◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Ethan Smith

8 papers hereh-index 327 citations19 works total

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

author position
  • sole author2
  • first author2
  • middle author3
  • last author1

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG4
same name
  • Ethan Smith — 3 papers, h 1
  • Ethan Smith — 2 papers, h 3
  • Ethan Smith — 1 paper, h 0
  • Ethan Smith — 1 paper, h 0

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

works on
adaptive optimizers 1neural networks 1optimization 1transformers 1weight reparameterization 1

From the 1 of 8 linked papers with an AI index.

activity
20242026
collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Learning in Curved Weight Space:Exponential-Linear Weight Reparameterization for Improved Optimization

Ethan Smith

The paper proposes a new weight reparameterization that combines an exponential and a linear pathway, creating a curved parameter space that enables more proportional updates and s…

cs.LG2026

NOBLE: Accelerating Transformers with Nonlinear Low-Rank Branches

Ethan Smith

We introduce NOBLE (Nonlinear lOw-rank Branch for Linear Enhancement), an architectural augmentation that adds nonlinear low-rank branches to transformer linear layers. Unlike LoRA…

cs.LG2026

From Tokens to Numbers: Continuous Number Modeling for SVG Generation

Michael Ogezi, Martin Bell, Freda Shi +1

For certain image generation tasks, vector graphics such as Scalable Vector Graphics (SVGs) offer clear benefits such as increased flexibility, size efficiency, and editing ease, b…

cs.LG2024

LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization

Ethan Smith, Rami Seid, Alberto Hojel +2

Low-Rank Adaptation (LoRA) and other parameter-efficient fine-tuning (PEFT) methods provide low-memory, storage-efficient solutions for personalizing text-to-image models. However,…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.