13 citations · 28 across the 28 of their papers we have counts for
26 papers · 1 filter
Stochastic Adaptive Fourier Decomposition for Operator Learning
Pengqing Shi, Liming Zhang, Tao Qian +3
Fourier Neural Operators (FNOs) offer an efficient paradigm for solving partial differential equations (PDEs). However, FNOs rely on a fixed Fourier basis and hard frequency trunca…
Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks
Xiaoyu Li, Zhizhou Sha, Jiaojiao Jiang +2
An input may activate few hidden units even when different inputs collectively use an entire network. We study the statistical complexity of this input-dependent sparsity in the on…
Optimistic Rates for Multiclass PAC Learning
Xiaoyu Li, Andi Han, Jiaojiao Jiang +1
Worst-case multiclass bounds do not become smaller when the best classifier is already nearly correct: what is missing is an optimistic rate, a guarantee whose fluctuation scales w…
Flood and Harvest: The Provable Necessity of Trivia for Generating Valuable Mathematics via the Lens of Language Generation in the Limit
Xiaoyu Li, Andi Han, Dai Shi +3
AI systems coupled to proof assistants now generate formal mathematics at scale, and the gap between what a checker can verify and what a mathematician would value has become the b…
SirenFNO: Efficient and Full Frequency Learning of Fourier Neural Operators
Pengqing Shi, Jie Yin, Stephen Tierney +1
Fourier neural operators (FNOs) are effective and efficient surrogates for approximating solutions of PDEs and generalize across discretizations. However, owing to the reliance on…
Learning Manifold and Itô Dynamics with Branched Neural Rough Differential Equations
Luke Thompson, Dai Shi, Lequan Lin +2
Neural rough differential equations (NRDEs) stay accurate under irregular sampling while taking far fewer integration steps than standard neural differential equations, summarising…
Contrastive Identification and Generation in the Limit
Xiaoyu Li, Andi Han, Jiaojiao Jiang +1
In the classical identification in the limit model of Gold [1967], a stream of positive examples is presented round by round, and the learner must eventually recover the target hyp…
Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
Dai Shi, Lequan Lin, Andi Han +4
Stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs) are fundamental for modeling stochastic dynamics across the natural sciences and mode…
ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking
Lequan Lin, Dai Shi, Andi Han +7
Supervised learning relies on high-quality labeled data, but obtaining such data through human annotation is both expensive and time-consuming. Recent work explores using large lan…
ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics
Luke Thompson, Davy Guan, Dai Shi +3
Molecular dynamics (MD) simulations underpin modern computational drug discovery, materials science, and biochemistry. Recent machine learning models provide high-fidelity MD predi…
Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond
Dai Shi, Kuan Yan, Lequan Lin +6
Understanding disease progression at the molecular pathway level usually requires capturing both structural dependencies between pathways and the temporal dynamics of disease evolu…
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
Lequan Lin, Dai Shi, Andi Han +2
Graph Neural Networks (GNNs) are proficient in graph representation learning and achieve promising performance on versatile tasks such as node classification and link prediction. U…
When Graph Neural Networks Meet Dynamic Mode Decomposition
Dai Shi, Lequan Lin, Andi Han +3
Graph Neural Networks (GNNs) have emerged as fundamental tools for a wide range of prediction tasks on graph-structured data. Recent studies have drawn analogies between GNN featur…
A Riemannian Approach to Ground Metric Learning for Optimal Transport
Pratik Jawanpuria, Dai Shi, Bamdev Mishra +1
Optimal transport (OT) theory has attracted much attention in machine learning and signal processing applications. OT defines a notion of distance between probability distributions…
Unleash Graph Neural Networks from Heavy Tuning
Lequan Lin, Dai Shi, Andi Han +2
Graph Neural Networks (GNNs) are deep-learning architectures designed for graph-type data, where understanding relationships among individual observations is crucial. However, achi…
CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model
Zhiqi Shao, Michael G. H. Bell, Ze Wang +3
Accurate, and effective traffic forecasting is vital for smart traffic systems, crucial in urban traffic planning and management. Current Spatio-Temporal Transformer models, despit…
Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks
Dai Shi, Andi Han, Lequan Lin +3
Physics-informed Graph Neural Networks have achieved remarkable performance in learning through graph-structured data by mitigating common GNN challenges such as over-smoothing, ov…
SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting
Lequan Lin, Dai Shi, Andi Han +1
Traffic forecasting, a crucial application of spatio-temporal graph (STG) learning, has traditionally relied on deterministic models for accurate point estimations. Yet, these mode…
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges
Dai Shi, Andi Han, Lequan Lin +2
Graph-based message-passing neural networks (MPNNs) have achieved remarkable success in both node and graph-level learning tasks. However, several identified problems, including ov…
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond
Andi Han, Dai Shi, Lequan Lin +1
Graph neural networks (GNNs) have demonstrated significant promise in modelling relational data and have been widely applied in various fields of interest. The key mechanism behind…
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond
Zhiqi Shao, Dai Shi, Andi Han +3
Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data. Despite their great success, critical computational challe…
Bregman Graph Neural Network
Jiayu Zhai, Lequan Lin, Dai Shi +1
Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption. However, in node cl…
How Curvature Enhance the Adaptation Power of Framelet GCNs
Dai Shi, Yi Guo, Zhiqi Shao +1
Graph neural network (GNN) has been demonstrated powerful in modeling graph-structured data. However, despite many successful cases of applying GNNs to various graph classification…
Frameless Graph Knowledge Distillation
Dai Shi, Zhiqi Shao, Yi Guo +1
Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be acco…