https://github.com/gmum/b-lora-xs
공식 코드Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA
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/goldoak/gsfusion
공식 코드GSFusion: Online RGB-D Mapping Where Gaussian Splatting Meets TSDF Fusion
https://github.com/gon-uri/detach_rocket
공식 코드https://github.com/goodmanpzh/route-to-reason
공식 코드Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection
https://github.com/google-research/big_vision
공식 코드https://github.com/google-research/google-research
공식 코드Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
https://github.com/google-research/url-nlp
공식 코드Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold
https://github.com/gradientinstitute/fastfair
공식 코드Fast Fair Regression via Efficient Approximations of Mutual Information
https://github.com/graph-com/ed-hnn
공식 코드https://github.com/graph-com/pvgalm
공식 코드Privately Learning from Graphs with Applications in Fine-tuning Large Language Models
https://github.com/graph4sec-team/pathmlp
공식 코드https://github.com/graphcore-research/jax-scalify
공식 코드Scalify: scale propagation for efficient low-precision LLM training
https://github.com/graphpku/refined-gae
공식 코드https://github.com/gregorbachmann/scaling_mlps
공식 코드https://github.com/gsarti/ETC-NLG
공식 코드ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation
https://github.com/gterty019/mipens
공식 코드https://github.com/gtfactslab/immrax
공식 코드https://github.com/gudovskiy/e2x
공식 코드Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
https://github.com/gumityolcu/dualview
공식 코드https://github.com/gumityolcu/dualview_data_attribution_influence_estimation
공식 코드https://github.com/gustmd0121/time_is_not_enough
공식 코드Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Models
https://github.com/gwenzhang/voxel-mamba
공식 코드Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection
https://github.com/gwgundersen/rflvm
공식 코드https://github.com/gzerveas/mvts_transformer
공식 코드A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/habicrowd/HabiCrowd
공식 코드HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation
https://github.com/hadiiiil/gnns-breastcompression
공식 코드Graph Neural Networks for modelling breast biomechanical compression
https://github.com/hadinemati70/Hnemati
공식 코드Flexible Robust Optimal Bidding of Renewable Virtual Power Plants in Sequential Markets
https://github.com/haeyun-choi/DeepDeblurRF
공식 코드https://github.com/haiduo/KernelDNA
공식 코드KernelDNA: Dynamic Kernel Sharing via Decoupled Naive Adapters
https://github.com/halimabouzidi/hadas
공식 코드HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling
https://github.com/halongbao/lrrnet
공식 코드https://github.com/haniehmsv/LowOrderFlowUQ
공식 코드https://github.com/hannespetrenz/ralmpc_linear_uncertain_systems
공식 코드Robust MPC for Uncertain Linear Systems -- Combining Model Adaptation and Iterative Learning
https://github.com/hanyas/mimo
공식 코드A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning
https://github.com/hanyusun/opr-ltr
공식 코드https://github.com/hanzhizhang-ulrica/dam
공식 코드DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.