no code implementations • 18 Mar 2024 • Tian-Yi Zhou, Namjoon Suh, Guang Cheng, Xiaoming Huo
Motivated by the abundance of functional data such as time series and images, there has been a growing interest in integrating such data into neural networks and learning maps from function spaces to R (i. e., functionals).
no code implementations • 1 Feb 2024 • Yue Xing, Xiaofeng Lin, Namjoon Suh, Qifan Song, Guang Cheng
In practice, it is observed that transformer-based models can learn concepts in context in the inference stage.
no code implementations • 14 Jan 2024 • Namjoon Suh, Guang Cheng
In this article, we review the literature on statistical theories of neural networks from three perspectives.
1 code implementation • 24 Oct 2023 • Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh, Merhdad Honarkhah, Guang Cheng
Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesis.
no code implementations • 26 Sep 2023 • Hyunouk Ko, Namjoon Suh, Xiaoming Huo
The recent success of neural networks in pattern recognition and classification problems suggests that neural networks possess qualities distinct from other more classical classifiers such as SVMs or boosting classifiers.
no code implementations • ICLR 2022 • Namjoon Suh, Hyunouk Ko, Xiaoming Huo
We study the generalization properties of the overparameterized deep neural network (DNN) with Rectified Linear Unit (ReLU) activations.
no code implementations • 12 Mar 2021 • Yuchen He, Namjoon Suh, Xiaoming Huo, Sungha Kang, Yajun Mei
We provide a set of sufficient conditions which guarantee that, from a single trajectory data denoised by a Local-Polynomial filter, the support of $\mathbf{c}(\lambda)$ asymptotically converges to the true signed-support associated with the underlying PDE for sufficiently many data and a certain range of $\lambda$.
no code implementations • 2 Dec 2019 • Namjoon Suh, Xiaoming Huo, Eric Heim, Lee Seversky
We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network.
no code implementations • 20 Nov 2017 • Namjoon Suh
In following section, we expand this idea on estimating parameters in Gaussian Hidden Markov Spatial-Temporal Random Field, and display results on two performed experiments.