Hierarchical Concept-based Interpretable Models | ScienceToStartup | ScienceToStartup

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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

Founder's Pitch

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

Interpretable AI•Score: 5•View PDF ↗

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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

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Hierarchical Concept-based Interpretable Models

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References (43)

Founder's Pitch

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

Commercial Viability Breakdown

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Why It Matters

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Hierarchical Concept-based Interpretable Models

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References (43)

Founder's Pitch

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

Commercial Viability Breakdown

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