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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://gitee.com/BioMechLab/l-sahgnet

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Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embedding

https://github.com/109502518/psc_bigdata

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Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering

https://github.com/2471023025/ralm_survey

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RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing

https://github.com/2760613195/km_unet

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KM-UNet KAN Mamba UNet for medical image segmentation

https://github.com/3riccc/neural_pruning

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Neural Network Pruning by Gradient Descent

https://github.com/594422814/CF-VGG

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Real-Time Correlation Tracking via Joint Model Compression and Transfer

https://github.com/ACAT-SCUT/PETformer

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PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer

https://github.com/ACEsuit/BIPs.jl

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BIP: Boost Invariant Polynomials for Efficient Jet Tagging

https://github.com/AI-in-Cardiovascular-Medicine/nnUZoo

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From Claims to Evidence: A Unified Framework and Critical Analysis of CNN vs. Transformer vs. Mamba in Medical Image Segmentation

https://github.com/AI2ES/miles-guess

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Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

https://github.com/AIDC-rPPG/RF-Net

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RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex Scenarios

https://github.com/AIH-SGML/mixmil

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Mixed Models with Multiple Instance Learning

https://github.com/AaltoML/kalman-jax

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State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

https://github.com/AaltoML/t-SVGP

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Dual Parameterization of Sparse Variational Gaussian Processes

https://github.com/AdaCompNUS/GAMMA

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GAMMA: A General Agent Motion Model for Autonomous Driving

https://github.com/AdaCompNUS/hyp-despot

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HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty

https://github.com/AdaCompNUS/magic

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MAGIC: Learning Macro-Actions for Online POMDP Planning

https://github.com/Alibaba-MIIL/CobBO

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CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage Kernels

https://github.com/AnTao97/LGM

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Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network

https://github.com/Andy97/DeepMLS

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Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

https://github.com/Anonymous6369/SkipSNN

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SkipSNN: Efficiently Classifying Spike Trains with Event-attention

https://github.com/Aradhye2002/selective-peft-toolkit

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Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models

https://github.com/AtlasAnalyticsLab/AdaFisher

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AdaFisher: Adaptive Second Order Optimization via Fisher Information

https://github.com/AutodeskAILab/UVStyle-Net

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UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps

https://github.com/Axect/Neural_Hamilton

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Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?

https://github.com/B-Deprez/GARG-AML

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GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering

https://github.com/BUAABIGSCity/PDFormer

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PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

https://github.com/BWLONG/BeyondAttentiveTokens

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Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers

https://github.com/BaowenZ/RaDe-GS

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RaDe-GS: Rasterizing Depth in Gaussian Splatting

https://github.com/Bearisbug/Bi-Elman

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Dynamic Bi-Elman Attention Networks: A Dual-Directional Context-Aware Test-Time Learning for Text Classification

https://github.com/Binwen6/CS182_PROJECT_2025

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Probing In-Context Learning: Impact of Task Complexity and Model Architecture on Generalization and Efficiency

https://github.com/BlinkDL/RWKV-LM

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RWKV: Reinventing RNNs for the Transformer Era

https://github.com/BouchardLab/DynamicalComponentsAnalysis

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Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis

https://github.com/Brain-Cog-Lab/IEMF

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Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence

https://github.com/CN-TU/adversarial-recurrent-ids

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SparseIDS: Learning Packet Sampling with Reinforcement Learning

https://github.com/CQUlearningsystemgroup/BitwiseBottleneck

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Neural Network Activation Quantization with Bitwise Information Bottlenecks

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