Google TechTalks | Fast Neural Kernel Embeddings for General Activations @GoogleTechTalks | Uploaded February 2023 | Updated October 2024, 1 week ago.
A Google TechTalk, presented by Insu Han, 2023-02-02
Algorithms Seminar Series. ABSTRACT: Infinite width limit has shed light on generalization and optimization aspects of deep learning by establishing connections between neural networks and kernel methods. Despite their importance, the utility of these kernel methods was limited in large-scale learning settings due to their (super-)quadratic runtime and memory complexities. Moreover, most prior works on neural kernels have focused on the ReLU activation, mainly due to its popularity but also due to the difficulty of computing such kernels for general activations. In this work, we overcome such difficulties by providing methods to work with general activations. First, we compile and expand the list of activation functions admitting exact dual activation expressions to compute neural kernels. When the exact computation is unknown, we present methods to effectively approximate them. We propose a fast sketching method that approximates any multi-layered Neural Network Gaussian Process (NNGP) kernel and Neural Tangent Kernel (NTK) matrices for a wide range of activation functions, going beyond the commonly analyzed ReLU activation. This is done by showing how to approximate the neural kernels using the truncated Hermite expansion of any desired activation functions. While most prior works require data points on the unit sphere, our methods do not suffer from such limitations and are applicable to any dataset of points in ℝd. Furthermore, we provide a subspace embedding for NNGP and NTK matrices with near input-sparsity runtime and near-optimal target dimension which applies to any \emph{homogeneous} dual activation functions with rapidly convergent Taylor expansion. Empirically, with respect to exact convolutional NTK (CNTK) computation, our method achieves 106× speedup for approximate CNTK of a 5-layer Myrtle network on CIFAR-10 dataset.
Bio: Insu Han is a postdoctoral research fellow in the Department of Electrical Engineering at Yale University, hosted by Prof. Amin Karbasi. He obtained his Ph.D. degree at Korea Advanced Institute of Science and Technology (KAIST) in 2021 under the supervision of Prof. Jinwoo Shin. He was a recipient of Microsoft Research Asia Fellowship in 2019. His research interests focus on approximate algorithm design and analysis for large-scale machine learning problems and their applications.
A Google TechTalk, presented by Insu Han, 2023-02-02
Algorithms Seminar Series. ABSTRACT: Infinite width limit has shed light on generalization and optimization aspects of deep learning by establishing connections between neural networks and kernel methods. Despite their importance, the utility of these kernel methods was limited in large-scale learning settings due to their (super-)quadratic runtime and memory complexities. Moreover, most prior works on neural kernels have focused on the ReLU activation, mainly due to its popularity but also due to the difficulty of computing such kernels for general activations. In this work, we overcome such difficulties by providing methods to work with general activations. First, we compile and expand the list of activation functions admitting exact dual activation expressions to compute neural kernels. When the exact computation is unknown, we present methods to effectively approximate them. We propose a fast sketching method that approximates any multi-layered Neural Network Gaussian Process (NNGP) kernel and Neural Tangent Kernel (NTK) matrices for a wide range of activation functions, going beyond the commonly analyzed ReLU activation. This is done by showing how to approximate the neural kernels using the truncated Hermite expansion of any desired activation functions. While most prior works require data points on the unit sphere, our methods do not suffer from such limitations and are applicable to any dataset of points in ℝd. Furthermore, we provide a subspace embedding for NNGP and NTK matrices with near input-sparsity runtime and near-optimal target dimension which applies to any \emph{homogeneous} dual activation functions with rapidly convergent Taylor expansion. Empirically, with respect to exact convolutional NTK (CNTK) computation, our method achieves 106× speedup for approximate CNTK of a 5-layer Myrtle network on CIFAR-10 dataset.
Bio: Insu Han is a postdoctoral research fellow in the Department of Electrical Engineering at Yale University, hosted by Prof. Amin Karbasi. He obtained his Ph.D. degree at Korea Advanced Institute of Science and Technology (KAIST) in 2021 under the supervision of Prof. Jinwoo Shin. He was a recipient of Microsoft Research Asia Fellowship in 2019. His research interests focus on approximate algorithm design and analysis for large-scale machine learning problems and their applications.
