EASYJOY TECHNOLOGIES’ Post

AlphaProof and AlphaGeometry 2 are steps toward building systems that can reason, which could unlock exciting new capabilities.

Great overview of compression algorithms for LLMs. Covers compression algorithms like pruning, quantization, knowledge distillation, low-rank approximation, parameter sharing, and efficient architecture design. This space is moving so fast. This is just a nice overview which also includes future research ideas and topics. #llm #cnn #transferlearning #ai

Recently listened to a great discussion between Oliver Thomas and Arturo Tedeschi, focusing on the future of architecture, computational design, and artificial intelligence. Arturo, known for his seminal work "AAD Algorithms-Aided Design," is at the forefront of blending computational design with AI in various fields. His book has significantly influenced the current and future direction of computationally led design. For emerging designers, Arturo advises two things: a strong focus on algorithmic thinking, and the ability to be highly adaptable in our learning, vital approaches in an ever evolving world. My takeaways: A focus on learning how to learn, and to approach this by finding leverage and efficiency through applying mental models like the pareto principle. It also won't hurt to lean into your curiosities...

🔥 Excited to share this groundbreaking blog post on Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning. This paper introduces a novel approach, Meta-GRL, to address the low generalization and practicality challenges in current stochastic power dispatch research. The proposed method leverages a contextual Markov decision process and scalable graph representation to achieve a highly generalized multi-stage stochastic power dispatch modeling. The results demonstrate the superiority of Meta-GRL in terms of optimality, efficiency, adaptability, and scalability compared to state-of-the-art policies and traditional reinforcement learning. Read the full post here: https://bit.ly/3UaxqP6 [cs.LG] #ReinforcementLearning #PowerDispatch #MetaGRL #Optimization #AIResearch

A paper in @Nature presents an artificial intelligence system that can solve International Mathematical Olympiad-level geometry problems, outperforming the previous best automated theorem prover. go.nature.com/42aaFwO

Exploring the boundless potential of Claude's artifact generation today! 🚀 Already leveraged it to design a study manipulation and create a class demonstration of the Bass model. The possibilities seem limitless - what should I tackle next? 🤔💡 For those curious about Claude's capabilities, check out this article showcasing some creative examples: https://lnkd.in/dWa-3SSg #AIInnovation #MachineLearning #DataScience #AIResearch #FutureOfTech #ArtificialIntelligence #AIEducation #TechTrends2024 #InnovationInAction #AIApplications

🚀 𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐑𝐞𝐚𝐝: "𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐔𝐩 𝐭𝐨 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞: 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐟𝐨𝐫 𝐏𝐡𝐨𝐭𝐨-𝐑𝐞𝐚𝐥𝐢𝐬𝐭𝐢𝐜 𝐈𝐦𝐚𝐠𝐞 𝐑𝐞𝐬𝐭𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐈𝐧 𝐭𝐡𝐞 𝐖𝐢𝐥𝐝" 🖼️ As a data enthusiast and a keen follower of advancements in AI and machine learning, I'm thrilled to share this groundbreaking work in the realm of image restoration (IR). The study underscores a monumental leap in perceptual quality and intelligent photo-realistic restoration. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐭𝐡𝐢𝐬 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐢𝐬 𝐚 𝐠𝐚𝐦𝐞-𝐜𝐡𝐚𝐧𝐠𝐞𝐫: - Leveraging powerful generative models, it pushes the envelope in high-quality image generation. - It introduces the largest-ever IR method known as SUPIR, capable of intelligent and ultra-high-quality image production. - Through model scaling, SUPIR employs a generative prior with 2.6 billion parameters, coupled with a 600 million-parameter adaptor. - An impressive database of over 20 million high-res images paired with descriptive text catapults its training capability. 💡 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: - The SUPIR model showcases exceptional performance in complex real-world IR tasks, setting a new industry standard. - It provides granular control over the restoration process with textual prompts, expanding the horizons of IR applications. - Innovative solutions like ZeroSFT and robust image encoders tackle scaling challenges and model fidelity, crafting a future-forward IR methodology. 🔍 This comprehensive study not only scales up the IR models but also fine-tunes our understanding of AI's potential in practical applications. 🔗 https://lnkd.in/eCrZ7f4a Feel free to dive into the article for a deeper look into how image restoration is evolving to meet the complexities of real-world applications. Let's discuss how these advancements can reshape the future of image processing! #ImageRestoration #AI #MachineLearning #DeepLearning #Innovation #TechTrends

📢 Less than a week to go until our first ever "Transformative Technologies" themed Techfast in partnership with REdirect Consulting and hosted by Dentons.    We are pleased to introduce our second speaker Alan Mosca, Co-Founder & CTO at nPlan! Alan leads technology, research, and product, whilst developing thought leadership about forecasting and risk. Alan has extensive experience in algorithm design and software engineering and holds a BEng in Computer Engineering, MSc in Computer Science, and doctoral research in machine learning theory.   🎤 Alan : "LLM? Generative AI? Reinforcement Learning? I'll try to demystify some of the more common notions about modern AI, followed by an example of how nPlan addresses fundamental issues in construction, infrastructure and engineering using AI. Finally, I'll try to translate some of the learnings back to real estate and development and stimulate some further conversation." ⏹ Final few spaces remaining, if you haven't already registered, secure your place now : https://lnkd.in/davjjzaW

🚨Paper  Alert 🚨 ➡️Paper Title: VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models 🌟Few pointers from the paper 🎯In this paper authors have introduced VideoElevator, a training-free and plug-and-play method, which elevates the performance of T2V using superior capabilities of T2I. 🎯Different from conventional T2V sampling (i.e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating. 🎯Specifically, temporal motion refining uses encapsulated T2V to enhance temporal consistency, followed by inverting to the noise distribution required by T2I. Then, spatial quality elevating harnesses inflated T2I to directly predict less noisy latent, adding more photo-realistic details. 🎯The authors have conducted experiments in extensive prompts under the combination of various T2V and T2I. The results show that VideoElevator not only improves the performance of T2V baselines with foundational T2I, but also facilitates stylistic video synthesis with personalized T2I. 🏢Organization: Harbin Institute of Technology Tsinghua University 🧙Paper Authors: 张亚博, Yuxiang Wei, Xianhui Lin, Zheng Hui, Peiran Ren, Xuansong Xie, Xiangyang Ji, Wangmeng Zuo 1️⃣Read the Full Paper here:https://lnkd.in/grcYWu-C 2️⃣Project Page: https://lnkd.in/ghwzswCM 3️⃣Code (Coming Soon) : https://lnkd.in/gEPh7vGA 🎥 Be sure to watch the attached demo video-Sound on 🔊🔊 Music by Maksym Dudchyk from Pixabay Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #generatieveai #texttovideo

👉 If you’re diving into amortized inference, check out BayesFlow's Awesome Amortized Inference repository  🔗 https://lnkd.in/g5sncNW3 📘 This is a carefully curated collection of essential resources: papers, tools, and reviews spanning simulation-based and more comprehensive inference methods. Marvin Schmitt has spearheaded a valuable effort to put together a repository that invites community contributions, making it a go-to resource for anyone in the field. ✅ Worth bookmarking if you’re in ML or research. Take a look, and maybe even add a resource of your own! #AmortizedInference #MachineLearning #AIResearch