7 citations · 7 across the 5 of their papers we have counts for
6 papers
Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate
Hanyu Lin, Min Cai, Jiawei Wen +1
Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface al…
JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
Aichen Cai, Anmeng Zhang, Anyu Li +66
We introduce JoyAI-LLM Flash, an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performance and token efficiency in the sub-50B…
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
Krzysztof Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury +4
We present a new class of fast polylog-linear algorithms based on the theory of structured matrices (in particular low displacement rank) for integrating tensor fields defined on w…
Efficient Graph Field Integrators Meet Point Clouds
Krzysztof Choromanski, Arijit Sehanobish, Han Lin +13
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds. The first class, SeparatorFactorization(SF), leverages the bounded genus o…
Hybrid Random Features
Krzysztof Choromanski, Haoxian Chen, Han Lin +10
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel es…
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
Krzysztof Choromanski, Han Lin, Haoxian Chen +7
In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable…