9 citations · 35 across the 23 of their papers we have counts for
8 papers · 1 filter
Asymptotic convexity of wide and shallow neural networks
Vivek Borkar, Parthe Pandit
For a simple model of shallow and wide neural networks, we show that the epigraph of its input-output map as a function of the network parameters approximates epigraph of a. convex…
Feature maps for the Laplacian kernel and its generalizations
Sudhendu Ahir, Parthe Pandit
Recent applications of kernel methods in machine learning have seen a renewed interest in the Laplacian kernel, due to its stability to the bandwidth hyperparameter in comparison t…
Fast training of large kernel models with delayed projections
Amirhesam Abedsoltan, Siyuan Ma, Parthe Pandit +1
Classical kernel machines have historically faced significant challenges in scaling to large datasets and model sizes--a key ingredient that has driven the success of neural networ…
Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime
Parthe Pandit, Zhichao Wang, Yizhe Zhu
Kernel ridge regression (KRR) is a popular class of machine learning models that has become an important tool for understanding deep learning. Much of the focus thus far has been o…
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
Neil Mallinar, Daniel Beaglehole, Libin Zhu +3
Neural networks trained to solve modular arithmetic tasks exhibit grokking, a phenomenon where the test accuracy starts improving long after the model achieves 100% training accura…
On the Nystrom Approximation for Preconditioning in Kernel Machines
Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher +1
Kernel methods are a popular class of nonlinear predictive models in machine learning. Scalable algorithms for learning kernel models need to be iterative in nature, but convergenc…
Mechanism of feature learning in convolutional neural networks
Daniel Beaglehole, Adityanarayanan Radhakrishnan, Parthe Pandit +1
Understanding the mechanism of how convolutional neural networks learn features from image data is a fundamental problem in machine learning and computer vision. In this work, we i…
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…