ID title ideation
Created: 30 Mar 2023, 11:42 AM | Modified: =dateformat(this.file.mtime,"dd MMM yyyy, hh:mm a")
Tags: knowledge,
“I have a new machine learning interpretability system currently titled “An Interpretability system to Discover and Describe Data Slices where ML models underperform”. The main purpose of this system is to discover subsets of data within the validation dataset where a machine learning model would fail in terms of model prediction. These subsets would then be described semantically so that the machine learning practitioner is able to understand the discovered subset. The unique feature of this system is that it can operate with any machine learning model that has a metric, including deep learning models, and on any type of data as long as it has a joint embedding space with natural language.
You are a machine learning researcher that has published many papers to top conferences, and have obtained many patents from those papers.
Generate a new catchy title for this system, either as an acronym or as a single word that describes the system as a metaphor / analogy that is easily remembered”
“SLICE” - Semantic Learning Interpretability for Critical Exclusions
“SLICE” - “Semantic Labelling of Inconsistent Classification Examples.”
SLICE: Semantic Learning Interpretability for Critical Errors
SLICE: Semantic Learning for Interpreting Challenging Examples
SLICE: System for Locating Interpretability Challenging Examples
Semantic Learning Interpretability for Complex Evaluations
“SLICE-INSIGHT: Semantic Learning Interpretability for Critical Error Insights”
“SliCE” (Subsets for Classification Evaluation)
“DISCERN” - Discovering Interpretable Subsets of Data to Enhance Robustness and Narration”
DissectML
DissectML suggests that your system can break down the inner workings of any ML model, identifying the specific slices of data where the model fails. Plus, the word “dissect” itself implies a deep dive and close examination, which fits nicely with the idea of interpretability.
“Lighthouse”, which evokes the idea of shining a light on areas of the data where the model needs improvement.
“SherlockML,” invoking the idea of a detective who investigates data to uncover hidden clues about model performance. Whatever title you choose, make sure it’s easy to remember and effectively conveys the system’s purpose and unique features.
: Discovering the Blind Spots of ML Models
: A Semantic Interpretability System for ML Failure Detection
: Protecting Your ML Models with Semantic Error Insights
: Navigating the Multiverse of ML Model Interpretability
: Unlocking the Secrets of Model Failure with Semantic Slicing
: Delving into the Inner Workings of ML Models with Semantic Slices
: An Interpretability System for Pinpointing Critical Model Errors
: A Scalable System for Exploring Model Blind Spots with Semantics
: Revealing the Hidden Patterns of Model Failure with Semantic Slices
: Semantic Insights from Model Underperformance
: Revealing the Blind Spots of Machine Learning Models
: An Interpretability System for Discovering and Describing Model Weaknesses
From <https://chat.openai.com/chat/1b8242e9-9972-49f6-8013-04fe0527674f>
I have a new machine learning interpretability system currently titled “A Data-centric Interpretability system to Discover and Describe Data Slices where ML models underperform”. The main purpose of this system is to discover subsets of data within the validation dataset where a machine learning model would fail in terms of model prediction. These subsets would then be described semantically so that the machine learning practitioner is able to understand the discovered subset. The unique feature of this system is that it can operate with any machine learning model that has a metric, including deep learning models, and on any type of data as long as it has a joint embedding space with natural language.
You are a machine learning researcher that has published many papers to top conferences, and have obtained many patents from those papers.
Generate a list of catchy titles for this system which uses the acronym “SLICE” that describes the system. The title can contain prefixes and suffixes, but must be an acronym like “xx-SLICE” or “SLICE-xx”, where “xx” does not mean limited to 2 characters. Ensure the following words are included in the title, where it is written in the form of boolean logic: “semantic” and (“cluster” or “subset”) and “model”
Generate a list of catchy titles for this system in the form of “
SLICE-MP: System for Locating Interpretable Clusters of Errors in Model Predictions
Slice - surgeon slicing, pizza slice, beef slice
Dissect
M U S I
System Discovering Model
“Sherlock: An Interpretability system to Discover and Describe Data Slices where ML models underperform”
Sherlock: A model interpretability system to Discover and Describe underperforming slices of data
Lighthouse: Illuminating …
Lighthouse: Shedding light on …
Sherlock: Uncovering …
Sherlock: Discovering …
Sherlock: Revealing …
… semantically meaningful data slices where ML models underperform
… semantic slices of data where ML models underperform
AI for data understanding
- AI for Discovering Model Failures
- AI for Slice-based Model Evaluation
- AI for Semantic Error Discovery
- AI for Model Failure Analysis
AI for …