Information exists in various forms in the real world, and the effective interaction and fusion of multimodal information plays a key role in the research
In this review, we focus on the developmental and learning aspects of brain organization while we refer the reader to Soltoggio et al. (2017) for a review of evolutionary imprinting. 2.2. Hebbian plasticity and stability The ability of the brain to adapt to changes in its environment ...
Few-shot learning旨在在数据稀缺的情况下通过少量训练样本学习机器学习系统。有许多方法可以实现few-shot learning,包括模型无关的元学习(Finn等,2017)(迅速适应新任务的学习特征),嵌入学习(Bertinetto等,2016)(将每个样本嵌入到一个较低维度的空间中,其中相似的样本彼此靠近),基于记忆的学习(Kaiser等,2017)(通过从...
By identifying those features, we provide a path towards the design of systems that better support learning. The paper provides new insights on the role of multimodal data in technology enhanced learning. In particular, we make the following contributions: The paper is structured as follows. The ...
in general machine learning, representative works27,28have demonstrated that large-scale vision-language representation learning can augment vision-only AI models with new capabilities, including zero-shot image recognition and text-to-image retrieval. Depending on the architectural design, training data ...
self-supervised learning has become an attractive strategy to alleviate the annotation bottleneck. Building on these two directions, self-supervised multimodal learning (SSML) provides ways to learn from raw multimodal data. In this survey, we provide a comprehensive review of the state-of-the-art ...
multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. We also survey current multimodal applications and present a collection ...
哥的Multimodal Unsupervised Image-to-Image Translation 等文章。下面博文中的文章引用序号因为要随着更新的太多了,我这里就不更新啦~ 下面的序号是和arXiv上v4版本([1705.04058v4] Neural Style Transfer: A Review)是对应的,大家找文章时候可以去下载一下v4,谢谢~ 如果...
Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but achieving fine-resolution and high-accuracy deconvolution remains a challenge. Here we introduce MeDuSA, a mixed model-based method that le
Forget Unlearning: Towards True Data-Deletion in Machine Learning 2022 Chourasia et al. ICLR - - noisy gradient descent Zero-Shot Machine Unlearning 2022 Chundawat et al. arXiv - - Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations 2022 Guo et...