| LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning added by Syntology |
2026-06 (from id) |
xinruihe389-commits/LongSpike/src/dataloaders/audio.py 1631cd936e9b1a90 |
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2026-05 (from id) |
ist-daslab/gptq/quant.py 41c57e55d21c4d96 |
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Apache-2.0 (permissive) |
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2026-02 (from id) |
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Apache-2.0 (permissive) |
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2026-01 (from id) |
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2025-10 (from id) |
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Apache-2.0 (permissive) |
| Layer-Adaptive State Pruning for Deep State Space Models |
5 Nov 2024 |
msgwak/LAST/s5/dataloaders/audio.py 1631cd936e9b1a90 |
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21 Oct 2024 |
iankur/vqllm/vqllm/quantizer/utils.py af4e407a59eea9ab |
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MIT (permissive) |
| SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression |
12 Oct 2024 |
mohammad-mozaffari/slim/slim/sparsegpt.py 50ceff9d34d96d60 |
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MIT (permissive) |
| LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid |
14 Jul 2024 |
LeanModels/LeanQuant/quant.py 41c57e55d21c4d96 |
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26 Jun 2024 |
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GPL-3.0 (copyleft) · pointer only |
| Attention-aware Post-training Quantization without Backpropagation |
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SamsungLabs/BoA/quantizers/boa.py b6a41484b3b9b656 |
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| Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark |
12 Jun 2024 |
unites-lab/moe-quantization/auto_gptq/quantization/quantizer.py 41c57e55d21c4d96 |
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MIT (permissive) |
| P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks |
5 Jun 2024 |
NeuroCompLab-psu/PSpikeSSMs/src/dataloaders/audio.py 1631cd936e9b1a90 |
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MIT (permissive) |
| MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization |
2 Jun 2024 |
AozhongZhang/MagR/quant.py 41c57e55d21c4d96 |
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MIT (permissive) |
| Sparse Expansion and Neuronal Disentanglement |
24 May 2024 |
shavit-lab/sparse-expansion/utils/quant.py 50ceff9d34d96d60 |
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MIT (permissive) |
| FrameQuant: Flexible Low-Bit Quantization for Transformers |
10 Mar 2024 |
vsingh-group/framequant/quant.py ff8ffe5499b035fb |
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| NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes |
28 Feb 2024 |
cidanshi/nitedr-nighttime-image-de-raining/utils_image.py b1c1acd3c89a39f3 |
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| SparseLLM: Towards Global Pruning for Pre-trained Language Models |
28 Feb 2024 |
BaiTheBest/SparseLLM/quant.py 50ceff9d34d96d60 |
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Apache-2.0 (permissive) |
| Adaptive Convolutional Neural Network for Image Super-resolution |
24 Feb 2024 |
hellloxiaotian/adsrnet/HDSRNet/utility.py b1c1acd3c89a39f3 |
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| State Space Models for Event Cameras |
23 Feb 2024 |
lindermanlab/S5/s5/dataloaders/audio.py 1631cd936e9b1a90 |
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MIT (permissive) |
| ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models |
10 Dec 2023 |
hahnyuan/asvd4llm/quantization.py 41c57e55d21c4d96 |
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MIT (permissive) |
| DeltaZip: Efficient Serving of Multiple Full-Model-Tuned LLMs |
8 Dec 2023 |
eth-easl/deltazip/deltazip/core/quant.py 8f8eedd720c09255 |
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Apache-2.0 (permissive) |
| In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models |
10 Nov 2023 |
kclip/icl-equalization/EQ_data.py 7d88083dd6876883 |
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| $Λ$-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI |
23 Oct 2023 |
nishio-laboratory/lambda_split/preliminary/text_to_image_base.py dabcd15634e546ca |
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MIT (permissive) |
| Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors |
