ScienceToStartup
TrendsTopicsSavedArticlesChangelogCareersAbout

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs
All systems operational

Product

  • Dashboard
  • Workspace
  • Build Loop
  • Research Map
  • Trends
  • Topics
  • Articles

Enterprise

  • TTO Dashboard
  • Scout Reports
  • RFP Marketplace
  • API

Resources

  • All Resources
  • Benchmark
  • Database
  • Dataset
  • Calculator
  • Glossary
  • State Reports
  • Industry Index
  • Directory
  • Templates
  • Alternatives
  • Changelog
  • FAQ
  • Docs

Company

  • About
  • Careers
  • For Media
  • Privacy Policy
  • Legal
  • Contact

Community

  • Open Source
  • Community
ScienceToStartup

Copyright © 2026 ScienceToStartup. All rights reserved.

Privacy Policy|Legal
  1. Home
  2. Signal Canvas
  3. Hierarchical Zero-Order Optimization for Deep Neural Network
← Back to Paper

Hierarchical Zero-Order Optimization for Deep Neural Networks

Fresh1d ago
Export BriefOpen in Build LoopConnect with Author
View PDF ↗
Viability
0.0/10

Compared to this week’s papers

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 28

Proof: pending

Distribution: unknown

Source paper: Hierarchical Zero-Order Optimization for Deep Neural Networks

PDF: https://arxiv.org/pdf/2602.10607v1

First buyer signal: unknown

Distribution channel: unknown

Starting…

Dimensions overall score 5.0

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Claim extraction is still pending for this paper. Check back after the next analysis run.

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Keep exploring

Builds On This
ZO-SAM: Zero-Order Sharpness-Aware Minimization for Efficient Sparse Training
Score 3.0down
Builds On This
HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
Score 4.0down
Builds On This
Inverse Depth Scaling From Most Layers Being Similar
Score 2.0down
Builds On This
Local learning for stable backpropagation-free neural network training towards physical learning
Score 3.0down
Builds On This
Does Your Optimizer Care How You Normalize? Normalization-Optimizer Coupling in LLM Training
Score 3.0down
Prior Work
CurvZO: Adaptive Curvature-Guided Sparse Zeroth-Order Optimization for Efficient LLM Fine-Tuning
Score 5.0stable
Higher Viability
Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs
Score 7.0up
Higher Viability
FedZMG: Efficient Client-Side Optimization in Federated Learning
Score 7.0up

Startup potential card

Startup potential card preview
Share on XLinkedIn

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Recommended Stack

PyTorchML Framework
FastAPIBackend
TensorFlowML Framework
JAXML Framework
KerasML Framework

Startup Essentials

Banana.dev

GPU Inference

Hugging Face Hub

ML Model Hub

Modal

Serverless GPU

Replicate

Run ML Models

Render

Deploy Backend

Railway

Full-Stack Deploy

Supabase

Backend & Auth

Vercel

Deploy Frontend

Estimated $9K - $13K over 6-10 weeks.

MVP Investment

$9K - $13K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

0.5-1x

3yr ROI

6-15x

GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Talent Scout

Find Builders

Neural experts on LinkedIn & GitHub

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.