no code implementations • 29 Sep 2024 • Kevin Li, Fulu Li
In this paper, we present a novel framework for the analysis of Riemann Hypothesis [27], which is composed of three key components: a) probabilistic modeling with cross entropy optimization and reasoning; b) the application of the law of large numbers; c) the application of mathematical inductions.
no code implementations • 20 Sep 2024 • Kevin Li, Fulu Li
We use images of cars of a wide range of varieties to compose an image of an animal such as a bird or a lion for the theme of environmental protection to maximize the information about cars in a single composed image and to raise the awareness about environmental challenges.
no code implementations • 14 Sep 2024 • Kevin Li, Fulu Li
The presented algorithm of cross-entropy optimization for hyperparameter optimization of a learning algorithm (CEHPO) can be equally applicable to other areas of optimization problems in deep learning.
no code implementations • 22 Apr 2024 • Seongmin Lee, Benjamin Hoover, Hendrik Strobelt, Zijie J. Wang, Shengyun Peng, Austin Wright, Kevin Li, Haekyu Park, Haoyang Yang, Polo Chau
Diffusion-based generative models' impressive ability to create convincing images has garnered global attention.
no code implementations • 17 Nov 2023 • Kevin Li, Danko Nikolić, Vjekoslav Nikolić, Davor Andrić, Lauren M. Sanders, Sylvain V. Costes
In this work we look at how transfer learning can be improved to learn from small RNA-seq sample sizes without significant human interference.
no code implementations • 23 Oct 2023 • Kevin Li, Max Balakirsky, Simon Mak
Fourier feature approximations have been successfully applied in the literature for scalable Gaussian Process (GP) regression.
no code implementations • 27 Sep 2023 • Kevin Li, Riya Desai, Ryan T. Scott, Joel Ricky Steele, Meera Machado, Samuel Demharter, Adrienne Hoarfrost, Jessica L. Braun, Val A. Fajardo, Lauren M. Sanders, Sylvain V. Costes
The adverse effects of microgravity exposure on mammalian physiology during spaceflight necessitate a deep understanding of the underlying mechanisms to develop effective countermeasures.
1 code implementation • 30 Aug 2023 • Shengyun Peng, Weilin Xu, Cory Cornelius, Matthew Hull, Kevin Li, Rahul Duggal, Mansi Phute, Jason Martin, Duen Horng Chau
Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs.
no code implementations • 11 Jun 2023 • Kevin Li, Simon Mak, J. -F Paquet, Steffen A. Bass
The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled the Universe shortly after the Big Bang.
1 code implementation • 4 May 2023 • Seongmin Lee, Benjamin Hoover, Hendrik Strobelt, Zijie J. Wang, Shengyun Peng, Austin Wright, Kevin Li, Haekyu Park, Haoyang Yang, Duen Horng Chau
Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex structure with explanations of the underlying operations.
1 code implementation • 8 Jan 2023 • Shengyun Peng, Weilin Xu, Cory Cornelius, Kevin Li, Rahul Duggal, Duen Horng Chau, Jason Martin
Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs).
no code implementations • 21 Dec 2022 • Jeffery Cheng, Kevin Li, Justin Lin, Pedro Pachuca
Reinforcement Learning is a powerful tool to model decision-making processes.
1 code implementation • 22 Oct 2022 • Zhiyan Zhou, Kevin Li, Haekyu Park, Megan Dass, Austin Wright, Nilaksh Das, Duen Horng Chau
We present our ongoing work NeuroMapper, an in-browser visualization tool that helps machine learning (ML) developers interpret the evolution of a model during training, providing a new way to monitor the training process and visually discover reasons for suboptimal training.
no code implementations • 30 Sep 2022 • Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Tim Genewein, Elliot Catt, Kevin Li, Anian Ruoss, Chris Cundy, Joel Veness, Jane Wang, Marcus Hutter, Christopher Summerfield, Shane Legg, Pedro Ortega
This is in contrast to risk-sensitive agents, which additionally exploit the higher-order moments of the return, and ambiguity-sensitive agents, which act differently when recognizing situations in which they lack knowledge.
no code implementations • 30 Mar 2022 • Haekyu Park, Seongmin Lee, Benjamin Hoover, Austin P. Wright, Omar Shaikh, Rahul Duggal, Nilaksh Das, Kevin Li, Judy Hoffman, Duen Horng Chau
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training.
no code implementations • 15 Jul 2021 • Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine
In this work, we show that an uncertainty aware classifier can solve challenging reinforcement learning problems by both encouraging exploration and provided directed guidance towards positive outcomes.
no code implementations • 1 Jan 2021 • Kevin Li, Abhishek Gupta, Vitchyr H. Pong, Ashwin Reddy, Aurick Zhou, Justin Yu, Sergey Levine
In this work, we study a more tractable class of reinforcement learning problems defined by data that provides examples of successful outcome states.
1 code implementation • 21 Dec 2020 • Isaac Godfried, Kriti Mahajan, Maggie Wang, Kevin Li, Pranjalya Tiwari
We introduce a novel hourly river flow and precipitation dataset and a second subset of flash flood events with damage estimates and injury counts.
no code implementations • 7 Dec 2020 • Kevin Li
Regression trees and random forests are popular and effective non-parametric estimators in practical applications.