Paper Citation Record · LEDGER

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models

As of 11 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2603.08173.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2603.08173 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

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measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T18:35:48.848722Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

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unresolved57
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External citation measurements

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Outbound references

Observation 9feaedd3-7a58-4f7d-8420-4fc21866379b · outbound

This paper cites Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:48.848722Z

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Observation d5dcb6d3-1b35-41fe-b29d-cd6a5f655201 · outbound

This paper cites Quantization Quantization enables deployment of neural networks in resource-constrained settings by reducing memory and compu- tational requirements [5].

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Quantization Quantization enables deployment of neural networks in resource-constrained settings by reducing memory and compu- tational requirements [5]

Reference 2

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Observation 4731f9c0-d2d4-4659-990c-6fc0b16f4057 · outbound

This paper cites an unresolved cited work.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Unresolved cited work

Reference 3

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Observation 657bdea9-f668-4ddf-b9ba-238df4f85e0d · outbound

This paper cites MSE(sN) MSE(s2) MSE(s1) Output: ŷ s1, s2,…, sN Parameters Objective function 𝓔(y,ŷ) CMA-ES.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models MSE(sN) MSE(s2) MSE(s1) Output: ŷ s1, s2,…, sN Parameters Objective function 𝓔(y,ŷ) CMA-ES

Reference 4

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Observation 1d6ded52-ec1a-45b5-ba34-0fc65c698a78 · outbound

This paper cites First, each layer-wise activation scaling factor is locally optimized by min- imizing the MSE between the FP32 and quantized layer outputs.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models First, each layer-wise activation scaling factor is locally optimized by min- imizing the MSE between the FP32 and quantized layer outputs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:49.432310Z

Source-reported events for the cited work

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Observation e40190e1-7f22-4878-884d-6d70f94ef083 · outbound

This paper cites an unresolved cited work.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Unresolved cited work

Reference 6

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Observation cade12a6-e144-4aff-a94f-a3e2aac0a295 · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 7

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Source-reported events for the cited work

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Observation a1cbf680-81b0-4ea6-ac21-4ff052ef7a9f · outbound

This paper cites Our study highlights that, unlike in vision or NLP, audio models are particularly sensitive to activation quan- tization, providing strong motivation for our approach.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Our study highlights that, unlike in vision or NLP, audio models are particularly sensitive to activation quan- tization, providing strong motivation for our approach

Reference 8

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Source-reported events for the cited work

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Observation 87660877-f178-4801-904d-9c55edb40692 · outbound

This paper cites Robust speech recognition via large-scale weak supervision,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Robust speech recognition via large-scale weak supervision,

Reference 9

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Observation a0e855e8-99dd-40de-9763-79b802444c77 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:50.270699Z

Source-reported events for the cited work

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Observation bfd8d8b4-0d06-45a6-8bf3-3feaa79919e9 · outbound

This paper cites Conformer: Convolution-augmented Transformer for Speech Recognition.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Conformer: Convolution-augmented Transformer for Speech Recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:50.419650Z

Source-reported events for the cited work

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Observation 9b53fdc8-ce7d-4013-8433-d8ec055fe305 · outbound

This paper cites AST: Audio Spectrogram Transformer.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models AST: Audio Spectrogram Transformer

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:50.568237Z

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Observation a3eccf1f-c30e-4128-a5f4-2a3659e5b6db · outbound

This paper cites Model Compression and Efficient Inference for Large Language Models: A Survey.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Model Compression and Efficient Inference for Large Language Models: A Survey

Reference 13

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Observation e58b30f6-bffa-40d2-bed2-e157a5d3167d · outbound

This paper cites Up or down? adaptive rounding for post-training quantization,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Up or down? adaptive rounding for post-training quantization,

Reference 14

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Observation abbd0a67-6932-4018-a9cf-c2ebd6be24ad · outbound

