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researcher

Bryan Catanzaro

4 papers here

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

author position
  • middle author2
  • last author1

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL1
  • cs.CV1
  • cs.LG1
  • cs.SD1
ORCID 0000-0003-0034-7728
same name
  • Bryan Catanzaro — 82 papers, h 61
  • Bryan Catanzaro — 71 papers, h 33
  • Bryan Catanzaro — 1 paper, h 0
  • Bryan Catanzaro — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162025
most citedDSD: Dense-Sparse-Dense Training for Deep Neural Networks

143 citations · 144 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2025

NVIDIA Nemotron Nano V2 VL

NVIDIA, :, Amala Sanjay Deshmukh +121

We introduce Nemotron Nano V2 VL, the latest model of the Nemotron vision-language series designed for strong real-world document understanding, long video comprehension, and reaso…

cs.CL2025★ 1 cited

Llama-Nemotron: Efficient Reasoning Models

Akhiad Bercovich, Itay Levy, Izik Golan +132

We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an ope…

cs.SD2024

TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization

Chia-Yu Hung, Navonil Majumder, Zhifeng Kong +6

We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio in just 3.7 seconds on a sin…

cs.CV2016★ 143 cited

DSD: Dense-Sparse-Dense Training for Deep Neural Networks

Song Han, Jeff Pool, Sharan Narang +9

Modern deep neural networks have a large number of parameters, making them very hard to train. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural netw…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.