Tag: paper review
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GraphSAGE vs GAT: Reddit/PPI Inductive Learning 95% F1
Compare GraphSAGE and GAT for inductive learning on Reddit and PPI datasets. Learn which model achieves 95% F1 and why architecture matters.
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EfficientNetV2 vs ResNet: 11x Faster Training Explained
EfficientNetV2 trains 11x faster than ResNet through progressive learning, Fused-MBConv blocks, and adaptive regularization strategies.
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SAM vs Grounded-SAM: Zero-Shot Segmentation mIoU Gap
SAM hits 46.5 mIoU on COCO but drops to 28-32 on custom objects. Grounded-SAM adds text prompts at 180ms latency. Real benchmark data inside.
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DETR vs Faster R-CNN: End-to-End Detection Hits 42 AP
Compare DETR vs Faster R-CNN object detection: how Transformers eliminate anchors and NMS to match 42 AP while simplifying the detection pipeline.
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YOLO-World vs Grounding DINO: Zero-Shot Detection Wins
Compare YOLO-World vs Grounding DINO for zero-shot detection. See which model wins on speed, accuracy, and real-world deployment scenarios.
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FlashAttention-2 vs xFormers: H100 Cost at 100M Tokens
Compare FlashAttention-2 vs xFormers on H100 GPUs for 100M token training. Discover which framework cuts costs and boosts speed for LLM workloads.
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QLoRA Review: 4-Bit Finetuning for 65B Models on One GPU
QLoRA fits a 65B model on one 48GB GPU with zero quality loss. Here's exactly how NF4 quantization, double quant, and LoRA combine โ with code.
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Chinchilla Review: Why Fewer Parameters Beat Gopher
Chinchilla's 70B model beat 280B Gopher using 4x fewer parameters. The secret: training tokens matter more than model size. Proof inside.