IEEE Access的动态

Our paper "Learning Embedding Representations in High Dimensions" is now available online! Abstract: Embeddings are a basic initial feature extraction step in many machine learning models, particularly in natural language processing. An embedding attempts to map data tokens to a low-dimensional space where similar tokens are mapped to vectors that are close to one another by some metric in the embedding space. A basic question is how well can such embedding be learned? To study this problem, we consider a simple probability model for discrete data where there is some "true" but unknown embedding where the correlation of random variables is related to the similarity of the embeddings. Under this model, it is shown that the embeddings can be learned by a variant of low-rank approximate message passing (AMP) method. The AMP approach enables precise predictions of the accuracy of the estimation in certain high-dimensional limits. In particular, the methodology provides insight on the relations of key parameters such as the number of samples per value, the frequency of the terms, and the strength of the embedding correlation on the probability distribution. Our theoretical findings are validated by simulations on both synthetic data and real text data. Read more: https://lnkd.in/g-sqjXYN

Researchers from MIT developed a technique that teaches machine-learning models to identify specific actions in long videos. https://lnkd.in/gx8sTy9g #MachineLearning #ArtificialIntelligence #DeepLearning #VideoAnalysis #ComputerVision #MITResearch #ActionRecognition #TechInnovation #DataScience #AIResearch #FutureOfAI #AIApplications #TechNews #ResearchAndDevelopment

Now that AI can code, learning to code is more important than ever (and AI is here to help). See new and ongoing guidance for CS classrooms: teachai.org/cs. Let’s #TeachAI! #CSed The guidance was developed by TeachAI and the Computer Science Teachers Association (CSTA) in partnership with Association for the Advancement of Artificial Intelligence (AAAI), #AI4K12, Code.org, Digital Promise, #EverydayAI, the Gesellschaft für Informatik e.V., Grok Academy, Karen Brennan, Shuchi Grover, Maya Israel, and Matti Tedre. The guidance was informed and reviewed by organizations from the TeachAI advisory committee and working groups, CSTA members, government agencies, and policymakers. TeachAI is led by a steering committee of Code.org, ETS, ISTE, Khan Academy, and the World Economic Forum.

Guidance on the Future of Computer Science Education in an Age of AI TeachAI

#Deep #learning has #revolutionized #computer #vision over the #past #decade. Now, a new #class of #vision-#language #models (#VLMs) are set to revolutionize computer vision once again. Closely related to large language models (#LLMs), VLMs can help with challenges like: ? #Ambiguity: #Difficulty in distinguishing between similar #actions or #objects. ? #Challenging $imaging #conditions: #Problems with #identifying #objects in #poor #visibility or #complex #environments. ? #Accessibility: #Costs and #complexity of obtaining large #amounts of #training #data.

Exciting to see the AI & ML content and questions go live!

Computers in Libraries 2024 Conference! A session on teaching students how to use AI accurately and ethically

This paper proposes a multi-objective optimization problem that minimizes #Virtual #Network #Functions (#VNF) computational costs and overhead of periodical reconfigurations simultaneously. Their solution uses constrained combinatorial optimization with #deep #reinforcement #learning (#DRL), where an agent minimizes a penalized cost function calculated by the proposed optimization problem. ---- Esmaeil Amiri, @Ning Wang, Mohammad Shojafar, @Mutasem Q. Hamdan, Chuan Heng Foh More details can be found at this link: https://lnkd.in/gxiSea-P

???? Embracing Lifelong Learning: The Essential Trait for Computer Scientists In the fast-paced world of technology, staying ahead means staying updated. As a computer scientist, the constant evolution driven by AI innovations demands a commitment to continuous learning. In a landscape where every breakthrough redefines the industry, the adage holds true: everyone wants to earn, but no one wants to learn. ? Let's embrace learning as the cornerstone of our success. Are you ready to dive into the ever-changing currents of tech innovation? Let's learn, grow, and thrive together. #TechInnovation #ContinuousLearning #ComputerScience

?? Exciting News! ?? I am thrilled to share that I’ve completed the "Unlocking LLMs and Creative Prompt Engineering" workshop! ??This has equipped me with advanced skills in leveraging large language models for innovative solutions. A huge thank you to Arun Chinnachamy ? for his invaluable guidance and support. Your insights were instrumental in my learning journey. Excited to apply these new skills to drive innovation and enhance problem-solving in my projects. ?? Let’s connect if you’re interested in AI and prompt engineering!?? #AI #MachineLearning #PromptEngineering #Innovation ?? #LLMs ??