1.1k citations · 7.8k across the 180 of their papers we have counts for
21 papers · 1 filter
Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits
Abigail McClain Gomez, Taylor L. Patti, Anima Anandkumar +1
Distributed quantum computation is often proposed to increase the scalability of quantum hardware, as it reduces cooperative noise and requisite connectivity by sharing quantum inf…
Neural Operators for Accelerating Scientific Simulations and Design
Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li +3
Scientific discovery and engineering design are currently limited by the time and cost of physical experiments, selected mostly through trial-and-error and intuition that require d…
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
Zongyi Li, Nikola Borislavov Kovachki, Chris Choy +8
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the solution operator of large-scale partial differential equations with varying ge…
Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces
Miguel Liu-Schiaffini, Clare E. Singer, Nikola Kovachki +4
Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems. For instance, increased greenhouse gas concentr…
Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs
Or Sharir, Anima Anandkumar
Deep learning often faces the challenge of efficiently processing dynamic inputs, such as sensor data or user inputs. For example, an AI writing assistant is required to update its…
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
Renbo Tu, Colin White, Jean Kossaifi +5
Neural operators, such as Fourier Neural Operators (FNO), form a principled approach for learning solution operators for PDEs and other mappings between function spaces. However, m…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Shengchao Liu, Weitao Du, Yanjing Li +10
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of…
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Boris Bonev, Thorsten Kurth, Christian Hundt +4
Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scie…
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Kaiyu Yang, Aidan M. Swope, Alex Gu +6
Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However, existing methods are difficult to reproduce or build on, du…
InRank: Incremental Low-Rank Learning
Jiawei Zhao, Yifei Zhang, Beidi Chen +2
The theory of greedy low-rank learning (GLRL) aims to explain the impressive generalization capabilities of deep learning. It proves that stochastic gradient-based training implici…
Fast Training of Diffusion Models with Masked Transformers
Hongkai Zheng, Weili Nie, Arash Vahdat +1
We propose an efficient approach to train large diffusion models with masked transformers. While masked transformers have been extensively explored for representation learning, the…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq, Qingfeng Lan, Pan Xu +4
We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling algor…
AI-aided Geometric Design of Anti-infection Catheters
Tingtao Zhou, Xuan Wan, Daniel Zhengyu Huang +6
Bacteria can swim upstream due to hydrodynamic interactions with the fluid flow in a narrow tube, and pose a clinical threat of urinary tract infection to patients implanted with c…
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study
Boxin Wang, Wei Ping, Peng Xu +9
Large decoder-only language models (LMs) can be largely improved in terms of perplexity by retrieval (e.g., RETRO), but its impact on text generation quality and downstream task ac…
Fourier Neural Operator for Plasma Modelling
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi +3
Predicting plasma evolution within a Tokamak is crucial to building a sustainable fusion reactor. Whether in the simulation space or within the experimental domain, the capability…
Score-based Diffusion Models in Function Space
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista +10
Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data with Gaussian white noise and a r…
BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models
Rafal Kocielnik, Shrimai Prabhumoye, Vivian Zhang +3
Pretrained Language Models (PLMs) harbor inherent social biases that can result in harmful real-world implications. Such social biases are measured through the probability values t…
ISB: Image-to-Image Schrödinger Bridge
Guan-Horng Liu, Arash Vahdat, De-An Huang +3
We propose Image-to-Image Schrödinger Bridge (ISB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distribu…
Forecasting subcritical cylinder wakes with Fourier Neural Operators
Peter I Renn, Cong Wang, Sahin Lale +3
We apply Fourier neural operators (FNOs), a state-of-the-art operator learning technique, to forecast the temporal evolution of experimentally measured velocity fields. FNOs are a…