You will get a rigorous peer review, comments and suggestions to improve your article.
Top Rated
Project details
Attention to detail is my expertise! I will ensure your manuscript is polished and publication-ready, specifically tailored for Machine Learning and Artificial Intelligence venues. If you choose the advanced service (substantive editing and peer review), you will gain the advantage of having your manuscript critically evaluated before submission. With my background as a peer reviewer for ICML and Artificial Intelligence Review, I will provide expert in-text comments on the technical content, helping to optimize your manuscript and address potential issues that reviewers often flag for rejection. My areas of expertise include machine learning, artificial intelligence, deep learning, privacy in images, and other cutting-edge technologies.
Service Type
Proofread & Edit, Critique, FormattingLanguage
EnglishContent Type
ArticleWhat's included
| Service Tiers |
Starter
$25
|
Standard
$200
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 1 day | 5 days | 5 days |
Number of Revisions | 0 | 1 | 1 |
Number of Words | 200 | 5000 | 5000 |
Grammar & Syntax Review | |||
Fact-Checking | - | - | - |
Content Rewriting | |||
Structural Edit | |||
Print Layout | - | - | - |
Optional add-ons
You can add these on the next page.
Additional 100 Words
(+ 1 Day)
+$50
Fact-Checking
(+ 1 Day)
+$100
15 reviews
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This project doesn't have any reviews.
MR
Mon R.
May 23, 2026
Machine Learning Paper Writeup
Younas is one of the best people I have worked with, he us hardworking and smart.
PD
Priya D.
Apr 29, 2025
Incorporate reviewers' feedback in an ML Paper
FF
Frank F.
Jan 17, 2025
Data access LLM model with high accuracy
The team delivered a basic working RAG system with API integration. Communication was consistent and professional throughout the project. However, there were significant challenges:
The team struggled with validation methodology, often using metrics that measured response format rather than factual accuracy. When concerns were raised about testing methods, they tended to defend limitations rather than implement solutions.
A $1000 fine-tuning phase produced minimal improvements, and the final system didn't achieve the required accuracy levels for production use. While technically competent, the team seemed more focused on explaining why issues couldn't be fixed rather than finding solutions.
Positives:
- Professional communication
- Regular updates
- Basic system functionality
Areas for improvement:
- Testing methodology
- Response to feedback
- Solution-focused approach
- Value delivered for cost
Future clients should ensure very clear agreement upfront on testing methodology and accuracy measurements
The team struggled with validation methodology, often using metrics that measured response format rather than factual accuracy. When concerns were raised about testing methods, they tended to defend limitations rather than implement solutions.
A $1000 fine-tuning phase produced minimal improvements, and the final system didn't achieve the required accuracy levels for production use. While technically competent, the team seemed more focused on explaining why issues couldn't be fixed rather than finding solutions.
Positives:
- Professional communication
- Regular updates
- Basic system functionality
Areas for improvement:
- Testing methodology
- Response to feedback
- Solution-focused approach
- Value delivered for cost
Future clients should ensure very clear agreement upfront on testing methodology and accuracy measurements
PD
Priya D.
Dec 10, 2024
Peer review on machine learning write up
FF
Frank F.
Sep 3, 2024
30 minute consultation
About Younas
ML/CV R&D Engineer | Ph.D. in Machine Learning
100%
Job Success
Tarragona, Spain - 5:35 am local time
R&D Engineering
- End-to-end CV pipelines: semantic/instance segmentation, detection, classification (PyTorch, OpenCV)
- Foundation-model adaptation: DINO/ViT backbones, SAM/SAM3 prompting, LoRA, DPT heads
- Weakly-supervised & pseudo-label systems: CAM seeding, confidence-gated verification, ignore-index/ternary labels, self-training
- Training engineering: loss design (Focal-Tversky, weighted CE), class-imbalance handling, multi-GPU pipelines, checkpoint/resume/early-stopping, OOM-safe execution
- Diagnosis-first debugging: root-cause isolation before patching, one-variable-per-run experimental discipline
Applied ML
- Model design, training, optimization, and evaluation on messy real-world data
- Reproducible experiments, versioned caches, documented and auditable code
- Domain-specific systems (industrial inspection, standards-driven labeling)
Research Delivery (on request)
- Publication-ready manuscripts, methodology design, and result interpretation
- Systematic literature reviews restricted to top venues (CVPR/ICML/NeurIPS-tier)
- Figures, tables, and quantitative visual analytics
Stack: Python, PyTorch, OpenCV, DINO/ViT, SAM/SAM3, DPT, Hugging Face, conda, Linux, multi-GPU CUDA.
I deliver working systems with clean, documented code and hold strict timelines. If you have a hard CV/ML problem, especially one where off-the-shelf models underperform on your data, send the details, and I'll respond with a concrete technical approach.
Steps for completing your project
After purchasing the project, send requirements so Younas can start the project.
Delivery time starts when Younas receives requirements from you.
Younas works on your project following the steps below.
Revisions may occur after the delivery date.
Review the feasibility
If I am sure that it is a best fit for me, I will start working on it.