This paper cites Whisper-kdq: A lightweight whisper via guided knowledge dis- tillation and quantization for efficient asr,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Whisper-kdq: A lightweight whisper via guided knowledge dis- tillation and quantization for efficient asr,

Reference 15

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Observation e7f0a706-7d1e-47cd-8214-d5cae633651e · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Smoothquant: Accurate and efficient post-training quantization for large language models,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:51.392923Z

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Observation 35231719-3eb9-443d-8a17-60577d2bf000 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 17

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Observation 89dc3a56-2a50-44f8-a0cf-4f27dce61e0f · outbound

This paper cites Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:51.825052Z

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Observation 6e6c3700-f386-4b40-be90-0d514395735b · outbound

This paper cites BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing

Reference 19

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Source-reported events for the cited work

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Observation cba13595-e9da-4985-9e93-c534abbaf99b · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 20

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Source-reported events for the cited work

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Observation bfbfc3ed-a734-464b-b5df-9fa9b0dac4a1 · outbound

This paper cites Outlier Reduction with Gated Attention for Improved Post-training Quantization in Large Sequence-to-sequence Speech Foundation Models.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Outlier Reduction with Gated Attention for Improved Post-training Quantization in Large Sequence-to-sequence Speech Foundation Models

Reference 21

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Source-reported events for the cited work

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Observation c8943af6-f2b8-4051-ab82-08df00cd71e3 · outbound

This paper cites Ultra-low bit post-training quantization of large speech models via k-means clustering and mixed precision allocation,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Ultra-low bit post-training quantization of large speech models via k-means clustering and mixed precision allocation,

Reference 22

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Observation c411d9c7-e474-4894-88d6-5abe734e2f44 · outbound

This paper cites A survey of quantization methods for efficient neural network inference,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models A survey of quantization methods for efficient neural network inference,

Reference 23

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Observation d6e86e5e-6e7f-4f64-ad5e-cec2906fe788 · outbound

This paper cites Deep residual learning for image recognition,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Deep residual learning for image recognition,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:52.739320Z

Source-reported events for the cited work

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Observation 9cfdb54b-96ee-4eaa-8928-a8af1c69ac1d · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language under- standing,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Bert: Pre- training of deep bidirectional transformers for language under- standing,

Reference 25

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Observation e9827d67-dc42-4971-9373-8bf2b734237c · outbound

This paper cites Improving the speed of neural networks on cpus,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Improving the speed of neural networks on cpus,

Reference 26

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Observation ea491315-d5a9-4b25-aae8-ef65d58cfef0 · outbound

This paper cites Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

Reference 27

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Observation 89463f21-4346-4381-9fe8-b3d6c47c31f0 · outbound

This paper cites Nvidia 8-bit inference with tensorrt,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Nvidia 8-bit inference with tensorrt,

Reference 28

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Observation cc470520-017d-4f67-8be3-dc2f4ce3835e · outbound

This paper cites Low-bit quantization of neural networks for efficient inference,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Low-bit quantization of neural networks for efficient inference,

Reference 29

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Observation 3f96582c-db94-43b4-ad9e-1932723cac15 · outbound

This paper cites Evolutionsstrategien,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Evolutionsstrategien,

Reference 30

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Observation d9a5f4f5-a8c6-42e9-9a79-befdc9bee2b5 · outbound

This paper cites Natural evolution strategies,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Natural evolution strategies,

Reference 31

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Observation c5315b06-910d-47ce-8993-2bb3295d73e6 · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 32

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Observation cc4b90c5-c49f-4f5f-a2c6-71dfefa391f9 · outbound

This paper cites Noisyquant: Noisy bias-enhanced post-training activation quan- tization for vision transformers,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Noisyquant: Noisy bias-enhanced post-training activation quan- tization for vision transformers,

Reference 33

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Observation 691dbcc2-e502-4b41-ba59-81c7e686fcd4 · outbound

