REPOGEO REPORT · LITE
yfzhang114/Generalization-Causality
Default branch main · commit b1af04ea · scanned 6/26/2026, 8:53:09 PM
GitHub: 1,240 stars · 103 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface yfzhang114/Generalization-Causality, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition README's opening to clarify its nature as a research resource
Why:
CURRENTThis is a repository for organizing articles related to Domain generalization, OOD, optimization, data-centric learning, prompt learning, robutness, and causality. Most papers are linked to **my reading notes**.
COPY-PASTE FIXThis repository serves as a curated collection of my personal reading notes and organized articles on key research topics including Domain Generalization, Out-of-Distribution (OOD) learning, Causality, Robustness, Prompt Learning, Optimization, and Generative Models. It is designed as a resource for researchers to explore and understand the latest advancements in these fields, with most papers linked to detailed notes.
- highabout#2Add a homepage URL to the repository's 'About' section
Why:
COPY-PASTE FIXhttps://yfzhang114.github.io/
- mediumtopics#3Refine topics to include 'literature-review' and 'research-notes'
Why:
CURRENTadaptation, causality, deep-learning, generative-model, machine-learning, optimization, robustness
COPY-PASTE FIXadaptation, causality, deep-learning, generative-model, machine-learning, optimization, robustness, literature-review, research-notes, survey
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- ADDA · recommended 1×
- DANN · recommended 1×
- CDAN · recommended 1×
- PyTorch · recommended 1×
- TensorFlow · recommended 1×
- CATEGORY QUERYHow to improve deep learning model performance when data distribution shifts between training and testing?you: not recommendedAI recommended (in order):
- ADDA
- DANN
- CDAN
- PyTorch
- TensorFlow
- Keras
- ImageNet
- BERT
- RoBERTa
- Hugging Face Transformers
- GPT
- Albumentations
- imgaug
- Keras ImageDataGenerator
- CutMix
- Mixup
- Augly (Facebook AI)
- Scikit-learn
- VotingClassifier
- StackingClassifier
- StackingRegressor
- Foolbox
- Advertorch
- Group DRO
- FixMatch
- UDA
- Mean Teacher
- Pandas
- Canny
- GLCM
- librosa
- OpenCV
AI recommended 32 alternatives but never named yfzhang114/Generalization-Causality. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking resources to understand causality and robustness for out-of-distribution machine learning models.you: not recommendedAI recommended (in order):
- Elements of Causal Inference: Foundations and Learning Algorithms
- Causal Inference in Statistics: A Primer
- Invariant Risk Minimization (IRM)
- Domain-Adversarial Training of Neural Networks (DANN)
- Distributionally Robust Optimization (DRO)
- PC algorithm
- FCI algorithm
- Causal-learn (cai-lab/Causal-learn)
- NeurIPS Workshop on Causal Inference and Machine Learning
- ICML Workshop on Causality in Machine Learning
AI recommended 10 alternatives but never named yfzhang114/Generalization-Causality. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of yfzhang114/Generalization-Causality?passAI did not name yfzhang114/Generalization-Causality — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts yfzhang114/Generalization-Causality in production, what risks or prerequisites should they evaluate first?passAI named yfzhang114/Generalization-Causality explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo yfzhang114/Generalization-Causality solve, and who is the primary audience?passAI did not name yfzhang114/Generalization-Causality — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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yfzhang114/Generalization-Causality — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite