mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-07-17 09:57:24 -05:00
3b70488856
Conducted multi-perspective review (6 reviewers: undergrad student, CS professor, industry engineer, PhD student, learning scientist, program chair). Implemented all high-priority improvements: 1. Added 'Is TinyTorch Right for You?' self-assessment (Section 1.1) - When to use vs not use TinyTorch - Clear positioning (after CS231n, before advanced systems) - Time commitment transparency (60-80 hours) - Target audience specification 2. Added 3 concrete course integration models (Section 3.5) - Model 1: Standalone 4-credit course (14 weeks) - Model 2: Half-semester in existing ML course (7 weeks) - Model 3: Optional deep-dive track (self-paced) - Instructor resource needs explicitly stated 3. Sharpened abstract contribution framing - Changed from 'framework' to 'pedagogical patterns' - Emphasized design contribution (not empirical study) - Clarified enables educators + researchers 4. Added 'What's NOT Covered' prominently (Section 6.1) - GPU/CUDA programming explicitly omitted - Distributed training not covered - Production deployment/serving excluded - Advanced systems techniques listed - Clear positioning: foundation, not replacement 5. Verified Adam memory technical precision - All mentions already specify '3x parameter memory' - Distinction from activation memory clear Key reviewer themes addressed: - Positioning ambiguity → Clear when/how to use - GPU omission concerns → Prominently acknowledged upfront - Adoption barriers → 3 concrete integration models - Time investment ROI → Self-assessment + positioning Paper now targets SIGCSE 2026 design track more clearly.
TinyTorch Research Paper
Complete LaTeX source for the TinyTorch research paper.
Files
- paper.tex - Main paper (~12-15 pages, two-column format)
- references.bib - Bibliography (22 references)
- compile_paper.sh - Build script (requires LaTeX installation)
Quick Start: Get PDF
Option 1: Overleaf (Recommended)
- Go to Overleaf.com
- Create free account
- Upload
paper.texandreferences.bib - Click "Recompile"
- Download PDF
Option 2: Local Compilation
./compile_paper.sh
Requires LaTeX installation (MacTeX or BasicTeX).
Paper Details
- Format: Two-column LaTeX (conference-standard)
- Length: ~12-15 pages
- Sections: 7 complete sections
- Tables: 3 (framework comparison, learning objectives, performance benchmarks)
- Code listings: 5 (syntax-highlighted Python examples)
- References: 22 citations
Key Contributions
- Progressive disclosure via monkey-patching - Novel pedagogical pattern
- Systems-first curriculum design - Memory/FLOPs from Module 01
- Historical milestone validation - 70 years of ML as learning modules
- Constructionist framework building - Students build complete ML system
Framed as design contribution with empirical validation planned for Fall 2025.
Submission Venues
- ArXiv - Immediate (establish priority)
- SIGCSE 2026 - August deadline (may need 6-page condensed version)
- ICER 2026 - After classroom data (full empirical study)
Ready for submission! Upload to Overleaf to get your PDF.