Search Results for author: Lai Wei

Found 22 papers, 4 papers with code

Probabilistic Generative Transformer Language models for Generative Design of Molecules

no code implementations20 Sep 2022 Lai Wei, Nihang Fu, Yuqi Song, Qian Wang, Jianjun Hu

Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction.

Language Modelling Representation Learning

Learning idempotent representation for subspace clustering

1 code implementation29 Jul 2022 Lai Wei, Shiteng Liu, Rigui Zhou, Changming Zhu

The critical point for the successes of spectral-type subspace clustering algorithms is to seek reconstruction coefficient matrices which can faithfully reveal the subspace structures of data sets.

Materials Transformers Language Models for Generative Materials Design: a benchmark study

no code implementations27 Jun 2022 Nihang Fu, Lai Wei, Yuqi Song, Qinyang Li, Rui Xin, Sadman Sadeed Omee, Rongzhi Dong, Edirisuriya M. Dilanga Siriwardane, Jianjun Hu

We also find that the properties of the generated samples can be tailored by training the models with selected training sets such as high-bandgap materials.

A Contraction-constrained Model Predictive Control for Multi-timescale Nonlinear Processes

no code implementations9 May 2022 Ryan Mccloy, Lai Wei, Jie Bao

Many chemical processes exhibit diverse timescale dynamics with a strong coupling between timescale sensitive variables.

A Contraction-constrained Model Predictive Control for Nonlinear Processes using Disturbance Forecasts

no code implementations9 May 2022 Ryan Mccloy, Lai Wei, Jie Bao

Model predictive control (MPC) has become the most widely used advanced control method in process industry.

Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials

no code implementations25 Apr 2022 Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Edirisuriya M. D. Siriwardane, Fanglin Chen, Jianjun Hu

Here we propose BLMM Crystal Transformer, a neural network based probabilistic generative model for generative and tinkering design of inorganic materials.

Language Modelling Self-Learning +1

Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks

no code implementations30 Jan 2022 Lai Wei, Ryan Mccloy, Jie Bao

This neural network is then embedded in an adaptive contraction-based control law which is updated by parameter estimates online.

Contraction Analysis and Control Synthesis for Discrete-time Nonlinear Processes

no code implementations9 Dec 2021 Lai Wei, Ryan Mccloy, Jie Bao

Shifting away from the traditional mass production approach, the process industry is moving towards more agile, cost-effective and dynamic process operation (next-generation smart plants).

Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks

no code implementations10 Nov 2021 Nghia Nguyen, Steph-Yves Louis, Lai Wei, Kamal Choudhary, Ming Hu, Jianjun Hu

Our work demonstrates the capability of deep graph neural networks to learn to predict phonon spectrum properties of crystal structures in addition to phonon density of states (DOS) and electronic DOS in which the output dimension is constant.

Materials Screening

A Two-stage Pricing Strategy Considering Learning Effects and Word-of-Mouth

no code implementations22 Oct 2021 YanRong Li, Lai Wei, Wei Jiang

This paper proposes a two-stage pricing strategy for nondurable (such as typical electronics) products, where retail price is cut down at certain time points of the product lifecycle.

Scalable deeper graph neural networks for high-performance materials property prediction

1 code implementation25 Sep 2021 Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu

Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science.

Band Gap Graph Attention +1

Online Estimation and Coverage Control with Heterogeneous Sensing Information

1 code implementation28 Jun 2021 Andrew McDonald, Lai Wei, Vaibhav Srivastava

In this paper, we address the problem of multi-robot online estimation and coverage control by combining low- and high-fidelity data to learn and cover a sensory function of interest.

Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks

no code implementations12 May 2021 Lai Wei, Ryan Mccloy, Jie Bao

In this paper, a contraction theory-based control approach using neural networks is developed for nonlinear chemical processes to achieve time-varying reference tracking.

online learning

Control Contraction Metric Synthesis for Discrete-time Nonlinear Systems

no code implementations21 Apr 2021 Lai Wei, Ryan Mccloy, Jie Bao

Flexible manufacturing has been the trend in the area of the modern chemical process nowadays.

Nonstationary Stochastic Multiarmed Bandits: UCB Policies and Minimax Regret

no code implementations22 Jan 2021 Lai Wei, Vaibhav Srivastava

We study the nonstationary stochastic Multi-Armed Bandit (MAB) problem in which the distribution of rewards associated with each arm are assumed to be time-varying and the total variation in the expected rewards is subject to a variation budget.

Multi-Robot Gaussian Process Estimation and Coverage: A Deterministic Sequencing Algorithm and Regret Analysis

no code implementations12 Jan 2021 Lai Wei, Andrew McDonald, Vaibhav Srivastava

Modeling the sensory field as a realization of a Gaussian Process and using Bayesian techniques, we devise a policy which aims to balance the tradeoff between learning the sensory function and covering the environment.

Minimax Policy for Heavy-tailed Bandits

no code implementations20 Jul 2020 Lai Wei, Vaibhav Srivastava

We study the stochastic Multi-Armed Bandit (MAB) problem under worst-case regret and heavy-tailed reward distribution.

Multi-Armed Bandits

When deep learning meets causal inference: a computational framework for drug repurposing from real-world data

1 code implementation16 Jul 2020 Ruoqi Liu, Lai Wei, Ping Zhang

Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside.

Causal Inference

Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes

no code implementations18 May 2020 Lai Wei, Xiaobo Tan, Vaibhav Srivastava

Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection.

Gaussian Processes

On Distributed Multi-player Multiarmed Bandit Problems in Abruptly Changing Environment

no code implementations12 Dec 2018 Lai Wei, Vaibhav Srivastava

We study the multi-player stochastic multiarmed bandit (MAB) problem in an abruptly changing environment.

On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems

no code implementations23 Feb 2018 Lai Wei, Vaibhav Srivastava

We study the non-stationary stochastic multiarmed bandit (MAB) problem and propose two generic algorithms, namely, the limited memory deterministic sequencing of exploration and exploitation (LM-DSEE) and the Sliding-Window Upper Confidence Bound# (SW-UCB#).

House Price Prediction Using LSTM

no code implementations25 Sep 2017 Xiaochen Chen, Lai Wei, Jiaxin Xu

In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen.

Time Series

Cannot find the paper you are looking for? You can Submit a new open access paper.