Multi-modal knowledge graphKnowledge graphImageDeep reinforcement learningRecommendationKnowledge graphs (KGs) can provide rich, structured information for recommendation systems as well as increase accuracy and perform explicit reasoning. Deep reinforcement learning (RL) has also sparked great interest in ...
Everett R, Roberts S (2018) Learning against non-stationary agents with opponent modelling and deep reinforcement learning. In: 2018 AAAI Spring symposium series Evtimova K, Drozdov A, Kiela D, Cho K (2018) Emergent communication in a multi-modal, multi-step referential game. In: International...
Multi-modal Deep Learning 的工作,现在最火热的恐怕就是 image caption generation。其实 image caption generation 的思想和大家熟悉的 Machine Tranlsation 非常相似。MT 是从 source language “translate”到另一种 target language;而 image caption generation 可以看成从 image “translate”到 caption text/descr...
Optimal gain scheduling for speed control of ultrasonic motors based on deep reinforcement learning Ultrasonic motors are next-generation actuators with fast response, compact structure, and high torque at low speed.However, due to their non-linearity, th... A Mustafa,T Sasamura,T Morita - 《Pro...
介绍完 multi-modal deep learning 的背景,我们来看下这方面近期工作的论文。先来一篇最新的,《Generating Images From Captions With Attention》,Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba & Ruslan Salakhutdinov,In submission to ICLR 2016。
Datasets of In-Context Learning NamePaperLinkNotes MIMIC-IT MIMIC-IT: Multi-Modal In-Context Instruction Tuning Coming soon Multimodal in-context instruction dataset Datasets of Multimodal Chain-of-Thought NamePaperLinkNotes EgoCOT EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought...
To cope with the problem, this article proposes a Mobile-IoT based multi-modal reinforcement learning service framework from data perspective, which has three highlights, i) Developing Action-aware High-order Transition Tensor ($AHTT$) to fuse the heterogeneous data from M-IoTs in a unified ...
Communication is a critical factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been ado
We describe a framework for multitask deep reinforcement learning guided by policy sketches. Sketches annotate tasks with sequences of named subtasks, prov... J Andreas,K Dan,S Levine 被引量: 76发表: 2016年 On Multiplicative Multitask Feature Learning We investigate a general framework of multip...
讲者:Xiaotie DengChair Professor at Peking University讲座题目:Modeling Multiagent Game Dynamics: Approaches to Equilibrium Computation and Incentive Analysis讲座摘要:This talk explores various research approach, 视频播放量 1501、弹幕量 0、点赞数 63、