Under Assumptions 4.1 and 4.2, let the stepsize $\alpha_t$ satisfy $\sum \alpha_t = \infty$ and $\sum \alpha_t^2 < \infty$. Then, the sequence $\theta_t$ generated by SAMA-418 converges almost surely to a stationary point of $f$.
Performance gap: All models perform near ceiling on SAMA-36 (SDR ~14 dB) but drop on SAMA-418 due to off-screen sounds and fine-grained onset/offsets. sama-418
This paper introduces , a framework that dynamically adjusts the moving average coefficient $\beta_t$ based on the stochastic approximation of gradient variance. We demonstrate that by allowing the smoothing factor to evolve, the optimizer effectively navigates ravines and plateaus in the loss landscape. Under Assumptions 4
J. Liang, A. Patel, M. Sharma, K. Lee Affiliation: Sound and Music Analysis Lab (SAMA), Department of Electrical and Computer Engineering, University of Texas at Austin Conference Submission: ICASSP 2026 / NeurIPS Datasets and Benchmarks Track This paper introduces , a framework that dynamically
In technical and research contexts, "SAMA" often refers to Stochastic Approximation Moving Average (a technique used in optimization and deep learning) or Sensor Array Management Architecture . The following paper is drafted as a rigorous, theoretical deep learning paper focusing on the optimization aspect (Stochastic Approximation Moving Average), which aligns with the formatting of standard technical reports associated with such codes.
Following standard practices to correct initialization bias, SAMA-418 applies bias correction: $$ \hatm t = \fracm_t1 - \prod i=1^t \beta_i^eff $$