Learning to Dissipate Energy in Oscillatory State-Space Models
Fuente:
arXiv
Saved in:
| Main Authors: | Boyer, Jared, Rusch, T. Konstantin, Rus, Daniela |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Oscillatory State-Space Models
by: Rusch, T. Konstantin, et al.
Published: (2024)
by: Rusch, T. Konstantin, et al.
Published: (2024)
The Curious Case of In-Training Compression of State Space Models
by: Chahine, Makram, et al.
Published: (2025)
by: Chahine, Makram, et al.
Published: (2025)
Quantifying Memory Use in Reinforcement Learning with Temporal Range
by: Lafuente-Mercado, Rodney, et al.
Published: (2025)
by: Lafuente-Mercado, Rodney, et al.
Published: (2025)
Low Stein Discrepancy via Message-Passing Monte Carlo
by: Kirk, Nathan, et al.
Published: (2025)
by: Kirk, Nathan, et al.
Published: (2025)
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
by: Rusch, T. Konstantin, et al.
Published: (2024)
by: Rusch, T. Konstantin, et al.
Published: (2024)
The Key to State Reduction in Linear Attention: A Rank-based Perspective
by: Nazari, Philipp, et al.
Published: (2026)
by: Nazari, Philipp, et al.
Published: (2026)
Neural Low-Discrepancy Sequences
by: Van Huffel, Michael Etienne, et al.
Published: (2025)
by: Van Huffel, Michael Etienne, et al.
Published: (2025)
Low-Pass Flow Matching
by: Ruscio, Francesco M., et al.
Published: (2026)
by: Ruscio, Francesco M., et al.
Published: (2026)
Parallelization of Non-linear State-Space Models: Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling
by: Farsang, Mónika, et al.
Published: (2025)
by: Farsang, Mónika, et al.
Published: (2025)
Relaxed Equivariance via Multitask Learning
by: Elhag, Ahmed A., et al.
Published: (2024)
by: Elhag, Ahmed A., et al.
Published: (2024)
Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences
by: Tumma, Neehal, et al.
Published: (2026)
by: Tumma, Neehal, et al.
Published: (2026)
PoSafeNet: Safe Learning with Poset-Structured Neural Nets
by: Wong, Kiwan, et al.
Published: (2026)
by: Wong, Kiwan, et al.
Published: (2026)
State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
by: Lu, Sen, et al.
Published: (2025)
by: Lu, Sen, et al.
Published: (2025)
HiPPO-Prophecy: State-Space Models can Provably Learn Dynamical Systems in Context
by: Joseph, Federico Arangath, et al.
Published: (2024)
by: Joseph, Federico Arangath, et al.
Published: (2024)
The Impact of Model Zoo Size and Composition on Weight Space Learning
by: Falk, Damian, et al.
Published: (2025)
by: Falk, Damian, et al.
Published: (2025)
ABNet: Attention BarrierNet for Safe and Scalable Robot Learning
by: Xiao, Wei, et al.
Published: (2024)
by: Xiao, Wei, et al.
Published: (2024)
Hebbian-Oscillatory Co-Learning
by: Hays, Hasi
Published: (2026)
by: Hays, Hasi
Published: (2026)
Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions
by: Xiao, Wei, et al.
Published: (2023)
by: Xiao, Wei, et al.
Published: (2023)
Towards Scalable and Versatile Weight Space Learning
by: Schürholt, Konstantin, et al.
Published: (2024)
by: Schürholt, Konstantin, et al.
Published: (2024)
Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs
by: Eltaieb, Rania A., et al.
Published: (2026)
by: Eltaieb, Rania A., et al.
Published: (2026)
Model Merging by Output-Space Projection
by: Evans, Bethan, et al.
Published: (2026)
by: Evans, Bethan, et al.
Published: (2026)
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
by: Tanaka, Yusuke, et al.
Published: (2024)
by: Tanaka, Yusuke, et al.
Published: (2024)
Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification
by: Farsang, Mónika, et al.
Published: (2026)
by: Farsang, Mónika, et al.
Published: (2026)
Exemplar-Free Continual Learning for State Space Models
by: Lee, Isaac Ning, et al.
Published: (2025)
by: Lee, Isaac Ning, et al.
Published: (2025)
Latent Matters: Learning Deep State-Space Models
by: Klushyn, Alexej, et al.
Published: (2026)
by: Klushyn, Alexej, et al.
Published: (2026)
Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo
by: Chahine, Makram, et al.
Published: (2024)
by: Chahine, Makram, et al.
Published: (2024)
Interpretable Imitation Learning via Generative Adversarial STL Inference and Control
by: Liu, Wenliang, et al.
Published: (2024)
by: Liu, Wenliang, et al.
Published: (2024)
AIRE-Prune: Asymptotic Impulse-Response Energy for State Pruning in State Space Models
by: Padhy, Apurba Prasad, et al.
Published: (2026)
by: Padhy, Apurba Prasad, et al.
Published: (2026)
Stuart-Landau Oscillatory Graph Neural Network
by: Zhang, Kaicheng, et al.
Published: (2025)
by: Zhang, Kaicheng, et al.
Published: (2025)
Learning Local Causal World Models with State Space Models and Attention
by: Petri, Francesco, et al.
Published: (2025)
by: Petri, Francesco, et al.
Published: (2025)
Artificial Kuramoto Oscillatory Neurons
by: Miyato, Takeru, et al.
Published: (2024)
by: Miyato, Takeru, et al.
Published: (2024)
Graph Mamba: Towards Learning on Graphs with State Space Models
by: Behrouz, Ali, et al.
Published: (2024)
by: Behrouz, Ali, et al.
Published: (2024)
How does over-squashing affect the power of GNNs?
by: Di Giovanni, Francesco, et al.
Published: (2023)
by: Di Giovanni, Francesco, et al.
Published: (2023)
BioOSS: A Bio-Inspired Oscillatory State System with Spatio-Temporal Dynamics
by: Yuan, Zhongju, et al.
Published: (2025)
by: Yuan, Zhongju, et al.
Published: (2025)
Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?
by: Farsang, Mónika, et al.
Published: (2023)
by: Farsang, Mónika, et al.
Published: (2023)
Spectral State Space Models
by: Agarwal, Naman, et al.
Published: (2023)
by: Agarwal, Naman, et al.
Published: (2023)
Regularization-Based Efficient Continual Learning in Deep State-Space Models
by: Zhang, Yuanhang, et al.
Published: (2024)
by: Zhang, Yuanhang, et al.
Published: (2024)
Hyper-Representations: Learning from Populations of Neural Networks
by: Schürholt, Konstantin
Published: (2024)
by: Schürholt, Konstantin
Published: (2024)
Active Human Feedback Collection via Neural Contextual Dueling Bandits
by: Verma, Arun, et al.
Published: (2025)
by: Verma, Arun, et al.
Published: (2025)
What Can We Learn from State Space Models for Machine Learning on Graphs?
by: Huang, Yinan, et al.
Published: (2024)
by: Huang, Yinan, et al.
Published: (2024)
Similar Items
-
Oscillatory State-Space Models
by: Rusch, T. Konstantin, et al.
Published: (2024) -
The Curious Case of In-Training Compression of State Space Models
by: Chahine, Makram, et al.
Published: (2025) -
Quantifying Memory Use in Reinforcement Learning with Temporal Range
by: Lafuente-Mercado, Rodney, et al.
Published: (2025) -
Low Stein Discrepancy via Message-Passing Monte Carlo
by: Kirk, Nathan, et al.
Published: (2025) -
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
by: Rusch, T. Konstantin, et al.
Published: (2024)