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Double descent in statistics and machine learning is the phenomenon where a model with a small number of parameters and a model with an extremely large number of parameters both have a small training error, but a model whose number of parameters is about the same as the number of data points used to train the model will have a much greater test ...
Illustration of gradient descent on a series of level sets. Gradient descent is based on the observation that if the multi-variable function is defined and differentiable in a neighborhood of a point , then () decreases fastest if one goes from in the direction of the negative gradient of at , ().
It’s known that if the weight vector is initialized close to zero, least-squares gradient descent converges to the minimum-norm solution, i.e., the final weight vector has the minimum Euclidean norm of all the interpolating solutions. In the same way, kernel gradient descent yields the minimum-norm solution with respect to the RKHS norm. This ...
The DNC is differentiable end-to-end (each subcomponent of the model is differentiable, therefore so is the whole model). This makes it possible to optimize them efficiently using gradient descent. [3] [6] [7] The DNC model is similar to the Von Neumann architecture, and because of the resizability of memory, it is Turing complete. [8]
Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.
While grokking has been thought of as largely a phenomenon of relatively shallow models, grokking has been observed in deep neural networks and non-neural models and is the subject of active research. [6] [7] [8] [9]
A deep CNN of (Dan Cireșan et al., 2011) at IDSIA was 60 times faster than an equivalent CPU implementation. [12] Between May 15, 2011, and September 10, 2012, their CNN won four image competitions and achieved SOTA for multiple image databases. [13] [14] [15] According to the AlexNet paper, [1] Cireșan's earlier net is "somewhat similar."
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).