![[D] icml, no rebuttal ack so far..](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-circuit-gold-PMJWD5qsqGfXwX8w9a97Cb.webp)
[D] icml, no rebuttal ack so far..
Almost all the papers I reviewed have received at least one ack, but I haven’t gotten a single rebuttal acknowledgment yet. Is there anyone else who hasn’t received theirs? submitted by /u/tuejan11 [link] [comments]
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reviewpaper![[D] The memory chip market lost tens of billions over a paper this community would have understood in 10 minutes](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-quantum-N2hdoEfCm2gAozJVRfL5wL.webp)
[D] The memory chip market lost tens of billions over a paper this community would have understood in 10 minutes
TurboQuant was teased recently and tens of billions gone from memory chip market in 48 hours but anyone in this community who read the paper would have seen the problem with the panic immediately. TurboQuant compresses the KV cache down to 3 bits per value from the standard 16 using polar coordinate quantization. But the KV cache is inference memory. Training memory, activations, gradients, optimizer states, is a completely different thing and completely untouched. And majority of HBM demand comes from training. An inference compression paper doesn't move that number. And the commercial inference baseline already runs at 4 to 8 bit precision. The 6x headline is benchmarked against 16 bit full precision. The real marginal gain over what's actually deployed is considerably smaller than that
![[D] Is research in semantic segmentation saturated?](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-microchip-RD7Ub6Tkp8JwbZxSThJdV5.webp)
[D] Is research in semantic segmentation saturated?
Nowadays I dont see a lot of papers addressing 2D semantic segmentation problem statements be it supervised, semi-supervised, domain adaptation. Is the problem statement saturated? Are there any promising research directions in segmentation except open-set segmentation? submitted by /u/Hot_Version_6403 [link] [comments]
Fine-tuned Gemma 4 E4B for structured JSON extraction from regulatory docs - 75% to 94% accuracy, notebook + 432 examples included
Gemma 4 dropped this week so I fine-tuned E4B for a specific task: extracting structured JSON (doc type, obligations, key fields) from technical and regulatory documents. https://preview.redd.it/v7yg80prpetg1.png?width=1026 format=png auto=webp s=517fb50868405f90a94f60b54b04608bcedd2ced Results on held-out test set: - doc_type accuracy: 75% base → 94% fine-tuned - Hallucinated obligations: 1.25/doc → 0.59/doc - JSON validity: 100% - Field coverage: 100% Setup: - QLoRA 4-bit, LoRA r=16 alpha=16, Unsloth + TRL - 432 training examples across 8 doc types - 5 epochs on a single L4, ~10 min training time - Final train loss 1.04, eval loss 1.12 The whole thing is open: notebook, dataset, serve.py for FastAPI inference. https://github.com/spriyads-vault/gemma4-docparse Some things I learned the ha
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[D] Is research in semantic segmentation saturated?
Nowadays I dont see a lot of papers addressing 2D semantic segmentation problem statements be it supervised, semi-supervised, domain adaptation. Is the problem statement saturated? Are there any promising research directions in segmentation except open-set segmentation? submitted by /u/Hot_Version_6403 [link] [comments]
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