Powerset Convolutional Neural Networks
Fuente:
arXiv
Guardado en:
| Autores principales: | Wendler, Chris, Alistarh, Dan, Püschel, Markus |
|---|---|
| Formato: | Preprint |
| Publicado: |
2019
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Learning DAGs from Data with Few Root Causes
por: Misiakos, Panagiotis, et al.
Publicado: (2023)
por: Misiakos, Panagiotis, et al.
Publicado: (2023)
Causal Fourier Analysis on Directed Acyclic Graphs and Posets
por: Seifert, Bastian, et al.
Publicado: (2022)
por: Seifert, Bastian, et al.
Publicado: (2022)
SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input
por: Misiakos, Panagiotis, et al.
Publicado: (2025)
por: Misiakos, Panagiotis, et al.
Publicado: (2025)
SPADE: Sparsity-Guided Debugging for Deep Neural Networks
por: Moakhar, Arshia Soltani, et al.
Publicado: (2023)
por: Moakhar, Arshia Soltani, et al.
Publicado: (2023)
Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data
por: Iofinova, Eugenia, et al.
Publicado: (2026)
por: Iofinova, Eugenia, et al.
Publicado: (2026)
Model Compression with Exact Budget Constraints via Riemannian Manifolds
por: Helcig, Michael, et al.
Publicado: (2026)
por: Helcig, Michael, et al.
Publicado: (2026)
Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
por: Chen, Jiale, et al.
Publicado: (2024)
por: Chen, Jiale, et al.
Publicado: (2024)
Towards Combinatorial Interpretability of Neural Computation
por: Adler, Micah, et al.
Publicado: (2025)
por: Adler, Micah, et al.
Publicado: (2025)
LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs
por: Schultheis, Erik, et al.
Publicado: (2025)
por: Schultheis, Erik, et al.
Publicado: (2025)
MatGPTQ: Accurate and Efficient Post-Training Matryoshka Quantization
por: Kleinegger, Maximilian, et al.
Publicado: (2026)
por: Kleinegger, Maximilian, et al.
Publicado: (2026)
Statistically-Lossless Quantization of Large Language Models
por: Helcig, Michael, et al.
Publicado: (2026)
por: Helcig, Michael, et al.
Publicado: (2026)
Simple Opinion Dynamics for No-Regret Learning
por: Lazarsfeld, John, et al.
Publicado: (2023)
por: Lazarsfeld, John, et al.
Publicado: (2023)
Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks
por: Ancelotti, Amy, et al.
Publicado: (2024)
por: Ancelotti, Amy, et al.
Publicado: (2024)
Apertus LLM Family Expansion via Distillation and Quantization
por: Panferov, Andrei, et al.
Publicado: (2026)
por: Panferov, Andrei, et al.
Publicado: (2026)
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
por: Sieberling, Oliver, et al.
Publicado: (2024)
por: Sieberling, Oliver, et al.
Publicado: (2024)
Communication-Efficient Federated Learning With Data and Client Heterogeneity
por: Zakerinia, Hossein, et al.
Publicado: (2022)
por: Zakerinia, Hossein, et al.
Publicado: (2022)
Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation
por: Panferov, Andrei, et al.
Publicado: (2026)
por: Panferov, Andrei, et al.
Publicado: (2026)
Benchmarking Convolutional Neural Network and Graph Neural Network based Surrogate Models on a Real-World Car External Aerodynamics Dataset
por: Jacob, Sam Jacob, et al.
Publicado: (2025)
por: Jacob, Sam Jacob, et al.
Publicado: (2025)
Towards Robust Scaling Laws for Optimizers
por: Volkova, Alexandra, et al.
Publicado: (2026)
por: Volkova, Alexandra, et al.
Publicado: (2026)
Multiscale Training of Convolutional Neural Networks
por: Ahamed, Shadab, et al.
Publicado: (2025)
por: Ahamed, Shadab, et al.
