RRepoGEO

REPOGEO REPORT · LITE

MingSun-Tse/Efficient-Deep-Learning

Default branch master · commit 51c0fec3 · scanned 6/6/2026, 2:47:37 PM

GitHub: 954 stars · 132 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

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 MingSun-Tse/Efficient-Deep-Learning, 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.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README H1 and opening paragraph to clarify repo's nature

    Why:

    CURRENT
    # EfficientDNNs

A collection of recent methods on DNN compression and acceleration.
    COPY-PASTE FIX
    # EfficientDNNs: A Curated Collection of Research Papers on DNN Compression and Acceleration

This repository provides a comprehensive, curated collection of recent research papers and methods focused on deep neural network (DNN) compression and acceleration.
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/MingSun-Tse/Efficient-Deep-Learning
  • lowtopics#3
    Add topics to explicitly categorize the repo as a collection of papers

    Why:

    CURRENT
    deep-learning, deep-neural-networks, efficient-deep-learning, knowledge-distillation, model-compression, network-pruning
    COPY-PASTE FIX
    deep-learning, deep-neural-networks, efficient-deep-learning, knowledge-distillation, model-compression, network-pruning, awesome-list, paper-collection, research-papers

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.

Recall
0 / 2
0% of queries surface MingSun-Tse/Efficient-Deep-Learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 5 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 5×
  2. tensorflow/tensorflow · recommended 4×
  3. microsoft/onnxruntime · recommended 2×
  4. tensorflow/model-optimization · recommended 2×
  5. NVIDIA/apex · recommended 2×
  • CATEGORY QUERY
    How can I reduce the computational cost and memory footprint of deep neural networks?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Quantization (pytorch/pytorch)
    2. TensorFlow Lite (TFLite) Converter (tensorflow/tensorflow)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. PyTorch Pruning (pytorch/pytorch)
    5. TensorFlow Model Optimization Toolkit (Pruning API) (tensorflow/model-optimization)
    6. NVIDIA Apex (NVIDIA/apex)
    7. Hugging Face Transformers (Trainer API) (huggingface/transformers)
    8. TensorFlow/Keras (tensorflow/tensorflow)
    9. PyTorch (pytorch/pytorch)
    10. MobileNet
    11. EfficientNet
    12. ShuffleNet
    13. OpenVINO Toolkit (Intel) (openvinotoolkit/openvino)
    14. NVIDIA TensorRT (NVIDIA/TensorRT)
    15. TVM (Apache TVM) (apache/tvm)

    AI recommended 15 alternatives but never named MingSun-Tse/Efficient-Deep-Learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective techniques for compressing neural networks like pruning and quantization?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    2. PyTorch's `torch.nn.utils.prune` (pytorch/pytorch)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. NVIDIA's Apex (NVIDIA/apex)
    5. AutoML for Keras
    6. TensorFlow Lite Converter (tensorflow/tensorflow)
    7. ONNX Runtime (microsoft/onnxruntime)
    8. PyTorch's `torch.quantization` (pytorch/pytorch)
    9. NVIDIA's TensorRT
    10. TensorFlow Lite (tensorflow/tensorflow)

    AI recommended 10 alternatives but never named MingSun-Tse/Efficient-Deep-Learning. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

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 MingSun-Tse/Efficient-Deep-Learning?
    pass
    AI named MingSun-Tse/Efficient-Deep-Learning explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts MingSun-Tse/Efficient-Deep-Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MingSun-Tse/Efficient-Deep-Learning 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 MingSun-Tse/Efficient-Deep-Learning solve, and who is the primary audience?
    pass
    AI did not name MingSun-Tse/Efficient-Deep-Learning — 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?

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MingSun-Tse/Efficient-Deep-Learning — 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