Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
Fuente:
arXiv
Guardado en:
| Autores principales: | Dhahri, Rayen, Immer, Alexander, Charpentier, Betrand, Günnemann, Stephan, Fortuin, Vincent |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Improving Neural Additive Models with Bayesian Principles
por: Bouchiat, Kouroche, et al.
Publicado: (2023)
por: Bouchiat, Kouroche, et al.
Publicado: (2023)
Quant-Trim in Practice: Improved Cross-Platform Low-Bit Deployment on Edge NPUs
por: Dhahri, Rayen, et al.
Publicado: (2025)
por: Dhahri, Rayen, et al.
Publicado: (2025)
Occam's Razor is Only as Sharp as Your ELBO
por: Harvey, Ethan, et al.
Publicado: (2026)
por: Harvey, Ethan, et al.
Publicado: (2026)
A Geometric Modeling of Occam's Razor in Deep Learning
por: Sun, Ke, et al.
Publicado: (2019)
por: Sun, Ke, et al.
Publicado: (2019)
Joint Model and Data Sparsification via the Marginal Likelihood
por: Timans, Alexander, et al.
Publicado: (2026)
por: Timans, Alexander, et al.
Publicado: (2026)
Amortising Inference and Meta-Learning Priors in Neural Networks
por: Rochussen, Tommy, et al.
Publicado: (2026)
por: Rochussen, Tommy, et al.
Publicado: (2026)
Incorporating Unlabelled Data into Bayesian Neural Networks
por: Sharma, Mrinank, et al.
Publicado: (2023)
por: Sharma, Mrinank, et al.
Publicado: (2023)
In-Context Occam's Razor: How Transformers Prefer Simpler Hypotheses on the Fly
por: Deora, Puneesh, et al.
Publicado: (2025)
por: Deora, Puneesh, et al.
Publicado: (2025)
Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?
por: Ibrahim, Mark, et al.
Publicado: (2024)
por: Ibrahim, Mark, et al.
Publicado: (2024)
Sparse Gaussian Neural Processes
por: Rochussen, Tommy, et al.
Publicado: (2025)
por: Rochussen, Tommy, et al.
Publicado: (2025)
Stein Variational Newton Neural Network Ensembles
por: Flöge, Klemens, et al.
Publicado: (2024)
por: Flöge, Klemens, et al.
Publicado: (2024)
Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization
por: Riegler, Ben, et al.
Publicado: (2026)
por: Riegler, Ben, et al.
Publicado: (2026)
Adversarial Attacks on Graph Neural Networks via Meta Learning
por: Zügner, Daniel, et al.
Publicado: (2019)
por: Zügner, Daniel, et al.
Publicado: (2019)
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation
por: Paul, Richard D., et al.
Publicado: (2024)
por: Paul, Richard D., et al.
Publicado: (2024)
Energy-based Epistemic Uncertainty for Graph Neural Networks
por: Fuchsgruber, Dominik, et al.
Publicado: (2024)
por: Fuchsgruber, Dominik, et al.
Publicado: (2024)
Generative Modeling with Bayesian Sample Inference
por: Lienen, Marten, et al.
Publicado: (2025)
por: Lienen, Marten, et al.
Publicado: (2025)
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
por: Gao, Nicholas, et al.
Publicado: (2024)
por: Gao, Nicholas, et al.
Publicado: (2024)
Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance
por: Fuchsgruber, Dominik, et al.
Publicado: (2024)
por: Fuchsgruber, Dominik, et al.
Publicado: (2024)
Structurally Prune Anything: Any Architecture, Any Framework, Any Time
por: Wang, Xun, et al.
Publicado: (2024)
por: Wang, Xun, et al.
Publicado: (2024)
Uncertainty for Active Learning on Graphs
por: Fuchsgruber, Dominik, et al.
Publicado: (2024)
por: Fuchsgruber, Dominik, et al.
Publicado: (2024)
Spatio-Spectral Graph Neural Networks
por: Geisler, Simon, et al.
