An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
Quantization.IEEE Transactions on Information Theory, 44(6):2325–2383
8 Pith papers cite this work, alongside 1,596 external citations. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
Quantized reasoning models produce longer chains of thought, inflating token usage and negating per-token speedups from low-bit quantization across multiple benchmarks.
NSVQ mitigates codebook collapse in large-codebook VQ by addressing encoder drift via non-stationary loss, replacement, and staged freezing, improving rFID from 2.39 to 2.10 on ImageNet-1k while achieving 100% utilization.
BMRUs enable analog recurrent neural network hardware via discrete outputs that suppress noise 20-fold, with one-to-one parameter-to-circuit mapping and linear power scaling for recurrence.
CMRU restores gradient flow in BMRU via cumulative state updates with skip-connections through time, yielding better convergence and benchmark performance while retaining quantized persistent memory.
A pipeline using product quantization and systematic parameter evaluation creates data-driven soil taxonomies with higher specificity than human-derived classifications.
Dask parallelization of product quantization and inverted indexing allows large-scale approximate nearest neighbor search while preserving accuracy and reducing computation to medium-scale levels.
citing papers explorer
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Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
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Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
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Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models
Quantized reasoning models produce longer chains of thought, inflating token usage and negating per-token speedups from low-bit quantization across multiple benchmarks.
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NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization
NSVQ mitigates codebook collapse in large-codebook VQ by addressing encoder drift via non-stationary loss, replacement, and staged freezing, improving rFID from 2.39 to 2.10 on ImageNet-1k while achieving 100% utilization.
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Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
BMRUs enable analog recurrent neural network hardware via discrete outputs that suppress noise 20-fold, with one-to-one parameter-to-circuit mapping and linear power scaling for recurrence.
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Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
CMRU restores gradient flow in BMRU via cumulative state updates with skip-connections through time, yielding better convergence and benchmark performance while retaining quantized persistent memory.
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Product Quantization for Surface Soil Similarity
A pipeline using product quantization and systematic parameter evaluation creates data-driven soil taxonomies with higher specificity than human-derived classifications.
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Large-Scale Data Parallelization of Product Quantization and Inverted Indexing Using Dask
Dask parallelization of product quantization and inverted indexing allows large-scale approximate nearest neighbor search while preserving accuracy and reducing computation to medium-scale levels.