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47 changes: 25 additions & 22 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -29,32 +29,35 @@ We will review submissions on a rolling basis.

## Leaderboard (sorted by overlap)

## Leaderboard (sorted by overlap)

| Rank | Name | Overlap | Wirelength (um) | Runtime (s) | Notes |
|------|-----------------|-------------|-----------------|-------------|----------------------|
| 1 | Brayden Rudisill | 0.0000 | 0.2611 | 50.51 | Timed on a mac air |
| 2 | manuhalapeth | 0.0000 | 0.2630 | 196.8 | |
| 3 | Neil Teje | 0.0000 | 0.2700 | 24.00s | |
| 4 | Leison Gao | 0.0000 | 0.2796 | 50.14s | |
| 5 | William Pan | 0.0000 | 0.2848 | 155.33s | |
| 6 | Ashmit Dutta | 0.0000 | 0.2870 | 995.58 | Spent my entire morning (12 am - 6 am) doing this :P |
| 7 | Pawan Paleja | 0.0000 | 0.3311 | 1.74s | Implemented hint for loss func, cosine annealing on learning rate with warmup, std annealing on lambda weight. Used optuna to tune hyperparam. Tested on gh codespaces 2-core. |
8 | Shashank Shriram | 0.0000 | 0.3312 | 11.32 | 🏎️💥 |
| 9 | Gabriel Del Monte | 0.0000 | 0.3427 | 606.07 | |
| 10 | Aleksey Valouev| 0.0000 | 0.3577 | 118.98 | |
| 11 | Mohul Shukla | 0.0000 | 0.5048 | 54.60s | |
| 12 | Ryan Hulke | 0.0000 | 0.5226 | 166.24 | |
| 13 | Neel Shah | 0.0000 | 0.5445 | 45.40 | Zero overlaps on all tests, adaptive schedule + early stop |
| 14 | Nawel Asgar | 0.0000 | 0.5675 | 81.49 | Adaptive penalty scaling with cubic gradients and design-size optimization
| 15 | Shiva Baghel | 0.0000 | 0.5885 | 491.00 | Stable zero-overlap with balanced optimization |
| 16 | Vansh Jain | 0.0000 | 0.9352 | 86.36 | |
| 17 | Akash Pai | 0.0006 | 0.4933 | 326.25s | |
| 18 | Zade Mahayni | 0.00665 | 0.5157 | 127.4 | Will try again tomorrow |
| 19 | Nithin Yanna | 0.0148 | 0.5034 | 247.30s | aggressive overlap penalty with quadratic scaling |
| 20 | Sean Ko | 0.0271 | .5138 | 31.83s | lr increase, decrease epoch, increase lambda overlap and decreased lambda wire_length + log penalty loss |
| 21 | Keya Gohil | 0.0155 | 0.4678 | 1513.07 | Still working |
| 22 | Prithvi Seran | 0.0499 | 0.4890 | 398.58 | |
| 23 | partcl example | 0.8 | 0.4 | 5 | example |
| 24 | Add Yours! | | | | |
| 3 | Richard Wang | 0.0000 | 0.2666 | 124.86 | |
| 4 | Neil Teje | 0.0000 | 0.2700 | 24.00s | |
| 5 | Leison Gao | 0.0000 | 0.2796 | 50.14s | |
| 6 | William Pan | 0.0000 | 0.2848 | 155.33s | |
| 7 | Ashmit Dutta | 0.0000 | 0.2870 | 995.58 | Spent my entire morning (12 am - 6 am) doing this :P |
| 8 | Pawan Paleja | 0.0000 | 0.3311 | 1.74s | Implemented hint for loss func, cosine annealing on learning rate with warmup, std annealing on lambda weight. Used optuna to tune hyperparam. Tested on gh codespaces 2-core. |
| 9 | Shashank Shriram | 0.0000 | 0.3312 | 11.32 | 🏎️💥 |
| 10 | Gabriel Del Monte | 0.0000 | 0.3427 | 606.07 | |
| 11 | Aleksey Valouev| 0.0000 | 0.3577 | 118.98 | |
| 12 | Mohul Shukla | 0.0000 | 0.5048 | 54.60s | |
| 13 | Ryan Hulke | 0.0000 | 0.5226 | 166.24 | |
| 14 | Neel Shah | 0.0000 | 0.5445 | 45.40 | Zero overlaps on all tests, adaptive schedule + early stop |
| 15 | Nawel Asgar | 0.0000 | 0.5675 | 81.49 | Adaptive penalty scaling with cubic gradients and design-size optimization
| 16 | Shiva Baghel | 0.0000 | 0.5885 | 491.00 | Stable zero-overlap with balanced optimization |
| 17 | Vansh Jain | 0.0000 | 0.9352 | 86.36 | |
| 18 | Akash Pai | 0.0006 | 0.4933 | 326.25s | |
| 19 | Zade Mahayni | 0.00665 | 0.5157 | 127.4 | Will try again tomorrow |
| 20 | Nithin Yanna | 0.0148 | 0.5034 | 247.30s | aggressive overlap penalty with quadratic scaling |
| 21 | Sean Ko | 0.0271 | .5138 | 31.83s | lr increase, decrease epoch, increase lambda overlap and decreased lambda wire_length + log penalty loss |
| 22 | Keya Gohil | 0.0155 | 0.4678 | 1513.07 | Still working |
| 23 | Prithvi Seran | 0.0499 | 0.4890 | 398.58 | |
| 24 | partcl example | 0.8 | 0.4 | 5 | example |
| 25 | Add Yours! | | | | |

> **To add your results:**
> Insert a new row in the table above with your name, overlap, wirelength, and any notes. Ensure you sort by overlap.
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