71 citations · 107 across the 8 of their papers we have counts for
10 papers · 1 filter
A Deep State Space Model for Rainfall-Runoff Simulations
Yihan Wang, Lujun Zhang, Annan Yu +2
The classical way of studying the rainfall-runoff processes in the water cycle relies on conceptual or physically-based hydrologic models. Deep learning (DL) has recently emerged a…
Tuning Frequency Bias of State Space Models
Annan Yu, Dongwei Lyu, Soon Hoe Lim +2
State space models (SSMs) leverage linear, time-invariant (LTI) systems to effectively learn sequences with long-range dependencies. By analyzing the transfer functions of LTI syst…
NoisyMix: Boosting Model Robustness to Common Corruptions
N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu +3
For many real-world applications, obtaining stable and robust statistical performance is more important than simply achieving state-of-the-art predictive test accuracy, and thus ro…
Cluster-and-Conquer: A Framework For Time-Series Forecasting
Reese Pathak, Rajat Sen, Nikhil Rao +3
We propose a three-stage framework for forecasting high-dimensional time-series data. Our method first estimates parameters for each univariate time series. Next, we use these para…
Stateful ODE-Nets using Basis Function Expansions
Alejandro Queiruga, N. Benjamin Erichson, Liam Hodgkinson +1
The recently-introduced class of ordinary differential equation networks (ODE-Nets) establishes a fruitful connection between deep learning and dynamical systems. In this work, we…
A Differential Geometry Perspective on Orthogonal Recurrent Models
Omri Azencot, N. Benjamin Erichson, Mirela Ben-Chen +1
Recently, orthogonal recurrent neural networks (RNNs) have emerged as state-of-the-art models for learning long-term dependencies. This class of models mitigates the exploding and…