https://github.com/lucaballotta/processingnetworks-rl
공식 코드To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing
Papers with Code 아카이브
Data Compression
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/luk4s-b/efficient-compression
공식 코드https://github.com/lywang3081/MRDC
공식 코드https://github.com/magics-lab/dnabert_2
공식 코드DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
https://github.com/mandt-lab/RD-sandwich
공식 코드Towards Empirical Sandwich Bounds on the Rate-Distortion Function
https://github.com/mandt-lab/empirical-RD-sandwich
공식 코드Towards Empirical Sandwich Bounds on the Rate-Distortion Function
https://github.com/mcGill-NLP/medal
공식 코드https://github.com/michael-psenka/manifold-linearization
공식 코드Representation Learning via Manifold Flattening and Reconstruction
https://github.com/milkthink-lab/minilongbench
공식 코드MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models
https://github.com/mlgroupjlu/matrixnuclearnorm
공식 코드https://github.com/mohit1997/DeepZip
공식 코드DeepZip: Lossless Data Compression using Recurrent Neural Networks
https://github.com/multimedialabsfu/learned-point-cloud-compression-for-classification
공식 코드https://github.com/nicholasjohnson2020/discretecompressedsensing.jl
공식 코드https://github.com/nicholasjohnson2020/sparselowranksoftware
공식 코드Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach
https://github.com/nnaisense/bayesian-flow-networks
공식 코드https://github.com/noelkelias/multitok
공식 코드MultiTok: Variable-Length Tokenization for Efficient LLMs Adapted from LZW Compression
https://github.com/nrel/phase-space-sampling
공식 코드Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows
https://github.com/nus-hpc-ai-lab/dd-ranking
공식 코드DD-Ranking: Rethinking the Evaluation of Dataset Distillation
https://github.com/palombom/sirenmri
공식 코드https://github.com/pku-alignment/llms-resist-alignment
공식 코드Language Models Resist Alignment: Evidence From Data Compression
https://github.com/qi-yangsjtu/ggsc
공식 코드A Benchmark for Gaussian Splatting Compression and Quality Assessment Study
https://github.com/qi-yangsjtu/hybridgs
공식 코드https://github.com/qiongwu86/blockchain-enabled-variational-information-bottleneck-for-iot-networks
공식 코드Blockchain-Enabled Variational Information Bottleneck for IoT Networks
https://github.com/rezafuru/frankensplit
공식 코드https://github.com/richealyoung/implicitneuralcompression
공식 코드SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data
https://github.com/richealyoung/tinc
공식 코드https://github.com/ryoungj/mcbits
공식 코드Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
https://github.com/shubhamchandak94/LFZip
공식 코드LFZip: Lossy compression of multivariate floating-point time series data via improved prediction
https://github.com/sotakudo/fvib
공식 코드Flexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training
https://github.com/sqflow/sqflow
공식 코드Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement
https://github.com/stefanradev93/BayesFlow
공식 코드BayesFlow: Amortized Bayesian Workflows With Neural Networks
https://github.com/sumn2u/neuralnetwork-jpeg
공식 코드https://github.com/swathi-shree-narashiman/alphazip
공식 코드https://github.com/taaha-khan/2023-compact-image-compression
공식 코드https://github.com/tailintalent/distillation
공식 코드Pareto-optimal data compression for binary classification tasks
https://github.com/timxzz/hit
공식 코드Trading Information between Latents in Hierarchical Variational Autoencoders
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.