View PDF ↗
PDF Viewer
Open Full PDF

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 (43)

[1]
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
2025Mateo Espinosa Zarlenga, Gabriele Dominici et al.
[2]
BatchTopK Sparse Autoencoders
2024Bart Bussmann, Patrick Leask et al.
[3]
Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery
2024Sukrut Rao, S. Mahajan et al.
[4]
Stochastic Concept Bottleneck Models
2024Moritz Vandenhirtz, Sonia Laguna et al.
[5]
Let Go of Your Labels with Unsupervised Transfer
2024Artyom Gadetsky, Yulun Jiang et al.
[6]
Understanding Inter-Concept Relationships in Concept-Based Models
2024Naveen Raman, Mateo Espinosa Zarlenga et al.
[7]
Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
2024Gabriele Dominici, Pietro Barbiero et al.
[8]
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
2024Nishad Singhi, Jae Myung Kim et al.
[9]
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
2024Xinyue Xu, Yi Qin et al.
[10]
DISCOVER: Making Vision Networks Interpretable via Competition and Dissection
2023Konstantinos P. Panousis, S. Chatzis
[11]
Coarse-to-Fine Concept Bottleneck Models
2023Konstantinos P. Panousis, Dino Ienco et al.
[12]
Learning to Receive Help: Intervention-Aware Concept Embedding Models
2023Mateo Espinosa Zarlenga, Katherine M. Collins et al.
[13]
Sparse Autoencoders Find Highly Interpretable Features in Language Models
2023Hoagy Cunningham, Aidan Ewart et al.
[14]
Probabilistic Concept Bottleneck Models
2023Eunji Kim, Dahuin Jung et al.
[15]
Disentangling Neuron Representations with Concept Vectors
2023Laura O'Mahony, V. Andrearczyk et al.
[16]
DINOv2: Learning Robust Visual Features without Supervision
2023M. Oquab, Timothée Darcet et al.
[17]
Label-Free Concept Bottleneck Models
2023Tuomas P. Oikarinen, Subhro Das et al.
[18]
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
2023Johanna Vielhaben, Stefan Blücher et al.
[19]
TabCBM: Concept-based Interpretable Neural Networks for Tabular Data
2023Mateo Espinosa Zarlenga, M. E. Nelson et al.
[20]
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification
2022Yue Yang, Artemis Panagopoulou et al.

Showing 20 of 43 references

Founder's Pitch

"Develop a tool using Hierarchical Concept Embedding Models for more interpretable machine learning through automatic concept discovery and intervention."

Interpretable AIScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

3/4 signals

7.5

Series A Potential

1/4 signals

2.5

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: 2/27/2026

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.