3 citations · 4 across the 6 of their papers we have counts for
6 papers
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
Convergence Analysis of Stochastic Accelerated Gradient Methods for Generalized Smooth Optimizations
Chenhao Yu, Yusu Hong, Junhong Lin
We investigate the Randomized Stochastic Accelerated Gradient (RSAG) method, utilizing either constant or adaptive step sizes, for stochastic optimization problems with generalized…
B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests
Mouxiang Chen, Zhongxin Liu, He Tao +4
Selecting the best code solution from multiple generated ones is an essential task in code generation, which can be achieved by using some reliable validators (e.g., developer-writ…
Revisiting Convergence of AdaGrad with Relaxed Assumptions
Yusu Hong, Junhong Lin
In this study, we revisit the convergence of AdaGrad with momentum (covering AdaGrad as a special case) on non-convex smooth optimization problems. We consider a general noise mode…
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
Yusu Hong, Junhong Lin
The Adaptive Momentum Estimation (Adam) algorithm is highly effective in training various deep learning tasks. Despite this, there's limited theoretical understanding for Adam, esp…
High Probability Convergence of Adam Under Unbounded Gradients and Affine Variance Noise
Yusu Hong, Junhong Lin
In this paper, we study the convergence of the Adaptive Moment Estimation (Adam) algorithm under unconstrained non-convex smooth stochastic optimizations. Despite the widespread us…