4 citations · 5 across the 10 of their papers we have counts for
10 papers
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…
MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE
Soheil Zibakhsh, Mohammad Samragh, Kumari Nishu +3
The generation quality of large language models (LLMs) is often improved by utilizing inference-time sequence-level scaling methods (e.g., Chain-of-Thought). We introduce hyper-par…
Mirror Speculative Decoding: Breaking the Serial Barrier in LLM Inference
Nikhil Bhendawade, Kumari Nishu, Arnav Kundu +3
Speculative decoding accelerates LLM inference by using a draft model to look ahead, but gains are capped by the cost of autoregressive draft generation: increasing draft size elev…
MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
Lauren. A Hannah, Soheil Zibakhsh, Kumari Nishu +4
Sparse Mixtures of Experts (MoEs) are typically trained to operate at a fixed sparsity level, e.g. in a top- gating function. This global sparsity level determines an operat…
Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential
Mohammad Samragh, Arnav Kundu, David Harrison +4
Autoregressive language models are constrained by their inherently sequential nature, generating one token at a time. This paradigm limits inference speed and parallelism, especial…
From Dense to Dynamic: Token-Difficulty Driven MoEfication of Pre-Trained LLMs
Kumari Nishu, Sachin Mehta, Samira Abnar +6
Training large language models (LLMs) for different inference constraints is computationally expensive, limiting control over efficiency-accuracy trade-offs. Moreover, once trained…
SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting
Kumari Nishu, Minsik Cho, Devang Naik
User-defined keyword spotting on a resource-constrained edge device is challenging. However, keywords are often bounded by a maximum keyword length, which has been largely under-le…
Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder
Alexandre Bittar, Paul Dixon, Mohammad Samragh +2
Using a vision-inspired keyword spotting framework, we propose an architecture with input-dependent dynamic depth capable of processing streaming audio. Specifically, we extend a c…
Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding
Kumari Nishu, Minsik Cho, Paul Dixon +1
Spotting user-defined/flexible keywords represented in text frequently uses an expensive text encoder for joint analysis with an audio encoder in an embedding space, which can suff…
Matching Latent Encoding for Audio-Text based Keyword Spotting
Kumari Nishu, Minsik Cho, Devang Naik
Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-quality results, but the key challenge of how to semantically align two embeddings for multi-word…