(Drawing by Max Graenitz) I train machine learning programs called neural networks - they work by looking at lists of data and then deducing their own rules about how to generate similar data. They’re used in everything from ad targeting to facial recognition to self-driving cars, but I use them for humor by giving them very silly datasets

Neural Concept Linking (NCL) for healthcare concept linking with COM-AID, addressing word mismatch and overlapping concept senses. Encode-decode with dual attention injects textual and structural

DEBOSH utilizes Bayesian Optimization (BO) and Graph Neural Networks (GNNs) in order to effectively explore the space of possible shapes and acquire the best performing samples according to physical properties

where Innovation meets Impact

https://gigadom.in/2019/01/15/my-presentations-on-elements-of-neural-networks-deep-learning-parts-45/ My presentations on ‘Elements of Neural Networks & Deep Learning’ -Parts 4,5

Abstract page for arXiv paper 2010.15327: Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth

1. どんなもの？

Picture by Retronator Deep learning is a very weird technology. It evolved over decades on a very different track than the mainstream of AI, kept alive by the efforts of a handful of believers. When I started using it a few years ago, it reminded me of the first time I played with an iPhone -…

ISCA Archive Interspeech 2020 ISCA Archive Interspeech 2020 Effect of Adding Positional Information on Convolutional Neural Networks for End-to-End Speech Recognition Jinhwan Park, Wonyong Sung Attention-based models with convolutional encoders enable faster training and inference than recurrent neural network-based ones. However, convolutional models often require a very large receptive field to achieve high recognition accuracy, which not only increases the parameter size but also the computational cost a

jarxiv Japanese arxiv コンテンツへスキップ ホーム ← Differentially Private Non-convex Learning for Multi-layer Neural Networks Data-Centric Learning from Unlabeled Graphs with Diffusion Model → Understanding Sparse Feature Updates in Deep Networks using Iterative Linearisation 投稿日: 2023年10月13日 作成者: jarxiv 要約 大規模で深いネットワークは、オーバーフィットする能力が増加しているにもかかわらず、うまく一般化します