4 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
Audio LLMs Know When They Can't Hear You
Amirhosein Javadi, Richa Dixit, Mehrdad Farajtabar +3
Audio large language models allow users to interact with the model through speech. When an input recording is too degraded, the model may misinterpret the user's query and respond…
Efficient Mixture-of-Experts with Speculative Decoding via Expert Coactivation
Kumari Nishu, Han-Byul Kim, Santosh Chilkunda +13
Mixture-of-Experts (MoE) models are increasingly deployed alongside Speculative Decoding (SD) to accelerate inference, but combining the two is challenging. SD improves the inferen…
Compressing Streaming Neural Audio Encoders via Latent-Space Distillation
Prasanth Yadla, Mohammad Samragh, Dongseong Hwang +7
System-wide Dictation on Apple devices runs entirely on-device, and the speech it transcribes reaches the foundation model through a tokenizer: an encoder that maps short windows o…
SpecMD: A Comprehensive Study On Speculative Expert Prefetching
Duc Hoang, Ajay Jaiswal, Mohammad Samragh +1
Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model's parameters is used during each inference. However, to translate this spar…