Data Science by ODS.ai 🦜

👑Llama 3 is here, with a brand new tokenizer! 🦙 Вышла Llama 3 Meta выпустила новую SOTA Llama 3 в двух версиях на 8B и 70B параметров. Длина контекста 8К, поддержка 30 языков. •HF: https://huggingface.co/spaces/ysharma/Chat_with_Meta_llama3_8b •Blog: https://ai.meta.com/blog/meta-llama-3/ Вы можете потестить 🦙 MetaLlama 3 70B и 🦙 Meta Llama 3 8B с помощью 🔥 бесплатного интерфейса: https://llama3.replicate.dev/ @ai_machinelearning_big_data

⚡️Map-relative Pose Regression🔥(#CVPR2024 highlight) For years absolute pose regression did not work. There was some success by massively synthesising scene-specific data. We train scene-agnostic APR and it works. Paper: https://arxiv.org/abs/2404.09884 Page: https://nianticlabs.github.io/marepo @opendatascience

🔥 ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback Proposes an approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency proj: https://liming-ai.github.io/ControlNet_Plus_Plus/ abs: https://arxiv.org/abs/2404.07987 @opendatascience

🥔 YaART: Yet Another ART Rendering Technology 💚 This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences using Reinforcement Learning from Human Feedback (RLHF). 💜 During the development of YaART, Yandex especially focus on the choices of the model and training dataset sizes, the aspects that were not systematically investigated for text-to-image cascaded diffusion models before. 💖 In particular, researchers comprehensively analyze how these choices affect both the efficiency of the training process and the quality of the generated images, which are highly important in practice. ▪Paper page - https://ya.ru/ai/art/paper-yaart-v1 ▪Arxiv - https://arxiv.org/abs/2404.05666 ▪Habr - https://habr.com/ru/companies/yandex/articles/805745/ @opendatascience

⚡️ PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models Significantly improved finetuned perf by simply changing the initialization of LoRA's AB matrix from Gaussian/zero to principal components of W ▪Github: https://github.com/GraphPKU/PiSSA ▪Paper: https://arxiv.org/abs/2404.02948 @opendatascience

⚡️ Awesome CVPR 2024 Papers, Workshops, Challenges, and Tutorials! На конференцию 2024 года по компьютерному зрению и распознаванию образов (CVPR) поступило 11 532 статей, из которых только 2 719 были приняты, что составляет около 23,6% от общего числа. Ниже приведен список лучших докладов, гайдов, статей, семинаров и датасетов с CVPR 2024. ▪Github @ai_machinelearning_big_data

Objective-Driven AI: Towards AI systems that can learn, remember, reason, and plan A presentation by Yann Lecun on the #SOTA in #DL YouTube: https://www.youtube.com/watch?v=MiqLoAZFRSE Slides: Google Doc Paper: Open Review P.S. Stole the post from @chillhousetech

Let’s get back to posting 😌

Position: Analyst/Researcher for AI Team at Cyber.fund About Cyber.fund: Cyber.fund is a pioneering $100mm research-driven fund specializing in the realm of web3, decentralized AI, autonomous agents, and self-sovereign identity. Our legacy is built upon being the architects behind monumental projects such as Lido, p2p.org, =nil; foundation, Neutron, NEON, and early investments in groundbreaking technologies like Solana, Ethereum, EigenLayer among 150+ others. We are committed to advancing the frontiers of Fully Homomorphic Encryption (FHE) for Machine Learning, privacy-first ML (Large Language Models), AI aggregations, and routing platforms alongside decentralized AI solutions. Who Are We Looking For? A dynamic individual who straddles the worlds of business acumen and academic rigor with: - A robust theoretical foundation in Computer Science and a must-have specialization in Machine Learning. - An educational background from a technical university, with a preference for PhD holders from prestigious institutions like MIT or МФТИ. - A track record of publications in the Machine Learning domain, ideally at the level of NeuroIPS. - Experience working in startups or major tech companies, ideally coupled with a background in angel investing. - A profound understanding of algorithms, techniques, and models in ML, with an exceptional ability to translate these into innovative products. - Fluent English, intellectual curiosity, and a fervent passion for keeping abreast of the latest developments in AI/ML. Responsibilities: 1) Investment Due Diligence: Conduct technical, product, and business analysis of potential AI/ML investments. This includes market analysis, engaging with founders and technical teams, and evaluating the scalability, reliability, risks, and limitations of products. 2) Portcos Support: Provide strategic and technical support to portfolio companies in AI/ML. Assist in crafting technological strategies, hiring, industry networking, identifying potential project challenges, and devising solutions. 3) Market and Technology Research: Stay at the forefront of ML/DL/AI trends (e.g., synthetic data, flash attention, 1bit LLM, FHE for ML, JEPA, etc.). Write publications, whitepapers, and potentially host X spaces/streams/podcasts on these subjects (in English). Identify promising companies and projects for investment opportunities. How to Apply? If you find yourself aligning with our requirements and are excited by the opportunity to contribute to our vision, please send your CV to [email protected]. Including a cover letter, links to publications, open-source contributions, and other achievements will be advantageous. Location: Location is flexible, but the candidate should be within the time zones ranging from EET to EST (Eastern Europe to the East Coast of the USA). This is not just a job opportunity; it's a call to be part of a visionary journey reshaping the landscape of AI and decentralized technology. Join us at Cyber.fund and be at the forefront of the technological revolution.

LLM models are in their childhood years Source.