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[Graph&Text] ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings https://arxiv.org/abs/2305.14321 ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings We propose ConGraT(Contrastive Graph-Text pretraining), a general, self-supervised method for jointly learning separate representations of texts and nodes in a parent (or ``supervening'') graph, where each text is associated with one of the nodes. Datasets arxiv.org
How Do Recommender Systems Work?
[CL] End-to-End Incremental Learning 0. Abstract catastrophic forgetting을 #incremental learning 을 통해 해결하고자 함. 전체 frame work 를 E2E으로 구성. 즉, data representation과 classifier를 jointly learn CIFAR-100 , ImageNet을 통해 Evaluate 함. 1. Introduction Main challenge : 현실에 적용하여 incremental하게 학습할 수 있는 classifier를 위한 / visual recognition system 구축 기존의 모델은 new data + old data 조금으로는 학습 불가능함. (시도는 있었으나 성능저하 극심했음.) Incremental DL approach flow of data..
[CL] Lifelong Learning with Dynamically Expandable Networks (ICLR 2018) https://openreview.net/pdf?id=Sk7KsfW0- 0. Abstarct Dynamically Expandable Network (DEN) dynamically decide its network capacity as it trains on a sequence of tasks trained in an online manner by performing selective retraining 1 Introduction lifelong learning → trained in an online manner by performing selective retraining [ Strategy 1 ] Fine-tune : training both origin & new task → degenerate ..
[ML] VAE(Variational Autoencoder) 보호되어 있는 글입니다.
[MMML] Multimodal Deep Learning (ICML2011) https://people.csail.mit.edu/khosla/papers/icml2011_ngiam.pdf 0. Abstract THIS paper a series of tasks for multimodal learning & how to train cross modality feature learning how to learn a shared representation between modalities 1. Introduction start of MMML : speech recognition (audio-visual information), McGurk effect THIS paper focus on modeling "mid-level" relationships task : audio-visual ..
[ML] RBM & sparse RBM 보호되어 있는 글입니다.
[MMML] Multimodal Machine Learning Introduction (CMU LTI-11777 Lecture1.1)