4 Oct 2023 |
idoamos/not-from-scratch/src/dataloaders/audio.py 1631cd936e9b1a90 |
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MIT (permissive) |
| PB-LLM: Partially Binarized Large Language Models |
29 Sep 2023 |
hahnyuan/binaryllm/gptq_pb/high_quant.py 50ceff9d34d96d60 |
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MIT (permissive) |
| Differentiable JPEG: The Devil is in the Details |
13 Sep 2023 |
necla-ml/diff-jpeg/diff_jpeg/encode.py 544144a7ead4418d |
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BSD-3-Clause (permissive) |
| Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement |
8 Sep 2023 |
zhihefang/SFGNet/utils.py b1c1acd3c89a39f3 |
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| ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation |
28 Jul 2023 |
lianjiatech/belle/models/gptq/quant.py 50ceff9d34d96d60 |
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Apache-2.0 (permissive) |
| OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models |
4 Jun 2023 |
xvyaward/qeft/qeft/quant.py a166c4b79b21b60a |
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MIT (permissive) |
| AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration |
1 Jun 2023 |
identical code first harvested elsewhere a166c4b79b21b60a |
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| QLoRA: Efficient Finetuning of Quantized LLMs |
23 May 2023 |
shuxueslpi/chatGLM-6B-QLoRA/merge_lora_and_quantize.py 9b9c46fa571c5690 |
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| Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement |
14 Apr 2023 |
langmanbusi/semantic-aware-low-light-image-enhancement/DRBN_SKF/src/utility.py b1c1acd3c89a39f3 |
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MIT (permissive) |
| Vector Quantized Time Series Generation with a Bidirectional Prior Model |
8 Mar 2023 |
ml4its/timevqvae/src/timevqvae/utils/nn.py e323b33b21b740e9 |
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MIT (permissive) |
| ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention |
9 Nov 2022 |
GATECH-EIC/ViTaLiTy/src/quantize.py 3f1de05d36b1a783 |
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| Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned |
23 Aug 2022 |
lyqcom/red30/metrics.py aa9d5683a00262dc |
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Apache-2.0 (permissive) |
| Simplified State Space Layers for Sequence Modeling |
9 Aug 2022 |
leonty1/deepldnn/src/dataloaders/audio.py 1631cd936e9b1a90 |
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Apache-2.0 (permissive) |
| Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training |
13 Jun 2022 |
zavareh1/ClippedQuantFL/model/cifar10DET_quantizedmaxscalar.py 00cb1200d5219763 |
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MIT (permissive) |
| UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes |
20 May 2022 |
google-research/big_vision/big_vision/models/proj/uvim/vit.py 0abb725ab0e6cfb6 |
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| Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo |
9 Feb 2022 |
ipeis/HH-VAEM/src/mutual_information.py 6ad9b288c102f26a |
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MIT (permissive) |
| Unsupervised Degradation Representation Learning for Blind Super-Resolution |
1 Apr 2021 |
LongguangWang/DASR/utility.py b1c1acd3c89a39f3 |
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MIT (permissive) |
| Hierarchical Autoregressive Modeling for Neural Video Compression |
19 Oct 2020 |
buggyyang/hier-video-compression/models.py 771654b864892612 |
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12 May 2020 |
andreasfloros/IRN/utils.py 852a7c9de098c70e |
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MIT (permissive) |
| Learning A Single Network for Scale-Arbitrary Super-Resolution |
8 Apr 2020 |
LongguangWang/ArbSR/utility.py b1c1acd3c89a39f3 |
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MIT (permissive) |
| Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution |
16 Mar 2020 |
guoyongcs/DRN/utility.py b1c1acd3c89a39f3 |
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MIT (permissive) |
| Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution |
26 Feb 2020 |
YapengTian/TDAN-VSR-CVPR-2020/solver.py 91df9c98a6149376 |
unverified |
MIT (permissive) |
| Private Summation in the Multi-Message Shuffle Model |
2020-02 (from id) |
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| Post-Training Piecewise Linear Quantization for Deep Neural Networks |
