Google Researchers Propose a Formal Boosting Machine Learning Algorithm for Any Loss Function Whose Set of Discontinuities has Zero Lebesgue Measure Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

[[{“value”:” As a very effective machine learning ML-born optimization setting, boosting requires one to efficiently learn arbitrarily good models using a weak learner oracle, which provides classifiers that perform marginally better than random guessing. Although the original boosting model did not necessitate first-order loss information,… Read More »Google Researchers Propose a Formal Boosting Machine Learning Algorithm for Any Loss Function Whose Set of Discontinuities has Zero Lebesgue Measure Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

Researchers at IT University of Copenhagen Propose Self-Organizing Neural Networks for Enhanced Adaptability Aswin Ak Artificial Intelligence Category – MarkTechPost

[[{“value”:” Artificial neural networks (ANNs) traditionally lack the adaptability and plasticity seen in biological neural networks. This limitation poses a significant challenge for their application in dynamic and unpredictable environments. The inability of ANNs to continuously adapt to new information and changing conditions hinders their… Read More »Researchers at IT University of Copenhagen Propose Self-Organizing Neural Networks for Enhanced Adaptability Aswin Ak Artificial Intelligence Category – MarkTechPost

[[{“value”:” In a significant leap forward for the field of code generation, the Knowledge Engineering Group (KEG) and Data Mining team at Tsinghua University have unveiled their latest innovation: CodeGeeX4-ALL-9B. This model, part of the renowned CodeGeeX series, represents the pinnacle of multilingual code generation,… Read More »Tsinghua University Open Sources CodeGeeX4-ALL-9B: A Groundbreaking Multilingual Code Generation Model Outperforming Major Competitors and Elevating Code Assistance Asif Razzaq Artificial Intelligence Category – MarkTechPost

[[{“value”:” Natural language processing (NLP) drives researchers to develop algorithms that enable computers to understand, interpret, and generate human languages. These efforts cover various applications, such as machine translation, sentiment analysis, and intelligent conversational agents. The problem concerns the inefficiencies and limitations of tokenizers used… Read More »T-FREE: A Tokenizer-Free Approach for Efficient and Scalable Text Encoding in Large Language Models Nikhil Artificial Intelligence Category – MarkTechPost

We study the problem of private vector mean estimation in the shuffle model of privacy where nnn users each have a unit vector in ddd dimensions. We propose a new multi-message protocol that achieves the optimal error using O~(min⁡(nε2,d))tilde{mathcal{O}}left(min(nvarepsilon^2,d)right)O~(min(nε2,d)) messages per user. Moreover, we show… Read More »Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages Apple Machine Learning Research

We introduce MIA-Bench, a new benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to strictly adhere to complex instructions. Our benchmark comprises a diverse set of 400 image-prompt pairs, each crafted to challenge the models’ compliance with layered instructions in generating… Read More »MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs Apple Machine Learning Research

[[{“value”:” With recent technological advancements, search engines have significantly improved. An Artificial Intelligence (AI) search engine improves user experience by comprehending user queries at a deeper level than just matching keywords. In order to understand and react to search inputs more accurately and individually, these… Read More »Top Artificial Intelligence AI Search Engines to Know in 2024 Tanya Malhotra Artificial Intelligence Category – MarkTechPost

[[{“value”:” Reinforcement Learning (RL) excels at tackling individual tasks but struggles with multitasking, especially across different robotic forms. World models, which simulate environments, offer scalable solutions but often rely on inefficient, high-variance optimization methods. While large models trained on vast datasets have advanced generalizability in… Read More »Policy Learning with Large World Models: Advancing Multi-Task Reinforcement Learning Efficiency and Performance Sana Hassan Artificial Intelligence Category – MarkTechPost

[[{“value”:” InternLM has unveiled its latest advancement in open large language models, the InternLM2.5-7B-Chat, available in GGUF format. This model is compatible with llama.cpp, an open-source framework for LLM inference, can be utilized locally and in the cloud across various hardware platforms. The GGUF format… Read More »InternLM2.5-7B-Chat: Open Sourcing Large Language Models with Unmatched Reasoning, Long-Context Handling, and Enhanced Tool Use Asif Razzaq Artificial Intelligence Category – MarkTechPost

[[{“value”:” Researchers from the University of Toronto present an insightful examination of the advanced algorithms used in modern ad and content recommendation systems. These systems drive user engagement and revenue generation in digital platforms. It explores various retrieval algorithms and their applications in ad targeting… Read More »A Survey of Advanced Retrieval Algorithms in Ad and Content Recommendation Systems: Mechanisms and Challenges Sana Hassan Artificial Intelligence Category – MarkTechPost