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Stephen Fox

sfoxBrown
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Quazim0t0's profile picture PhysiQuanty's profile picture
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AI & ML interests

Timeseries machine learning, Deep Learning in Bioinformatics, and World Models

Recent Activity

reacted to Quazim0t0's post with 🔥 24 days ago
🧩 Verified neural units, now for memory & storage Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain — N/N), I applied the same discipline to memory and storage. Three new repos: 🔷 neural-ddr — verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM — flip a bit in every stored byte, ECC corrects all of them. 🤗 https://huggingface.co/Quazim0t0/neural-ddr · 💻 https://github.com/quzi93/neural-ddr 🗄️ neural-storage — a self-healing vault on a neural-verified GF(2⁸) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive → self-healing .pt imager. 🤗 https://huggingface.co/Quazim0t0/neural-storage · 💻 https://github.com/quzi93/neural-storage 💿 neural-cd-preserve — scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong. 🤗 https://huggingface.co/Quazim0t0/neural-cd-preserve · 💻 https://github.com/quzi93/neural-cd-preserve Build your own: golden finite function → enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) → train a small MLP → verify must be bit-exact on 100% of inputs or it's rejected → compose. Every repo ships the training + exhaustive-verification scripts. Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults — none of it pretends to beat entropy. Runs on modest/older hardware. 🤗
reacted to Quazim0t0's post with ❤️ 24 days ago
🧩 Verified neural units, now for memory & storage Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain — N/N), I applied the same discipline to memory and storage. Three new repos: 🔷 neural-ddr — verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM — flip a bit in every stored byte, ECC corrects all of them. 🤗 https://huggingface.co/Quazim0t0/neural-ddr · 💻 https://github.com/quzi93/neural-ddr 🗄️ neural-storage — a self-healing vault on a neural-verified GF(2⁸) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive → self-healing .pt imager. 🤗 https://huggingface.co/Quazim0t0/neural-storage · 💻 https://github.com/quzi93/neural-storage 💿 neural-cd-preserve — scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong. 🤗 https://huggingface.co/Quazim0t0/neural-cd-preserve · 💻 https://github.com/quzi93/neural-cd-preserve Build your own: golden finite function → enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) → train a small MLP → verify must be bit-exact on 100% of inputs or it's rejected → compose. Every repo ships the training + exhaustive-verification scripts. Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults — none of it pretends to beat entropy. Runs on modest/older hardware. 🤗
upvoted a paper 25 days ago
ResNet strikes back: An improved training procedure in timm
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