TARQ applies a closed-form per-layer rule called rareBAL to equalize calibration mass between common and tail words in ASR quantization, improving rare-WER across models and datasets without aggregate WER regression.
Qtip: Quantiza- tion with trellises and incoherence processing
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
XFP introduces quality-targeted adaptive codebook quantization with sparse outlier separation that auto-selects parameters from cosine similarity floors, achieving high throughput and accuracy on Qwen3.5 models at low effective bits without calibration data.
With known covariance, waterfilling improves GPTQ and WaterSIC reaches within 0.25 bit/entry of the rate-distortion limit while being basis-independent.
citing papers explorer
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TARQ: Tail-Aware Reconstruction Quantization for Rare-Word Robust Automatic Speech Recognition
TARQ applies a closed-form per-layer rule called rareBAL to equalize calibration mass between common and tail words in ASR quantization, improving rare-WER across models and datasets without aggregate WER regression.
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XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference
XFP introduces quality-targeted adaptive codebook quantization with sparse outlier separation that auto-selects parameters from cosine similarity floors, achieving high throughput and accuracy on Qwen3.5 models at low effective bits without calibration data.
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High-Rate Quantized Matrix Multiplication II
With known covariance, waterfilling improves GPTQ and WaterSIC reaches within 0.25 bit/entry of the rate-distortion limit while being basis-independent.