Edge-GNN: Constraint-aware graph neural networks for biological interaction modeling under edge deployment constraints (computational oncology, PPI networks).
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Updated
Mar 24, 2026 - Python
Edge-GNN: Constraint-aware graph neural networks for biological interaction modeling under edge deployment constraints (computational oncology, PPI networks).
Neural network built from scratch in NumPy to classify data-center workloads based on thermal load indicators (CPU utilization, runtime), using Google cluster traces.
Runtime-verified fidelity for long-context LLM serving: detect, label-free, when sparse-attention KV compression silently degrades output, and bound it with anytime-valid confidence sequences. Custom HF attention backend + elastic probe scheduler. H1/H4 confirmed at 7B across Qwen2.5 & Mistral on 2x H100 · 88 tests · pre-registered hypotheses.
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