Search Results for author: Kevin Li

Found 19 papers, 5 papers with code

Analysis on Riemann Hypothesis with Cross Entropy Optimization and Reasoning

no code implementations29 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.

Reinforcement Learning (RL)

TalkMosaic: Interactive PhotoMosaic with Multi-modal LLM Q&A Interactions

no code implementations20 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.

Quantization

Cross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks

no code implementations14 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.

Hyperparameter Optimization Stochastic Optimization

Interactive Visual Learning for Stable Diffusion

no code implementations22 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.

Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression

no code implementations23 Oct 2023 Kevin Li, Max Balakirsky, Simon Mak

Fourier feature approximations have been successfully applied in the literature for scalable Gaussian Process (GP) regression.

regression

Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice

no code implementations27 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.

Explainable Models Symbolic Regression

Robust Principles: Architectural Design Principles for Adversarially Robust CNNs

1 code implementation30 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.

Adversarial Robustness

Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma

no code implementations11 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.

Variational Inference

Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion

1 code implementation4 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.

Image Generation

RobArch: Designing Robust Architectures against Adversarial Attacks

1 code implementation8 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).

NeuroMapper: In-browser Visualizer for Neural Network Training

1 code implementation22 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.

Dimensionality Reduction

Beyond Bayes-optimality: meta-learning what you know you don't know

no code implementations30 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.

Decision Making Meta-Learning

Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries

no code implementations30 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.

Decision Making

MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning

no code implementations15 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.

Meta-Learning reinforcement-learning +2

Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples

no code implementations1 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.

reinforcement-learning Reinforcement Learning +1

FlowDB a large scale precipitation, river, and flash flood dataset

1 code implementation21 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.

Multivariate Time Series Forecasting

Asymptotic Normality for Multivariate Random Forest Estimators

no code implementations7 Dec 2020 Kevin Li

Regression trees and random forests are popular and effective non-parametric estimators in practical applications.

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