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Methods/Exponential Decay

Exponential Decay

GeneralIntroduced 2000110 papers

Description

Exponential Decay is a learning rate schedule where we decay the learning rate with more iterations using an exponential function:

$\text{lr} = \text{lr}\_{0}\exp\left(-kt\right)$

Image Credit: Suki Lau

Papers Using This Method

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs2025-06-23 Compact Amplified Laser Power Stabilization Using Robust Active Disturbance Rejection Control with Sensor Noise Decoupling2025-06-10 Towards Data-Driven Model-Free Safety-Critical Control2025-06-07 Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget2025-06-03 Beyond Exponential Decay: Rethinking Error Accumulation in Large Language Models2025-05-30 Continuous-Time Attention: PDE-Guided Mechanisms for Long-Sequence Transformers2025-05-27 Incremental Attractor Neural Network Modelling of the Lifespan Retrieval Curve2025-04-20 On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems2025-04-11 Temporal Model On Quantum Logic2025-02-09 Uniform-in-time weak propagation of chaos for consensus-based optimization2025-02-01 Contextual Online Decision Making with Infinite-Dimensional Functional Regression2025-01-30 Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals2025-01-28 The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubits2024-12-16 Modelling Networked Dynamical System by Temporal Graph Neural ODE with Irregularly Partial Observed Time-series Data2024-11-29 Scalable spectral representations for multi-agent reinforcement learning in network MDPs2024-10-22 Nonlinear second-order dynamics describe labial constriction trajectories across languages and contexts2024-10-10 Efficient $1$-bit tensor approximations2024-10-02 Koopman Operator in the Weighted Function Spaces and its Learning for the Estimation of Lyapunov and Zubov Functions2024-09-30 Super Level Sets and Exponential Decay: A Synergistic Approach to Stable Neural Network Training2024-09-25 Quasi-potential and drift decomposition in stochastic systems by sparse identification2024-09-10