Second opinion needed: communicating uncertainty in medical machine learning
There is great excitement that medical artificial intelligence (AI) based on machine learning
(ML) can be used to improve decision making at the patient level in a variety of healthcare …
(ML) can be used to improve decision making at the patient level in a variety of healthcare …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
Resmlp: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image
classification. It is a simple residual network that alternates (i) a linear layer in which image …
classification. It is a simple residual network that alternates (i) a linear layer in which image …
Similarity of neural network representations revisited
Recent work has sought to understand the behavior of neural networks by comparing
representations between layers and between different trained models. We examine methods …
representations between layers and between different trained models. We examine methods …
Wide neural networks of any depth evolve as linear models under gradient descent
A longstanding goal in deep learning research has been to precisely characterize training
and generalization. However, the often complex loss landscapes of neural networks have …
and generalization. However, the often complex loss landscapes of neural networks have …
On exact computation with an infinitely wide neural net
How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard
dataset such as CIFAR-10 when its “width”—namely, number of channels in convolutional …
dataset such as CIFAR-10 when its “width”—namely, number of channels in convolutional …
The generalization error of random features regression: Precise asymptotics and the double descent curve
Deep learning methods operate in regimes that defy the traditional statistical mindset.
Neural network architectures often contain more parameters than training samples, and are …
Neural network architectures often contain more parameters than training samples, and are …
Dataset distillation with infinitely wide convolutional networks
The effectiveness of machine learning algorithms arises from being able to extract useful
features from large amounts of data. As model and dataset sizes increase, dataset …
features from large amounts of data. As model and dataset sizes increase, dataset …
Explaining neural scaling laws
The population loss of trained deep neural networks often follows precise power-law scaling
relations with either the size of the training dataset or the number of parameters in the …
relations with either the size of the training dataset or the number of parameters in the …
Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth
A key factor in the success of deep neural networks is the ability to scale models to improve
performance by varying the architecture depth and width. This simple property of neural …
performance by varying the architecture depth and width. This simple property of neural …