31 Jan 2020 |
jakc4103/piecewise-quantization/utils/quantize.py ce590a0abac42625 |
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| Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference |
3 Jan 2020 |
Torment123/DFS/models.py 7d546c0310d1c591 |
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| Deformable Non-local Network for Video Super-Resolution |
24 Sep 2019 |
wh1h/DNLN/src/utility.py b1c1acd3c89a39f3 |
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MIT (permissive) |
| Deformable Non-local Network for Video Super-Resolution |
24 Sep 2019 |
wh1h/DNLN/src/trainer.py 91df9c98a6149376 |
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MIT (permissive) |
| Robust Invisible Video Watermarking with Attention |
3 Sep 2019 |
DAI-Lab/RivaGAN/rivagan/rivagan.py 34dad53061b916d4 |
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MIT (permissive) |
| Densely Residual Laplacian Super-Resolution |
28 Jun 2019 |
saeed-anwar/DRLN/TestCode/code/utility.py b1c1acd3c89a39f3 |
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MIT (permissive) |
| Sparsified SGD with Memory |
20 Sep 2018 |
epfml/sparsifiedSGD/qsgd.py 96442fc743ff7faf |
unverified |
Apache-2.0 (permissive) |
| Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation |
20 Sep 2018 |
IliasDG/Demucs_Project/quantize.py 95678855edc371ba |
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| Neural Ordinary Differential Equations |
19 Jun 2018 |
HolmesShuan/OISR-PyTorch/NTIRE2019/OISR/src/utility.py b1c1acd3c89a39f3 |
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BSD-2-Clause (permissive) |
| RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation |
4 Jun 2018 |
lyqcom/rednet30/Initial/metrics.py aa9d5683a00262dc |
unverified |
Apache-2.0 (permissive) |
| Maintaining Natural Image Statistics with the Contextual Loss |
13 Mar 2018 |
subeeshvasu/2018_subeesh_epsr_eccvw/EPSR_testcode/code/utility.py b1c1acd3c89a39f3 |
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MIT (permissive) |
| Deep Back-Projection Networks For Super-Resolution |
7 Mar 2018 |
LEEPEIQIN/EDSR/src/utility.py b1c1acd3c89a39f3 |
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MIT (permissive) |
| signSGD: Compressed Optimisation for Non-Convex Problems |
13 Feb 2018 |
identical code first harvested elsewhere 8c0c528956fb38b9 |
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| Attacking Binarized Neural Networks |
1 Nov 2017 |
AngusG/cleverhans-attacking-bnns/cleverhans_tutorials/resnet_model.py fb9a05da9fb0eb6e |
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| Trained Ternary Quantization |
4 Dec 2016 |
vinsis/ternary-quantization/quantification.py d68f2c13b2387a62 |
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MIT (permissive) |
| QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding |
7 Oct 2016 |
scottjiao/Gradient-Compression-Methods/utils.py 8c0c528956fb38b9 |
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| Neural NILM: Deep Neural Networks Applied to Energy Disaggregation |
23 Jul 2015 |
JackKelly/neuralnilm_prototype/neuralnilm/source.py a4c12d1effb52c55 |
unverified |
MIT (permissive) |
| Going Deeper with Convolutions |
17 Sep 2014 |
HolmesShuan/AIM2020-RealSR/src/utility.py 7185b10fd4807808 |
unverified |
BSD-2-Clause (permissive) |
| Intriguing properties of neural networks |
21 Dec 2013 |
anishathalye/neural-hash-collider/collide.py 9e4646f8887baf9a |
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MIT (permissive) |
| arXiv:openreview_d3RFDLBw01 |
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AI2C-Lab/STLA/quantizer.py 41c57e55d21c4d96 |
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Apache-2.0 (permissive) |
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wangh-allen/LAU-Net/utility.py b1c1acd3c89a39f3 |
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| arXiv:2025.findings-emnlp.1054 |
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IST-DASLab/sparsegpt/quant.py 50ceff9d34d96d60 |
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Apache-2.0 (permissive) |
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LianjiaTech/BELLE/models/gptq/quant.py 50ceff9d34d96d60 |
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Apache-2.0 (permissive) |
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SamsungLabs/Z-Fold/quant.py 41c57e55d21c4d96 |
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MIT (permissive) |