Publicado: (2025)
Advection Augmented Convolutional Neural Networks
por: Zakariaei, Niloufar, et al.
Publicado: (2024)
por: Zakariaei, Niloufar, et al.
Publicado: (2024)
Variational Graph Convolutional Neural Networks
por: Oleksiienko, Illia, et al.
Publicado: (2025)
por: Oleksiienko, Illia, et al.
Publicado: (2025)
Dual Convexified Convolutional Neural Networks
por: Bai, Site, et al.
Publicado: (2022)
por: Bai, Site, et al.
Publicado: (2022)
Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective
por: Li, Tenghui, et al.
Publicado: (2021)
por: Li, Tenghui, et al.
Publicado: (2021)
MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
por: Modoranu, Ionut-Vlad, et al.
Publicado: (2026)
por: Modoranu, Ionut-Vlad, et al.
Publicado: (2026)
Beyond Outliers: A Study of Optimizers Under Quantization
por: Vlassis, Georgios, et al.
Publicado: (2025)
por: Vlassis, Georgios, et al.
Publicado: (2025)
CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training
por: Tabesh, Soroush, et al.
Publicado: (2025)
por: Tabesh, Soroush, et al.
Publicado: (2025)
Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models
por: Kurtic, Eldar, et al.
Publicado: (2024)
por: Kurtic, Eldar, et al.
Publicado: (2024)
In-flight Novelty Detection with Convolutional Neural Networks
por: Hartwell, Adam, et al.
Publicado: (2021)
por: Hartwell, Adam, et al.
Publicado: (2021)
Efficient Data Selection at Scale via Influence Distillation
por: Nikdan, Mahdi, et al.
Publicado: (2025)
por: Nikdan, Mahdi, et al.
Publicado: (2025)
LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics
por: Robert, Thomas, et al.
Publicado: (2024)
por: Robert, Thomas, et al.
Publicado: (2024)
A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces
por: Cui, Yuntian, et al.
Publicado: (2024)
por: Cui, Yuntian, et al.
Publicado: (2024)
MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models
por: Frantar, Elias, et al.
Publicado: (2024)
por: Frantar, Elias, et al.
Publicado: (2024)
The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information
por: Wu, Diyuan, et al.
Publicado: (2024)
por: Wu, Diyuan, et al.
Publicado: (2024)
DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers
por: Modoranu, Ionut-Vlad, et al.
Publicado: (2026)
por: Modoranu, Ionut-Vlad, et al.
Publicado: (2026)
WUSH: Near-Optimal Adaptive Transforms for LLM Quantization
por: Chen, Jiale, et al.
Publicado: (2025)
por: Chen, Jiale, et al.
Publicado: (2025)
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
por: Ansaripour, Matin, et al.
Publicado: (2022)
por: Ansaripour, Matin, et al.
Publicado: (2022)
ECO: Quantized Training without Full-Precision Master Weights
por: Nikdan, Mahdi, et al.
Publicado: (2026)
por: Nikdan, Mahdi, et al.
Publicado: (2026)
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
por: Nikdan, Mahdi, et al.
Publicado: (2024)
por: Nikdan, Mahdi, et al.
Publicado: (2024)
Binarized Simplicial Convolutional Neural Networks
por: Yan, Yi, et al.
Publicado: (2024)
por: Yan, Yi, et al.
Publicado: (2024)
Ejemplares similares
-
Learning DAGs from Data with Few Root Causes
por: Misiakos, Panagiotis, et al.
Publicado: (2023) -
Causal Fourier Analysis on Directed Acyclic Graphs and Posets
por: Seifert, Bastian, et al.
Publicado: (2022) -
SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input
por: Misiakos, Panagiotis, et al.
Publicado: (2025) -
SPADE: Sparsity-Guided Debugging for Deep Neural Networks
por: Moakhar, Arshia Soltani, et al.
Publicado: (2023) -
Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data
por: Iofinova, Eugenia, et al.
Publicado: (2026)