​[[{“value”:” Identifying gene deletion strategies for growth-coupled production in genome-scale metabolic models presents significant computational challenges. Growth-coupled production, which links cell growth to the synthesis of target metabolites, is essential for metabolic engineering applications. However, deriving gene deletion strategies for large-scale models places high computational… Read More »DBgDel: Database-Enhanced Gene Deletion Framework for Growth-Coupled Production in Genome-Scale Metabolic Models Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In recent times, Retrieval-augmented generation (RAG) has become popular due to its ability to solve challenges using Large Language Models, such as hallucinations and outdated training data. A RAG pipeline consists of two components: a retriever and a reader. The retriever component finds useful… Read More »Balancing Accuracy and Speed in RAG Systems: Insights into Optimized Retrieval Techniques Divyesh Vitthal Jawkhede Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Self-supervised learning on offline datasets has permitted large models to reach remarkable capabilities both in text and image domains. Still, analogous generalizations for agents acting sequentially in decision-making problems are difficult to attain. The environments of classical Reinforcement Learning (RL) are mostly narrow and… Read More »Kinetix: An Open-Ended Universe of Physics-based Tasks for Reinforcement Learning Nazmi Syed Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are particularly effective when dealing with complex datasets. The core principle behind SVM is to identify the optimal… Read More »Support Vector Machine (SVM) Algorithm Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have revolutionized artificial intelligence applications across various fields, enabling domain experts to use pre-trained models for innovative solutions. While LLMs excel at tasks like summarization, correlation, and inference, developing LLM-based applications remains a dynamic area of research across various input… Read More »GraphAide: Building and Utilizing Knowledge Graphs for Domain-Specific Digital Assistants Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Understanding biomolecular interactions is crucial for fields like drug discovery and protein design. Traditionally, determining the three-dimensional structure of proteins and other biomolecules required costly and time-consuming laboratory experiments. AlphaFold3, launched in 2024, revolutionized the field by demonstrating that deep learning could achieve experimental-level… Read More »MIT Researchers Propose Boltz-1: The First Open-Source AI Model Achieving AlphaFold3-Level Accuracy in Biomolecular Structure Prediction Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Modern language models have transformed our daily interactions with technology, offering tools that help draft emails, write articles, code software, and much more. However, these powerful models often come with significant limitations. Many language models today are hamstrung by overly cautious guardrails that restrict… Read More »Meet Beepo-22B: The Unrestricted AI Finetuned Model based on Mistral Small Instruct 22B Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:”This paper was accepted at the Self-Supervised Learning – Theory and Practice (SSLTP) Workshop at NeurIPS 2024. Image-based Joint-Embedding Predictive Architecture (IJEPA) offers an attractive alternative to Masked Autoencoder (MAE) for representation learning using the Masked Image Modeling framework. IJEPA drives representations to capture useful… Read More »Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning Apple Machine Learning Research

​This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting, a strong case of out-of-distribution generalization. This is motivated by the fact that the rich combinatorial nature of data in certain reasoning tasks (e.g., arithmetic/logic)… Read More »Generalization on the Unseen, Logic Reasoning and Degree Curriculum Apple Machine Learning Research

​[[{“value”:”This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) Workshop at NeurIPS 2024. Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture… Read More »Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models Apple Machine Learning Research