:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhou, Yefan, Yang, Yaoqing, Chang, Arin, Mahoney, Michael W.
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2305.18383
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
di: Lu, Haiquan, et al.
Pubblicazione: (2024)

A Model Zoo on Phase Transitions in Neural Networks
di: Schürholt, Konstantin, et al.
Pubblicazione: (2025)

Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
di: Lu, Haiquan, et al.
Pubblicazione: (2024)

MD tree: a model-diagnostic tree grown on loss landscape
di: Zhou, Yefan, et al.
Pubblicazione: (2024)

Model Balancing Helps Low-data Training and Fine-tuning
di: Liu, Zihang, et al.
Pubblicazione: (2024)

Learning to Discover Iterative Spectral Algorithms
di: Liu, Zihang, et al.
Pubblicazione: (2026)

RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms Through Curriculum Design and Graph-Based Search
di: Xiong, Jinglong, et al.
Pubblicazione: (2026)

Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization
di: Wang, Yuxin, et al.
Pubblicazione: (2026)

Mitigating Memorization In Language Models
di: Sakarvadia, Mansi, et al.
Pubblicazione: (2024)

The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators
di: Sakarvadia, Mansi, et al.
Pubblicazione: (2025)

Evaluating Loss Landscapes from a Topology Perspective
di: Xie, Tiankai, et al.
Pubblicazione: (2024)

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
di: Hu, Yuanzhe, et al.
Pubblicazione: (2025)

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models
di: Liao, Zhenyu, et al.
Pubblicazione: (2025)

Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
di: Pang, Tianyu, et al.
Pubblicazione: (2026)

KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexity
di: Jia, Yaning, et al.
Pubblicazione: (2025)

Spectral Insights into Data-Oblivious Critical Layers in Large Language Models
di: Liu, Xuyuan, et al.
Pubblicazione: (2025)

Visualizing Loss Functions as Topological Landscape Profiles
di: Geniesse, Caleb, et al.
Pubblicazione: (2024)

Models of Heavy-Tailed Mechanistic Universality
di: Hodgkinson, Liam, et al.
Pubblicazione: (2025)

LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics
di: Xie, Tiankai, et al.
Pubblicazione: (2024)

A PRISMA Driven Systematic Review of Publicly Available Datasets for Benchmark and Model Developments for Industrial Defect Detection
di: Akbas, Can, et al.
Pubblicazione: (2024)

From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks
di: Kothapalli, Vignesh, et al.
Pubblicazione: (2024)

HOPE for a Robust Parameterization of Long-memory State Space Models
di: Yu, Annan, et al.
Pubblicazione: (2024)

Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis
di: Chen, Jiaqing, et al.
Pubblicazione: (2026)

Variation in Verification: Understanding Verification Dynamics in Large Language Models
di: Zhou, Yefan, et al.
Pubblicazione: (2025)

Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
di: Dereziński, Michał, et al.
Pubblicazione: (2024)

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
di: Na, Sen, et al.
Pubblicazione: (2022)

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation
di: Deng, Shenyang, et al.
Pubblicazione: (2026)

Fast and Compact Tsetlin Machine Inference on CPUs Using Instruction-Level Optimization
di: Zeng, Yefan, et al.
Pubblicazione: (2025)

Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
di: Erichson, N. Benjamin, et al.
Pubblicazione: (2022)

Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation
di: Liu, Xiaotian, et al.
Pubblicazione: (2026)

Towards Scalable and Versatile Weight Space Learning
di: Schürholt, Konstantin, et al.
Pubblicazione: (2024)

Suspicious Alignment of SGD: A Fine-Grained Step Size Condition Analysis
di: Deng, Shenyang, et al.
Pubblicazione: (2026)

Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels
di: Wang, Zhu, et al.
Pubblicazione: (2024)

The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering
di: Zhou, Yefan, et al.
Pubblicazione: (2026)

Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
di: Naiman, Ilan, et al.
Pubblicazione: (2023)

The Interpolating Information Criterion for Overparameterized Models
di: Hodgkinson, Liam, et al.
Pubblicazione: (2023)

Using Pre-trained LLMs for Multivariate Time Series Forecasting
di: Wolff, Malcolm L., et al.
Pubblicazione: (2025)

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
di: Kang, Yuhan, et al.
Pubblicazione: (2025)

Tuning Frequency Bias of State Space Models
di: Yu, Annan, et al.
Pubblicazione: (2024)

How many classifiers do we need?
di: Kim, Hyunsuk, et al.
Pubblicazione: (2024)