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Neural Network Optimization

903 papers

Papers per year

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Papers

Last-iterate Convergence of Shuffling Momentum Gradient Method under the Kurdyka-Lojasiewicz Inequality JMLR 2025
On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm JMLR 2025
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization JMLR 2025
Sequence-level Large Language Model Training with Contrastive Preference Optimization NAACL 2025
ENAF: A Multi-Exit Network with an Adaptive Patch Fusion for Large Image Super Resolution WACV 2025
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence NIPS 2024
Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training NIPS 2024
Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization NIPS 2024
Weight decay induces low-rank attention layers NIPS 2024
Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms NIPS 2024
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression NIPS 2024
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences NIPS 2024
Understanding Representation of Deep Equilibrium Models from Neural Collapse Perspective NIPS 2024
The Road Less Scheduled NIPS 2024
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions NIPS 2024
Sparse High Rank Adapters NIPS 2024
Loki: Low-rank Keys for Efficient Sparse Attention NIPS 2024
Ordered Momentum for Asynchronous SGD NIPS 2024
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation NIPS 2024
Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models NIPS 2024
Why Do We Need Weight Decay in Modern Deep Learning? NIPS 2024
Consistency Diffusion Bridge Models NIPS 2024
Identifying Equivalent Training Dynamics NIPS 2024
The Implicit Bias of Adam on Separable Data NIPS 2024
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training NIPS 2024