Search Results for author: Minghao Guo

Found 16 papers, 8 papers with code

LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

no code implementations16 May 2024 Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, Zhiqing Sun, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan, Wojciech Matusik

Large Language Models have recently gained significant attention in scientific discovery for their extensive knowledge and advanced reasoning capabilities.

Bilevel Optimization

Representing Molecules as Random Walks Over Interpretable Grammars

no code implementations13 Mar 2024 Michael Sun, Minghao Guo, Weize Yuan, Veronika Thost, Crystal Elaine Owens, Aristotle Franklin Grosz, Sharvaa Selvan, Katelyn Zhou, Hassan Mohiuddin, Benjamin J Pedretti, Zachary P Smith, Jie Chen, Wojciech Matusik

Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate technology.

Property Prediction

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

1 code implementation4 Sep 2023 Minghao Guo, Veronika Thost, Samuel W Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik

Still, these techniques are faced with a common challenge in practice: Labeled data are limited by the cost of manual extraction from literature and laborious experimentation.

Drug Discovery Molecular Property Prediction +1

Adaptive Approximate Implicitization of Planar Parametric Curves via Weak Gradient Constraints

no code implementations23 Feb 2023 Minghao Guo, Yan Gao, Zheng Pan

Converting a parametric curve into the implicit form, which is called implicitization, has always been a popular but challenging problem in geometric modeling and related applications.

A Synergistic Compilation Workflow for Tackling Crosstalk in Quantum Machines

no code implementations12 Jul 2022 Fei Hua, Yuwei Jin, Ang Li, Chenxu Liu, Meng Wang, Yanhao Chen, Chi Zhang, Ari Hayes, Samuel Stein, Minghao Guo, Yipeng Huang, Eddy Z. Zhang

Evaluations through simulation and on real IBM-Q devices show that our framework can significantly reduce the error rate by up to 6$\times$, with only $\sim$60\% circuit depth compared to state-of-the-art gate scheduling approaches.

Scheduling

Data-Efficient Graph Grammar Learning for Molecular Generation

1 code implementation ICLR 2022 Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik

This is a non-trivial task for neural network-based generative models since the relevant chemical knowledge can only be extracted and generalized from the limited training data.

Polygrammar: Grammar for Digital Polymer Representation and Generation

no code implementations5 May 2021 Minghao Guo, Wan Shou, Liane Makatura, Timothy Erps, Michael Foshey, Wojciech Matusik

Here, we present a parametric, context-sensitive grammar designed specifically for the representation and generation of polymers.

valid

Towards Evaluating and Training Verifiably Robust Neural Networks

1 code implementation CVPR 2021 Zhaoyang Lyu, Minghao Guo, Tong Wu, Guodong Xu, Kehuan Zhang, Dahua Lin

Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks.

Texture Memory-Augmented Deep Patch-Based Image Inpainting

1 code implementation28 Sep 2020 Rui Xu, Minghao Guo, Jiaqi Wang, Xiaoxiao Li, Bolei Zhou, Chen Change Loy

By bringing together the best of both paradigms, we propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions.

Image Inpainting Retrieval +1

AM-LFS: AutoML for Loss Function Search

1 code implementation ICCV 2019 Chuming Li, Yuan Xin, Chen Lin, Minghao Guo, Wei Wu, Wanli Ouyang, Junjie Yan

The key contribution of this work is the design of search space which can guarantee the generalization and transferability on different vision tasks by including a bunch of existing prevailing loss functions in a unified formulation.

AutoML

IRLAS: Inverse Reinforcement Learning for Architecture Search

1 code implementation CVPR 2019 Minghao Guo, Zhao Zhong, Wei Wu, Dahua Lin, Junjie Yan

Motivated by the fact that human-designed networks are elegant in topology with a fast inference speed, we propose a mirror stimuli function inspired by biological cognition theory to extract the abstract topological knowledge of an expert human-design network (ResNeXt).

Neural Architecture Search reinforcement-learning +1

Dual-Agent Deep Reinforcement Learning for Deformable Face Tracking

no code implementations ECCV 2018 Minghao Guo, Jiwen Lu, Jie zhou

In this paper, we propose a dual-agent deep reinforcement learning (DADRL) method for deformable face tracking, which generates bounding boxes and detects facial landmarks interactively from face videos.

Facial Landmark Detection reinforcement-learning +1

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