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In this episode, we discuss Welcome to the Era of Experience by David Silver, Richard S. Sutton. The paper discusses the forthcoming era of artificial intelligence marked by agents with superhuman capabilities. These agents will primarily learn through experience. The note highlights the essential features that will characterize this new phase in AI development.
arxiv preprint - Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions
arxiv preprint - Evaluating Human Alignment and Model Faithfulness of LLM Rationale
arxiv preprint - Detection and Measurement of Syntactic Templates in Generated Text
arxiv preprint - From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data
arxiv preprint - MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning
arxiv preprint - 4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
arxiv preprint - VideoLLM-online: Online Video Large Language Model for Streaming Video
arxiv preprint - EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
arxiv preprint - MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model
arxiv preprint - An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels
arxiv preprint - Graphic Design with Large Multimodal Model
arxiv preprint - LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning
arxiv preprint - Transformers need glasses! Information over-squashing in language tasks
arxiv preprint - Show, Don’t Tell: Aligning Language Models with Demonstrated Feedback
arxiv preprint - TextGrad: Automatic ”Differentiation” via Text
arxiv preprint - SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales
arxiv preprint - Open-Endedness is Essential for Artificial Superhuman Intelligence
arxiv preprint - To Believe or Not to Believe Your LLM
arxiv preprint - Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
arxiv preprint - Contextual Position Encoding: Learning to Count What’s Important
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