Publicado: (2024)
por: Geisler, Simon, et al.
Publicado: (2024)
GemNet: Universal Directional Graph Neural Networks for Molecules
por: Gasteiger, Johannes, et al.
Publicado: (2021)
por: Gasteiger, Johannes, et al.
Publicado: (2021)
Can Transformers Learn Full Bayesian Inference in Context?
por: Reuter, Arik, et al.
Publicado: (2025)
por: Reuter, Arik, et al.
Publicado: (2025)
On the Effect of Regularization on Nonparametric Mean-Variance Regression
por: Wong-Toi, Eliot, et al.
Publicado: (2025)
por: Wong-Toi, Eliot, et al.
Publicado: (2025)
Understanding Pathologies of Deep Heteroskedastic Regression
por: Wong-Toi, Eliot, et al.
Publicado: (2023)
por: Wong-Toi, Eliot, et al.
Publicado: (2023)
Discrete Bayesian Sample Inference for Graph Generation
por: Petersen, Ole, et al.
Publicado: (2025)
por: Petersen, Ole, et al.
Publicado: (2025)
Exact Certification of (Graph) Neural Networks Against Label Poisoning
por: Sabanayagam, Mahalakshmi, et al.
Publicado: (2024)
por: Sabanayagam, Mahalakshmi, et al.
Publicado: (2024)
Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
por: Sommer, Johanna, et al.
Publicado: (2025)
por: Sommer, Johanna, et al.
Publicado: (2025)
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
por: Eschenhagen, Runa, et al.
Publicado: (2023)
por: Eschenhagen, Runa, et al.
Publicado: (2023)
Learning on a Razor's Edge: Identifiability and Singularity of Polynomial Neural Networks
por: Shahverdi, Vahid, et al.
Publicado: (2025)
por: Shahverdi, Vahid, et al.
Publicado: (2025)
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
por: Gosch, Lukas, et al.
Publicado: (2024)
por: Gosch, Lukas, et al.
Publicado: (2024)
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
por: Gao, Nicholas, et al.
Publicado: (2024)
por: Gao, Nicholas, et al.
Publicado: (2024)
ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods
por: Kmicikiewicz, Michal, et al.
Publicado: (2025)
por: Kmicikiewicz, Michal, et al.
Publicado: (2025)
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
por: Scholten, Yan, et al.
Publicado: (2023)
por: Scholten, Yan, et al.
Publicado: (2023)
Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning
por: Mohgaonkar, Ajinkya, et al.
Publicado: (2026)
por: Mohgaonkar, Ajinkya, et al.
Publicado: (2026)
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE
por: Regol, Florence, et al.
Publicado: (2024)
por: Regol, Florence, et al.
Publicado: (2024)
Marginal Pseudo-Likelihood Learning of Markov Network structures
por: Pensar, Johan, et al.
Publicado: (2014)
por: Pensar, Johan, et al.
Publicado: (2014)
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning
por: Scholten, Yan, et al.
Publicado: (2024)
por: Scholten, Yan, et al.
Publicado: (2024)
Occam Gradient Descent
por: Kausik, B. N.
Publicado: (2024)
por: Kausik, B. N.
Publicado: (2024)
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
por: Cinquin, Tristan, et al.
Publicado: (2024)
por: Cinquin, Tristan, et al.
Publicado: (2024)
Ejemplares similares
-
Improving Neural Additive Models with Bayesian Principles
por: Bouchiat, Kouroche, et al.
Publicado: (2023) -
Quant-Trim in Practice: Improved Cross-Platform Low-Bit Deployment on Edge NPUs
por: Dhahri, Rayen, et al.
Publicado: (2025) -
Occam's Razor is Only as Sharp as Your ELBO
por: Harvey, Ethan, et al.
Publicado: (2026) -
A Geometric Modeling of Occam's Razor in Deep Learning
por: Sun, Ke, et al.
Publicado: (2019) -
Joint Model and Data Sparsification via the Marginal Likelihood
por: Timans, Alexander, et al.
Publicado: (2026)