​[[{“value”:” In the evolving field of machine learning, fine-tuning foundation models such as BERT or LLAMA for specific downstream tasks has become a prevalent approach. However, the success of such fine-tuning depends not only on the model but also heavily on the quality and relevance… Read More »Task-Specific Data Selection: A Practical Approach to Enhance Fine-Tuning Efficiency and Performance Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Vision Transformers (ViTs) have revolutionized computer vision by offering an innovative architecture that uses self-attention mechanisms to process image data. Unlike Convolutional Neural Networks (CNNs), which rely on convolutional layers for feature extraction, ViTs divide images into smaller patches and treat them as individual… Read More »Attention Transfer: A Novel Machine Learning Approach for Efficient Vision Transformer Pre-Training and Fine-Tuning Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Matching patients to suitable clinical trials is a pivotal but highly challenging process in modern medical research. It involves analyzing complex patient medical histories and mapping them against considerable levels of detail found in trial eligibility criteria. These criteria are complex, ambiguous, and heterogeneous,… Read More »This AI Paper Unveils TrialGPT: Revolutionizing Patient-to-Trial Matching with Precision and Speed Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Generative agents are computational models replicating human behavior and attitudes across diverse contexts. These models aim to simulate individual responses to various stimuli, making them invaluable tools for exploring human interactions and testing hypotheses in sociology, psychology, and political science. By integrating artificial intelligence,… Read More »This AI Paper Introduces Interview-Based Generative Agents: Accurate and Bias-Reduced Simulations of Human Behavior Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In the evolving landscape of artificial intelligence, building language models capable of replicating human understanding and reasoning remains a significant challenge. One major hurdle in the development of large language models (LLMs) is balancing computational efficiency with expansive capabilities. As models grow larger to… Read More »Chinese AGI Startup ‘StepFun’ Developed ‘Step-2’: A New Trillion-Parameter MoE Architecture Model Ranking 5th on Livebench Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Generating high-quality, real-time video simulations poses significant challenges, especially when aiming for extended lengths without compromising quality. Traditionally, world models for video generation have faced limitations due to high computational costs, short video duration, and lack of real-time interactivity. The use of manually configured… Read More »Meet The Matrix: A New AI Approach to Infinite-Length and Real-Time Video Generation Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Quantum computing, despite its potential to outperform classical systems in certain tasks, faces a significant challenge: error correction. Quantum systems are highly sensitive to noise, and even the smallest environmental disturbance can lead to computation errors, affecting the expected outcomes. Unlike classical systems, which… Read More »Google Researchers Developed AlphaQubit: A Deep Learning-based Decoder for Quantum Computing Error Detection Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:”*Equal Contributors A dominant paradigm in large multimodal models is to pair a large language de- coder with a vision encoder. While it is well-known how to pre-train and tune language decoders for multimodal tasks, it is less clear how the vision encoder should be… Read More »Multimodal Autoregressive Pre-Training of Large Vision Encoders Apple Machine Learning Research

​[[{“value”:”Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that… Read More »Instance-Optimal Private Density Estimation in the Wasserstein Distance Apple Machine Learning Research

​Our new AI system accurately identifies errors inside quantum computers, helping to make this new technology more reliable. Our new AI system accurately identifies errors inside quantum computers, helping to make this new technology more reliable.  Read More