RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health RecordsThe paper “RAM-EHR” introduces a new model based on retrieval augmented generation and deep learning for clinical predictions. Let’s go…10h ago10h ago
BioLORD: Contrastive learning for biomedical conceptsThe paper “BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)”…4d ago4d ago
Graph Language ModelsThe paper “Graph Language Models” introduces a novel approach for jointly modeling graph and text information. Let’s go through it.Nov 13Nov 13
CLIP: Aligning images and text with contrastive learningThe paper “Learning Transferable Visual Models From Natural Language Supervision” introduces the CLIP (Contrastive Language–Image…Nov 8Nov 8
Learning to build GPT: my takeawaysThe lecture on building GPT by Andrej Karpathy is a MUST-watch for everyone who’s in NLP. In this post, I’ll be summarizing my notes from…Oct 25Oct 25
The gradual information-fusing neural modelHello, this post is based on lecture 4 by Andrej Karpathy on building neural nets from scratch.Oct 23Oct 23
The essentials of activations, gradients, and batch normalizationHello everyone. As you know, I’m watching the lectures by Andrej Karpathy on building neural networks from scratch.Oct 14Oct 14
The neural probabilistic language modelIn our previous post, we discussed count-based and simple neural bigram models. The problem with both is that they only consider the…Oct 10Oct 10
From count-based to neural bigram modelsIn this post, I will summarize my notes from the 2nd lecture of the Zero to Hero course by Andrej Karpathy, which concerns building…Oct 8Oct 8
