Search Results for author:
Found 31 papers, 11 papers with code
AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials.
Multi-view Subspace Clustering via An Adaptive Consensus Graph Filter
Therefore, in the proposed method, the consensus reconstruction coefficient matrix, the consensus graph filter, and the reconstruction coefficient matrices from different views are interdependent.
Large Language Model Evaluation via Matrix Entropy
Large language models (LLMs) have revolutionized the field of natural language processing, extending their strong capabilities into multi-modal domains.
Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortex
While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains underexplored.
Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm
While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (MOGA) with a neural network inter-atomic potential (IAP) model to find energetically optimal crystal structures given chemical compositions.
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
To achieve this, we first propose several metrics to access the quality of multimodal instruction data.
Approximate Causal Effect Identification under Weak Confounding
We show that our bounds are consistent in the sense that as the entropy of unobserved confounders goes to zero, the gap between the upper and lower bound vanishes.
Adaptive Graph Convolutional Subspace Clustering
Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications.
SCoDA: Domain Adaptive Shape Completion for Real Scans
Thus, we propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data.
Probabilistic Generative Transformer Language models for Generative Design of Molecules
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.
Learning idempotent representation for subspace clustering
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
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 Nonlinear Processes using Disturbance Forecasts
Model predictive control (MPC) has become the most widely used advanced control method in process industry.
A Contraction-constrained Model Predictive Control for Multi-timescale Nonlinear Processes
Many chemical processes exhibit diverse timescale dynamics with a strong coupling between timescale sensitive variables.
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials
Here we propose BLMM Crystal Transformer, a neural network based probabilistic generative model for generative and tinkering design of inorganic materials.
Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks
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
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
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.
A Two-stage Pricing Strategy Considering Learning Effects and Word-of-Mouth
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
Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science.
Online Estimation and Coverage Control with Heterogeneous Sensing Information
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
In this paper, a contraction theory-based control approach using neural networks is developed for nonlinear chemical processes to achieve time-varying reference tracking.
Control Contraction Metric Synthesis for Discrete-time Nonlinear Systems
Flexible manufacturing has been the trend in the area of the modern chemical process nowadays.
Nonstationary Stochastic Multiarmed Bandits: UCB Policies and Minimax Regret
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
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
We study the stochastic Multi-Armed Bandit (MAB) problem under worst-case regret and heavy-tailed reward distribution.
When deep learning meets causal inference: a computational framework for drug repurposing from real-world data
Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside.
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes
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.
On Distributed Multi-player Multiarmed Bandit Problems in Abruptly Changing Environment
We study the multi-player stochastic multiarmed bandit (MAB) problem in an abruptly changing environment.
On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems
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
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.
