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Towards Neural Variational Monte Carlo That Scales Linearly with System Size
Or Sharir, Garnet Kin-Lic Chan, Anima Anandkumar
Quantum many-body problems are some of the most challenging problems in science and are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductor…
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Shengchao Liu, Weili Nie, Chengpeng Wang +6
There is increasing adoption of artificial intelligence in drug discovery. However, existing studies use machine learning to mainly utilize the chemical structures of molecules but…
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression
Jiaqi Gu, Ben Keller, Jean Kossaifi +3
Transformers have attained superior performance in natural language processing and computer vision. Their self-attention and feedforward layers are overparameterized, limiting infe…
Machine Learning Accelerated PDE Backstepping Observers
Yuanyuan Shi, Zongyi Li, Huan Yu +3
State estimation is important for a variety of tasks, from forecasting to substituting for unmeasured states in feedback controllers. Performing real-time state estimation for PDEs…
FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
Adarsh Ganeshram, Haydn Maust, Valentin Duruisseaux +6
The physics-informed neural operator (PINO) is a machine learning paradigm that has demonstrated promising results for learning solutions to partial differential equations (PDEs).…
Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs
Robert Joseph George, Jiawei Zhao, Jean Kossaifi +2
Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows. At the core of FNO is a spectral lay…
Fast Sampling of Diffusion Models via Operator Learning
Hongkai Zheng, Weili Nie, Arash Vahdat +2
Diffusion models have found widespread adoption in various areas. However, their sampling process is slow because it requires hundreds to thousands of network evaluations to emulat…
DensePure: Understanding Diffusion Models towards Adversarial Robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin +6
Diffusion models have been recently employed to improve certified robustness through the process of denoising. However, the theoretical understanding of why diffusion models are ab…
An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design
Mingjie Liu, Haoyu Yang, Zongyi Li +7
Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. It requires rigorous simulations of optical and chemical models that are co…
Real-time high-resolution CO geological storage prediction using nested Fourier neural operators
Gege Wen, Zongyi Li, Qirui Long +3
Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoi…
Context Generation Improves Open Domain Question Answering
Dan Su, Mostofa Patwary, Shrimai Prabhumoye +6
Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either direct…
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction
Yulong Cao, Chaowei Xiao, Anima Anandkumar +2
Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe driving behaviors. While many prior works aim to achieve higher prediction accuracy, few s…
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control
Jie Feng, Yuanyuan Shi, Guannan Qu +3
Deep reinforcement learning has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power sys…
Retrieval-based Controllable Molecule Generation
Zichao Wang, Weili Nie, Zhuoran Qiao +3
Generating new molecules with specified chemical and biological properties via generative models has emerged as a promising direction for drug discovery. However, existing methods…
Robust Trajectory Prediction against Adversarial Attacks
Yulong Cao, Danfei Xu, Xinshuo Weng +4
Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving (AD) systems. However, these methods are vulnerable to adversarial attacks,…
Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training
Haoyu Yang, Zongyi Li, Kumara Sastry +5
Machine learning techniques have been extensively studied for mask optimization problems, aiming at better mask printability, shorter turnaround time, better mask manufacturability…
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
Zongyi Li, Daniel Zhengyu Huang, Burigede Liu +1
Deep learning surrogate models have shown promise in solving partial differential equations (PDEs). Among them, the Fourier neural operator (FNO) achieves good accuracy, and is sig…
Langevin Monte Carlo for Contextual Bandits
Pan Xu, Hongkai Zheng, Eric Mazumdar +2
We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i.e., a Gaussian distrib…
Thompson Sampling Achieves Regret in Linear Quadratic Control
Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli +2
Thompson Sampling (TS) is an efficient method for decision-making under uncertainty, where an action is sampled from a carefully prescribed distribution which is updated based on t…
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits
Tianyuan Jin, Pan Xu, Xiaokui Xiao +1
We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers ma…
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems
Sahin Lale, Yuanyuan Shi, Guannan Qu +3
Learning a dynamical system requires stabilizing the unknown dynamics to avoid state blow-ups. However, current reinforcement learning (RL) methods lack stabilization guarantees, w…
Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints
Taylor L. Patti, Jean Kossaifi, Anima Anandkumar +1
Semidefinite programs are optimization methods with a wide array of applications, such as approximating difficult combinatorial problems. One such semidefinite program is the Goema…
Diffusion Models for Adversarial Purification
Weili Nie, Brandon Guo, Yujia Huang +3
Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. These methods do not make assumptions on the form of a…
Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
Michael O'Connell, Guanya Shi, Xichen Shi +4
Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the rel…