PDF Viewer

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.

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

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

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (55)

[1]
Universal Value-Function Uncertainties
2025Moritz A. Zanger, Max Weltevrede et al.
[2]
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
2025Moritz A. Zanger, P. V. D. Vaart et al.
[3]
Uncertainty Quantification with the Empirical Neural Tangent Kernel
2025Joseph Wilson, Christopher van der Heide et al.
[4]
Epistemic Uncertainty and Observation Noise with the Neural Tangent Kernel
2024Sergio Calvo-Ordoñez, Konstantina Palla et al.
[5]
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
2024Michael Matthews, Michael Beukman et al.
[6]
Tractable Function-Space Variational Inference in Bayesian Neural Networks
2023Tim G. J. Rudner, Zonghao Chen et al.
[7]
Diverse Projection Ensembles for Distributional Reinforcement Learning
2023Moritz A. Zanger, Wendelin Böhmer et al.
[8]
Anti-Exploration by Random Network Distillation
2023Alexander Nikulin, Vladislav Kurenkov et al.
[9]
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
2022Seijin Kobayashi, Pau Vilimelis Aceituno et al.
[10]
Packed-Ensembles for Efficient Uncertainty Estimation
2022Olivier Laurent, Adrien Lafage et al.
[11]
BYOL-Explore: Exploration by Bootstrapped Prediction
2022Z. Guo, S. Thakoor et al.
[12]
Learning Dynamics and Generalization in Reinforcement Learning
2022Clare Lyle, Mark Rowland et al.
[13]
Understanding and Leveraging Overparameterization in Recursive Value Estimation
2022Chenjun Xiao, Bo Dai et al.
[14]
Repulsive Deep Ensembles are Bayesian
2021Francesco D'Angelo, Vincent Fortuin
[15]
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
2021Haque Ishfaq, Qiwen Cui et al.
[16]
What Are Bayesian Neural Network Posteriors Really Like?
2021Pavel Izmailov, S. Vikram et al.
[17]
DEUP: Direct Epistemic Uncertainty Prediction
2021Moksh Jain, S. Lahlou et al.
[18]
Exact Langevin Dynamics with Stochastic Gradients
2021Adrià Garriga-Alonso, Vincent Fortuin
[19]
Analyzing Finite Neural Networks: Can We Trust Neural Tangent Kernel Theory?
2020Mariia Seleznova, Gitta Kutyniok
[20]
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
2020Moloud Abdar, Farhad Pourpanah et al.

Showing 20 of 55 references

Founder's Pitch

"Unify RND with deep ensembles and Bayesian inference for uncertainty quantification."

Uncertainty QuantificationScore: 2View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

0/4 signals

0

Quick Build

0/4 signals

0

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/23/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.