficiency in… Read More »Can AI Think Better by Breaking Down Problems? Insights from a Joint Apple and University of Michigan Study on Enhancing Large Language Models Muhammad Athar Ganaie Artificial Intelligence Category – MarkTechPost

​Paper abstract: Large-scale web-crawled datasets are fundamental for the success of pre-training vision-language models, such as CLIP. However, the inherent noise and potential irrelevance of web-crawled AltTexts pose challenges in achieving precise image-text alignment. Existing methods utilizing large language models (LLMs) for caption rewriting have… Read More »VeCLIP: Improving CLIP Training via Visual-enriched Captions Apple Machine Learning Research

​[[{“value”:” Engineering effective prompts for LLMs is crucial yet challenging due to their sensitivity to prompts and the ambiguity of task instructions. Recent studies propose using meta-prompts that learn from past trials to suggest improved prompts automatically. However, evaluating prompt effectiveness requires high-quality benchmarks, often… Read More »Automated Prompt Engineering: Leveraging Synthetic Data and Meta-Prompts for Enhanced LLM Performance Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Researchers from Microsoft attempt to solve the challenge faced in predicting molecular properties and simulating molecular dynamics by presenting a method, ViSNet, that results in more accurate predictions. Predicting molecular properties is crucial for understanding structure-activity relationships (SAR) in drug discovery, biotechnology, and materials… Read More »Microsoft Researchers Propose ViSNet: An Equivariant Geometry-Enhanced Graph Neural Network for Predicting Molecular Properties and Simulating Molecular Dynamics  Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” From producing writing that resembles that of a human being to comprehending subtleties of language, Large Language Models (LLMs) have played a key role in attaining state-of-the-art performance in various Natural Language Processing (NLP) applications. However, their efficacy wanes when processing texts exceeding their… Read More »Efficiently Processing Extended Contexts in Large Language Models: Dual Chunk Attention for Training-Free Long-Context Support Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The recent success of large language models relies heavily on extensive text datasets for pre-training. However, indiscriminate use of all available data may not be optimal due to varying quality. Data selection methods are crucial for optimizing training datasets and reducing costs and carbon… Read More »Maximizing Efficiency in AI Training: A Deep Dive into Data Selection Practices and Future Directions Mohammad Asjad Artificial Intelligence Category – MarkTechPost