5 citations · 23 across the 21 of their papers we have counts for
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Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features
Adityanarayanan Radhakrishnan, Daniel Beaglehole, Parthe Pandit +1
In recent years neural networks have achieved impressive results on many technological and scientific tasks. Yet, the mechanism through which these models automatically select feat…
Instability and Local Minima in GAN Training with Kernel Discriminators
Evan Becker, Parthe Pandit, Sundeep Rangan +1
Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data. Despite their empirical success, the training of GANs is not fully understood…
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
Neil Mallinar, James B. Simon, Amirhesam Abedsoltan +3
The practical success of overparameterized neural networks has motivated the recent scientific study of interpolating methods, which perfectly fit their training data. Certain inte…
A note on Linear Bottleneck networks and their Transition to Multilinearity
Libin Zhu, Parthe Pandit, Mikhail Belkin
Randomly initialized wide neural networks transition to linear functions of weights as the width grows, in a ball of radius around initialization. A necessary condition for…
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions
Daniel Beaglehole, Mikhail Belkin, Parthe Pandit
``Benign overfitting'', the ability of certain algorithms to interpolate noisy training data and yet perform well out-of-sample, has been a topic of considerable recent interest. W…
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions
Mojtaba Sahraee-Ardakan, Melikasadat Emami, Parthe Pandit +2
Empirical observation of high dimensional phenomena, such as the double descent behaviour, has attracted a lot of interest in understanding classical techniques such as kernel meth…