This paper cites Hyq: Hardware-friendly post- training quantization for cnn-transformer hybrid networks,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Hyq: Hardware-friendly post- training quantization for cnn-transformer hybrid networks,

Reference 34

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Observation 8a764bfd-fc28-4cf0-9266-46b1d73bbe35 · outbound

This paper cites Ditas: Quantizing diffusion trans- formers via enhanced activation smoothing,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Ditas: Quantizing diffusion trans- formers via enhanced activation smoothing,

Reference 35

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Observation 2f71155e-cd7c-4447-94cd-8e15db4d0e29 · outbound

This paper cites Minimize Quantization Output Error with Bias Compensation.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Minimize Quantization Output Error with Bias Compensation

Reference 36

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Observation ec61ca2a-6947-4589-bcdc-18d5780db898 · outbound

This paper cites 2019 Evolutionary Algorithms Review.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models 2019 Evolutionary Algorithms Review

Reference 37

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Observation 09d9bd5f-6bc2-40dd-9284-fb57b88c765f · outbound

This paper cites The cma evolution strategy: a comparing review,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models The cma evolution strategy: a comparing review,

Reference 38

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Observation 224d1e25-3f2c-4f73-8c17-6fb2db2eeb23 · outbound

This paper cites High dimensions and heavy tails for natural evolution strategies,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models High dimensions and heavy tails for natural evolution strategies,

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Observation 96fc2c92-9f77-4d26-9dcd-9571d55d0cc4 · outbound

This paper cites Inves- tigating rnn-based speech enhancement methods for noise-robust text-to-speech,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Inves- tigating rnn-based speech enhancement methods for noise-robust text-to-speech,

Reference 40

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Observation 8d0d5933-a97a-415d-99c4-b9cfd8414a70 · outbound

This paper cites FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

Reference 41

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Observation 5f0c0fdf-6664-47f6-931d-653bcd61dcf8 · outbound

This paper cites GDRQ: Group-based Distribution Reshaping for Quantization.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models GDRQ: Group-based Distribution Reshaping for Quantization

Reference 42

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Observation 0d974da5-5134-4c45-beea-b0bc6353bb91 · outbound

This paper cites Squashed weight distribution for low bit quantization of deep models,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Squashed weight distribution for low bit quantization of deep models,

Reference 43

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This paper cites CMA-ES for Hyperparameter Optimization of Deep Neural Networks.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models CMA-ES for Hyperparameter Optimization of Deep Neural Networks

Reference 44

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Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Unresolved cited work

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Observation 640e59ff-b116-4e46-b9a1-048ed5213fbb · outbound

This paper cites Lib- rispeech: an asr corpus based on public domain audio books,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Lib- rispeech: an asr corpus based on public domain audio books,

Reference 46

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This paper cites ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification

Reference 47

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Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models VoxCeleb2: Deep Speaker Recognition

Reference 48

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Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra

Reference 49

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Source-reported events for the cited work

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This paper cites The lj speech dataset,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models The lj speech dataset,

Reference 51

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This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 52

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Source-reported events for the cited work

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This paper cites pytorch quantization: http://github.com/nvidia/tensorrt.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models pytorch quantization: http://github.com/nvidia/tensorrt

Reference 53

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Source-reported events for the cited work

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Observation e5974c2d-5ff1-45f2-84ee-c12269397da5 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Pytorch: An imperative style, high-performance deep learning library,

Reference 54

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Source-reported events for the cited work

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Observation 6e800ee6-6a9d-4b2a-909f-85f9b878fc2c · outbound

This paper cites TensorRT: https://developer.nvidia.com/tensorrt.

Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models TensorRT: https://developer.nvidia.com/tensorrt

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T18:35:56.478899Z

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Observation fc41297d-7de4-471b-b9ae-f402c4088a3d · outbound

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Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models 11.3 metis aipu: A 12nm 15tops/w 209.6 tops soc for cost-and energy-efficient inference at the edge,

Reference 56

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Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models Survey of machine learning accelerators,

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