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researcher

Nitesh Sekhar

Amazon, UC San Diego

4 papers hereh-index 4130 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2

Across the 2 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.AI1
  • cs.LG1
affiliations
  • Amazon, UC San Diego

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators

4 papers

cs.LG2026

MoE Routing Testbed: Studying Expert Specialization and Routing Behavior at Small Scale

Tobias Falke, Nicolas Anastassacos, Samson Tan +6

Sparse Mixture-of-Experts (MoE) architectures are increasingly popular for frontier large language models (LLM) but they introduce training challenges due to routing complexity. Fu…

cs.CV2026

DeepInsert: Early Layer Bypass for Efficient and Performant Multimodal Understanding

Moulik Choraria, Xinbo Wu, Akhil Bhimaraju +5

Hyperscaling of data and parameter count in LLMs is yielding diminishing improvement when weighed against training costs, underlining a growing need for more efficient finetuning a…

cs.AI2025

The Amazon Nova Family of Models: Technical Report and Model Card

Amazon AGI, Aaron Langford, Aayush Shah +783

We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…

cs.CV2024

Semantically Grounded QFormer for Efficient Vision Language Understanding

Moulik Choraria, Xinbo Wu, Sourya Basu +5

General purpose Vision Language Models (VLMs) have received tremendous interest in recent years, owing to their ability to learn rich vision-language correlations as well as their…

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