Seeing it All: LLaVA-UHD Perceives High-Resolution Images at Any Aspect Ratio

Revolutionizing Wireless Communication with Machine Learning Machine Learning (ML) is transforming wireless communication systems, improving tasks like modulation recognition, resource allocation, and signal detection. However, as we rely more on ML, the risk of adversarial attacks…

Transforming Machine Learning with Automatic Differentiation Automatic differentiation has revolutionized machine learning by simplifying the process of calculating gradients. This innovation allows for efficient computation of Jacobian-vector and vector-Jacobian products without needing to construct large matrices,…

Understanding Generative Reward Models (GenRM) What is Reinforcement Learning? Reinforcement Learning (RL) helps AI learn by interacting with its environment. It uses rewards for good actions and penalties for bad ones. A new method called Reinforcement…

Machine learning in healthcare aims to revolutionize medical treatment by predicting tailored outcomes for individual patients. Traditional clinical trials often fail to represent diverse patient populations, hindering the development of effective treatments. Researchers are turning to…

This article discusses the importance of integrating images with large language models (LLMs) to enhance AI capabilities. It introduces the GPT-4 Vision model and outlines the process of using it in a Streamlit application for financial…

Advancements in Large Language Models (LLMs) enabled by Natural Language Processing and Generation have broad applications. However, their biased representations of human viewpoints stemming from pretraining data composition have prompted researchers to focus on data curation.…

Researchers have developed Wonder3D, an innovative method for generating high-quality 3D models from single-view images. It addresses the limitations of existing approaches, such as time-consuming optimization and low-quality results. Wonder3D utilizes a cross-domain attention mechanism and…

Understanding Multimodal Large Language Models (MLLMs) Multimodal Large Language Models (MLLMs) are advanced AI systems that can understand both text and visual information. However, they struggle with detailed tasks like object detection, which is essential for…

Donald Trump’s former lawyer, Michael Cohen, revealed providing his attorney with AI-generated false case citations, which were mistakenly included in a court filing. Cohen admitted to overlooking the potential for generative AI to produce misinformation. This…

Research on conflicts in Scrum teams highlights the impact of latent conflicts on team performance and job satisfaction. However, open conflicts, when managed appropriately, can enhance team creativity and problem-solving abilities. Conflict management determines its effect…

The article emphasizes the challenges and benefits of adopting generative AI in enterprises. It warns about the inaccuracies and potential risks associated with large language models (LLMs) due to hallucinations, but also highlights the necessity and…

Introducing Hugging Face Observers Hugging Face has launched Observers, a powerful tool for improving transparency in generative AI use. This open-source Python SDK makes it easy for developers to track and analyze their interactions with AI…

FineWeb2: A Breakthrough in Multilingual Datasets FineWeb2 enhances multilingual pretraining with over 1000 languages and high-quality data. It utilizes 8 terabytes of compressed text, containing nearly 3 trillion words from 96 CommonCrawl snapshots (2013-2024). This dataset…

Understanding Large Language Models (LLMs) Large Language Models (LLMs) have made significant progress in the last decade. However, they still face challenges in deployment and use, especially regarding: Computational Cost Latency Output Accuracy These issues limit…

Enhancing Efficiency and Performance with Binarized Large Language Models Addressing Challenges with Quantization Transformer-based LLMs like ChatGPT and LLaMA excel in domain-specific tasks, but face computational and storage limitations. Quantization offers practical solutions by converting large…

Self-supervised learning (SSL) is crucial in AI, reducing reliance on labeled data. Evaluating representation quality remains a challenge, with recent limitations in assessing informative features. Apple researchers introduce LiDAR, a novel metric addressing these limitations by…

Practical Solutions for Robotics and IoT Businesses Addressing the Scarcity of DevOps Solutions For robotics and IoT businesses, the lack of mass-produced DevOps solutions often leads to manual SSH/SCP device deployment or the need to develop…

Learn to incorporate Llama Guard into RAG pipelines for moderating LLM inputs/outputs and combating prompt injection. Find more details on Towards Data Science.