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abs: https://arxiv.org/abs/2305.17552 Online Nonstochastic Model-Free Reinforcement Learning In this work, we explore robust model-free reinforcement learning algorithms for environments that may be dynamic or even adversarial. Conventional state-based policies fail to accommodate the challenge imposed by the presence of unmodeled disturbances in arxiv.org 1. 이 연구에서는 동적이거나 적대적일 수 있는 환경에 대한 강건한 모..

https://themiilk.com/articles/a6946e73d?u=15ed0645&t=abef9db15&from&fbclid=IwAR1I6NThOH6Dnb3hrL-ZG_3qbFfC8QEs2dgzaHmwGhNSBkkXIEetHED7_lQ AI 휩쓰는 미국 빅테크에 '디지털 영토' 또 내줄 것인가? - 더밀크 [네이버 클라우드 AI이노베이션 하정우 센터장 인터뷰] ●일본, 동남아, 중동 등 글로벌 시장, 소버린(sovereign) AI서비스 및 인프라 구축 계획 하는 네이버. ●챗GPT등 기존 빅테크 서비스에 종속 막 themiilk.com 하 센터장은 AI가 발전한다면 AGI가 이론적으로는 가능은 하지만, ‘지능(intelligence)를 어떻게 정의하느냐?’에 따라서 달라질 수도 있다. 또한 언..

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https://twitter.com/arankomatsuzaki/status/1663360307274690560?s=20 abs: https://arxiv.org/abs/2305.17333 Fine-Tuning Language Models with Just Forward Passes Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but as LMs grow in size, backpropagation requires a prohibitively large amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients usin..

https://arxiv.org/abs/2305.18274 github: https://medarc-ai.github.io/mindeye/ Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed images from brain activity. Our model comprises two parallel submodules that are specialized for retrieval (using contrastive learning) and re..

abs: https://arxiv.org/abs/2305.18161 VA-learning as a more efficient alternative to Q-learning In reinforcement learning, the advantage function is critical for policy improvement, but is often extracted from a learned Q-function. A natural question is: Why not learn the advantage function directly? In this work, we introduce VA-learning, which dire arxiv.org 1. 강화 학습에서 이점 함수는 정책 개선에 있어 중요하지만, ..

abs: https://arxiv.org/abs/2305.17144 github: https://github.com/OpenGVLab/GITM Ghost in the Minecraft: Generally Capable Agents for Open-World Enviroments via Large Language Models with Text-based Knowledge The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-wo..

abs: https://arxiv.org/abs/2305.17250 Self-Supervised Reinforcement Learning that Transfers using Random Features Model-free reinforcement learning algorithms have exhibited great potential in solving single-task sequential decision-making problems with high-dimensional observations and long horizons, but are known to be hard to generalize across tasks. Model-based RL arxiv.org 설명: https://twitt..

abs: https://arxiv.org/abs/2305.17216 Generating Images with Multimodal Language Models We propose a method to fuse frozen text-only large language models (LLMs) with pre-trained image encoder and decoder models, by mapping between their embedding spaces. Our model demonstrates a wide suite of multimodal capabilities: image retrieval, novel i arxiv.org 설명-> https://twitter.com/kohjingyu/status/1..

abs: https://arxiv.org/abs/2305.17256 Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts). Despite their potential, our understanding of the factors influencing end-tas arxiv.org 1. 대규모 언..