https://github.com/microsoft/t-mac
공식 코드T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
Papers with Code 아카이브
Computational Efficiency
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Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/tora
공식 코드ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
https://github.com/microsoft/vert-papers/tree/master/papers/GRN-NER
공식 코드GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
https://github.com/mikecovlee/DynMoLE
공식 코드DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing Mechanism
https://github.com/mikelzc1990/nsganetv2
공식 코드Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification
https://github.com/mila-iqia/babyai
공식 코드https://github.com/mililab/roma
공식 코드RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
https://github.com/mingr-ma/rfr-wwanet
공식 코드https://github.com/mingyue-cheng/hmf
공식 코드https://github.com/mint-vu/linearized_partial_gromov_wasserstein
공식 코드https://github.com/mir-group/allegro
공식 코드High-performance training and inference for deep equivariant interatomic potentials
https://github.com/mir-group/nequip
공식 코드High-performance training and inference for deep equivariant interatomic potentials
https://github.com/mirthAI/CIS-UNet
공식 코드https://github.com/mirthai/csa-net
공식 코드A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
https://github.com/mixxxxx358/model-augmentation-public
공식 코드https://github.com/mjq11302010044/RRPN
공식 코드Arbitrary-Oriented Scene Text Detection via Rotation Proposals
https://github.com/mkjia/beautymap
공식 코드BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps
https://github.com/mlglobalhealth/pi-vae
공식 코드$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
https://github.com/mlgroupjlu/matrixnuclearnorm
공식 코드https://github.com/mlvlab/mctf
공식 코드Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers
https://github.com/mlvlab/vid-tldr
공식 코드vid-TLDR: Training Free Token merging for Light-weight Video Transformer
https://github.com/mnovak42/leuven
공식 코드Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems
https://github.com/mobarakol/ST-MTL
공식 코드https://github.com/modelscope/data-juicer
공식 코드MindGYM: Enhancing Vision-Language Models via Synthetic Self-Challenging Questions
https://github.com/modeltc/llmc
공식 코드LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
https://github.com/modestyachts/ARS
공식 코드Simple random search provides a competitive approach to reinforcement learning
https://github.com/modichirag/atlassampler
공식 코드ATLAS: Adapting Trajectory Lengths and Step-Size for Hamiltonian Monte Carlo
https://github.com/moe-inf/awesome-moe-inference
공식 코드A Survey on Inference Optimization Techniques for Mixture of Experts Models
https://github.com/moepien/sliced_ot_for_gmms
공식 코드https://github.com/moffran/calibrated_explanations
공식 코드https://github.com/mosaf/res-srdiff
공식 코드https://github.com/mouthful/ClofNet
공식 코드SE(3) Equivariant Graph Neural Networks with Complete Local Frames
https://github.com/mrflogs/LoRA-Pro
공식 코드https://github.com/mseg-dataset/mseg-semantic
공식 코드MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
https://github.com/mtanveer1/GBTSVM
공식 코드https://github.com/mtanveer1/mvtpmsvm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.