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Here's everything you need to know about convolutional neural networks, from how they work to their various applications

The Neural Tangent Kernel (NTK) is an important milestone in the ongoing effort to build a theory for deep learning. Its prediction that sufficiently wide neural networks behave as kernel methods, or equivalently as random feature models, has been confirmed empirically for certain wide architectures. It remains an open question how well NTK theory models standard neural network architectures of widths common in practice, trained on complex datasets such as ImageNet. We study this question empirically for tw

Neural ordinary differential equations Chen et al., NeurIPS'18 ‘Neural Ordinary Differential Equations’ won a best paper award at NeurIPS last month. It’s not an easy piece (at least not for me!), but in the spirit of ‘deliberate practice’ that doesn’t mean there isn’t something to be gained from trying to understand as much as possible

A recent work by Ramanujan et al. (2020) provides significant empirical evidence that sufficiently overparameterized, random neural networks contain untrained subnetworks that achieve

A brief post on residual networks with some experiments on variational autoencoders

(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