Low-Rank Filtering and Smoothing for Sequential Deep Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Sliwa, Joanna, Schneider, Frank, Bosch, Nathanael, Kristiadi, Agustinus, Hennig, Philipp |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mitigating Forgetting in Low Rank Adaptation
by: Sliwa, Joanna, et al.
Published: (2025)
by: Sliwa, Joanna, et al.
Published: (2025)
Introduction to the Analysis of Probabilistic Decision-Making Algorithms
by: Kristiadi, Agustinus
Published: (2025)
by: Kristiadi, Agustinus
Published: (2025)
On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective
by: Wenger, Jonathan, et al.
Published: (2023)
by: Wenger, Jonathan, et al.
Published: (2023)
Uncertainty-Guided Likelihood Tree Search
by: Grosse, Julia, et al.
Published: (2024)
by: Grosse, Julia, et al.
Published: (2024)
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
by: Schmidt, Jonathan, et al.
Published: (2023)
by: Schmidt, Jonathan, et al.
Published: (2023)
Limits of PRM-Guided Tree Search for Mathematical Reasoning with LLMs
by: Cinquin, Tristan, et al.
Published: (2025)
by: Cinquin, Tristan, et al.
Published: (2025)
Sketching Low-Rank Plus Diagonal Matrices
by: Fernandez, Andres, et al.
Published: (2025)
by: Fernandez, Andres, et al.
Published: (2025)
FlashMD: long-stride, universal prediction of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)
by: Bigi, Filippo, et al.
Published: (2025)
Computation-Aware Kalman Filtering and Smoothing
by: Pförtner, Marvin, et al.
Published: (2024)
by: Pförtner, Marvin, et al.
Published: (2024)
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks
by: Rashid, Ahmad, et al.
Published: (2023)
by: Rashid, Ahmad, et al.
Published: (2023)
A Critical Look At Tokenwise Reward-Guided Text Generation
by: Rashid, Ahmad, et al.
Published: (2024)
by: Rashid, Ahmad, et al.
Published: (2024)
Position: Curvature Matrices Should Be Democratized via Linear Operators
by: Dangel, Felix, et al.
Published: (2025)
by: Dangel, Felix, et al.
Published: (2025)
Parallel-in-Time Probabilistic Numerical ODE Solvers
by: Bosch, Nathanael, et al.
Published: (2023)
by: Bosch, Nathanael, et al.
Published: (2023)
Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers
by: Yao, Dingling, et al.
Published: (2025)
by: Yao, Dingling, et al.
Published: (2025)
Towards Cost-Effective Reward Guided Text Generation
by: Rashid, Ahmad, et al.
Published: (2025)
by: Rashid, Ahmad, et al.
Published: (2025)
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
by: Beck, Jonas, et al.
Published: (2024)
by: Beck, Jonas, et al.
Published: (2024)
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
by: Kristiadi, Agustinus, et al.
Published: (2024)
by: Kristiadi, Agustinus, et al.
Published: (2024)
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?
by: Kristiadi, Agustinus, et al.
Published: (2024)
by: Kristiadi, Agustinus, et al.
Published: (2024)
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC
by: Lin, Wu, et al.
Published: (2023)
by: Lin, Wu, et al.
Published: (2023)
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty
by: Tatzel, Lukas, et al.
Published: (2023)
by: Tatzel, Lukas, et al.
Published: (2023)
Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods
by: Fernandez, Andres, et al.
Published: (2025)
by: Fernandez, Andres, et al.
Published: (2025)
Debiasing Mini-Batch Quadratics for Applications in Deep Learning
by: Tatzel, Lukas, et al.
Published: (2024)
by: Tatzel, Lukas, et al.
Published: (2024)
Multi-layer Stack Ensembles for Time Series Forecasting
by: Bosch, Nathanael, et al.
Published: (2025)
by: Bosch, Nathanael, et al.
Published: (2025)
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
by: Eschenhagen, Runa, et al.
Published: (2023)
by: Eschenhagen, Runa, et al.
Published: (2023)
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
by: Papamarkou, Theodore, et al.
Published: (2024)
by: Papamarkou, Theodore, et al.
Published: (2024)
Sequential Off-Policy Learning with Logarithmic Smoothing
by: Haddouche, Maxime, et al.
Published: (2025)
by: Haddouche, Maxime, et al.
Published: (2025)
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
by: Cinquin, Tristan, et al.
Published: (2024)
by: Cinquin, Tristan, et al.
Published: (2024)
Emergent Low-Rank Training Dynamics in MLPs with Smooth Activations
by: Xu, Alec S., et al.
Published: (2026)
by: Xu, Alec S., et al.
Published: (2026)
Scalable Bayesian Inference for Nonlinear Conservation Laws
by: Weiland, Tim, et al.
Published: (2026)
by: Weiland, Tim, et al.
Published: (2026)
InRank: Incremental Low-Rank Learning
by: Zhao, Jiawei, et al.
Published: (2023)
by: Zhao, Jiawei, et al.
Published: (2023)
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
by: Hennig, Leona, et al.
Published: (2024)
by: Hennig, Leona, et al.
Published: (2024)
Application of predictive machine learning in pen & paper RPG game design
by: Śliwa, Jolanta
Published: (2025)
by: Śliwa, Jolanta
Published: (2025)
One Set to Rule Them All: How to Obtain General Chemical Conditions via Bayesian Optimization over Curried Functions
by: Schmid, Stefan P., et al.
Published: (2025)
by: Schmid, Stefan P., et al.
Published: (2025)
Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization
by: Hajimolahoseini, Habib, et al.
Published: (2023)
by: Hajimolahoseini, Habib, et al.
Published: (2023)
Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling
by: de Carvalho, Gustavo Sutter Pessurno, et al.
Published: (2025)
by: de Carvalho, Gustavo Sutter Pessurno, et al.
Published: (2025)
Active Bipartite Ranking with Smooth Posterior Distributions
by: Cheshire, James, et al.
Published: (2026)
by: Cheshire, James, et al.
Published: (2026)
Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning
by: Goddla, Vikram
Published: (2024)
by: Goddla, Vikram
Published: (2024)
Rethinking Approximate Gaussian Inference in Classification
by: Mucsányi, Bálint, et al.
Published: (2025)
by: Mucsányi, Bálint, et al.
Published: (2025)
Low-Rank Adaptation of Evolutionary Deep Neural Networks for Efficient Learning of Time-Dependent PDEs
by: Zhang, Jiahao, et al.
Published: (2025)
by: Zhang, Jiahao, et al.
Published: (2025)
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
by: Yaras, Can, et al.
Published: (2024)
by: Yaras, Can, et al.
Published: (2024)
Similar Items
-
Mitigating Forgetting in Low Rank Adaptation
by: Sliwa, Joanna, et al.
Published: (2025) -
Introduction to the Analysis of Probabilistic Decision-Making Algorithms
by: Kristiadi, Agustinus
Published: (2025) -
On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective
by: Wenger, Jonathan, et al.
Published: (2023) -
Uncertainty-Guided Likelihood Tree Search
by: Grosse, Julia, et al.
Published: (2024) -
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
by: Schmidt, Jonathan, et al.
Published: (2023)