总结——A multimodal deep learning framework for predicting drug–drug interaction events DDIMDL首先利用药物的化学亚结构、靶点、酶和通路四类特征,分别构建基于深度神经网络的子模型,然后采用联合DNN框架将这些子模型组合起来,学习药物-药物对的跨模表征,预测DDI事件。 基于相似性的方法是这些方法中的一个主要类别...
近期在arXiv上发布出一本新的名为《Multimodal Deep Learning》,是德国的一个seminar里,好多人一起整理出来在multimodal领域里对SOTA的综述。全书272页,很综合的对这个方向的工作以及展望进行了完整的阐述,看了下,还可以,开源的书,也是免费的,推荐给大家。书的结构如下: Multimodal Deep Learning 感兴趣的朋友可以去...
deep-learning prompt artificial-intelligence multimodal gpt4 prompt-learning prompt-tuning prompt-engineering chatgpt Updated Oct 29, 2024 Python IDEA-CCNL / Fengshenbang-LM Star 4.1k Code Issues Pull requests Discussions Fengshenbang-LM(封神榜大模型)是IDEA研究院认知计算与自然语言研究中心主导的...
多模态深度学习(英文名:Multimodal Deep Learning)是人工智能(AI)的一个子领域,其重点是开发能够同时处理和学习多种类型数据的模型。这些数据类型,或称模态,可以包括文本、图像、音频、视频和传感器数据等。通过结合这些不同的模式,多模态深度学习旨在创建更强大和多功能的人工智能系统,能够更好地理解、解释复杂的现实...
Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based models to solve different multimodal problems such as multimodal representation learning, multimodal fusion for downstream tasks e.g., multimodal sentiment analy...
Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and ...
Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer’s disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accom...
相关学科:Cross-Modal RetrievalVisual Question AnsweringMultimodal Emotion RecognitionMovement PredictionGamma Belief NetworkVideo Emotion RecognitionActivity SegmentationEfficientNetB0nnU-NetHand Gesture Classification 学科讨论 暂无讨论内容,你可以 推荐文献 发布年度 ...
The success of deep learning has been a catalyst to solving increasingly complex machine-learning problems, which often involve multiple data modalities. We review recent advances in deep multimodal learning and highlight the state-of the art, as well as gaps and challenges in this active research...
What is the Goal of Multimodal Deep Learning? The primary goal of Multimodal Deep Learning is to create a shared representation space that can effectively capture complementary information from different modalities. This shared representation can then be used to perform various tasks, such as image ca...