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09/15/2023
Replacing softmax with ReLU in Vision Transformers
Previous research observed accuracy degradation when replacing the atten...
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08/29/2023
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models
The time-series anomaly detection is one of the most fundamental tasks f...
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06/27/2023
Precursor-of-Anomaly Detection for Irregular Time Series
Anomaly detection is an important field that aims to identify unexpected...
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03/09/2023
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples
Neural kernels have drastically increased performance on diverse and non...
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11/22/2022
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Forecasting future outcomes from recent time series data is not easy, es...
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09/09/2022
Fast Neural Kernel Embeddings for General Activations
Infinite width limit has shed light on generalization and optimization a...
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04/19/2022
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
The problem of processing very long time-series data (e.g., a length of ...
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04/19/2022
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
Deep learning inspired by differential equations is a recent research tr...
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02/08/2022
Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesis
Tabular data synthesis has received wide attention in the literature. Th...
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07/27/2021
Dataset Distillation with Infinitely Wide Convolutional Networks
The effectiveness of machine learning algorithms arises from being able ...
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05/31/2021
OCT-GAN: Neural ODE-based Conditional Tabular GANs
Synthesizing tabular data is attracting much attention these days for va...
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02/12/2021
Explaining Neural Scaling Laws
The test loss of well-trained neural networks often follows precise powe...
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11/11/2020
Towards NNGP-guided Neural Architecture Search
The predictions of wide Bayesian neural networks are described by a Gaus...
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10/30/2020
Dataset Meta-Learning from Kernel Ridge-Regression
One of the most fundamental aspects of any machine learning algorithm is...
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10/14/2020
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit
Modern deep learning models have achieved great success in predictive ac...
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07/31/2020
Finite Versus Infinite Neural Networks: an Empirical Study
We perform a careful, thorough, and large scale empirical study of the c...
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07/15/2020
Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble
In general, sufficient data is essential for the better performance and ...
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05/05/2020
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
This paper reviews the NTIRE 2020 challenge on video quality mapping (VQ...
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01/21/2020
On the infinite width limit of neural networks with a standard parameterization
There are currently two parameterizations used to derive fixed kernels c...
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12/05/2019
Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a library designed to enable research into infinite-w...
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10/11/2019
On Empirical Comparisons of Optimizers for Deep Learning
Selecting an optimizer is a central step in the contemporary deep learni...
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03/11/2019
Physics Enhanced Artificial Intelligence
We propose that intelligently combining models from the domains of Artif...
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02/18/2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
A longstanding goal in deep learning research has been to precisely char...
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11/08/2018
Measuring the Effects of Data Parallelism on Neural Network Training
Recent hardware developments have made unprecedented amounts of data par...
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10/11/2018
Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes
There is a previously identified equivalence between wide fully connecte...
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11/01/2017