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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://github.com/Sohl-Dickstein/Sum-of-Functions-Optimizer

공식 코드

Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods

https://github.com/Sompote/YOLO9tr

공식 코드

YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention Mechanism

https://github.com/StanfordASL/MATS

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MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control

https://github.com/StanfordBDHG/HealthBench

공식 코드

Medicine on the Edge: Comparative Performance Analysis of On-Device LLMs for Clinical Reasoning

https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization

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End-to-end reconstruction meets data-driven regularization for inverse problems

https://github.com/THU-BPM/private_watermark

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An Unforgeable Publicly Verifiable Watermark for Large Language Models

https://github.com/TakuyaHiraoka/Dropout-Q-Functions-for-Doubly-Efficient-Reinforcement-Learning

공식 코드

Dropout Q-Functions for Doubly Efficient Reinforcement Learning

https://github.com/TalSchuster/CATs

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Consistent Accelerated Inference via Confident Adaptive Transformers

https://github.com/Talented-Q/MSMAE

공식 코드

Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification

https://github.com/TengjieZheng/rgpssm

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Recursive Gaussian Process State Space Model

https://github.com/Tinker-Twins/Robust_Behavioral_Cloning

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Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning

https://github.com/TomWaka/CovariateDependentStacking

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Ensemble Prediction via Covariate-dependent Stacking

https://github.com/TomasVaskevicius/implicit_sparsity_neurips2019

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Implicit Regularization for Optimal Sparse Recovery

https://github.com/Tombs98/ALGNet

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Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring

https://github.com/VDIGPKU/DynamicDet

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DynamicDet: A Unified Dynamic Architecture for Object Detection

https://github.com/VITA-Group/VideoLifter

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VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment

https://github.com/VhaoYan/PDE-constrained-DKL

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PDE-DKL: PDE-constrained deep kernel learning in high dimensionality

https://github.com/VideoNetworks/TokShift-Transformer

공식 코드

Token Shift Transformer for Video Classification

https://github.com/Wangya1996/Two-way-Node-Popularity-Model

공식 코드

Two-way Node Popularity Model for Directed and Bipartite Networks

https://github.com/Wei9711/NegGCNs

공식 코드

Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes

https://github.com/WesternDundrey/KFAC_DEEPHEDGE

공식 코드

A New Way: Kronecker-Factored Approximate Curvature Deep Hedging and its Benefits

https://github.com/WilliamLiPro/LpSS

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Training Sparse Neural Network by Constraining Synaptic Weight on Unit Lp Sphere

https://github.com/WuDH2000/CrossMamba

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Cross-attention Inspired Selective State Space Models for Target Sound Extraction

https://github.com/XiangLiu0731/MFGNet

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Multi-Features Guidance Network for partial-to-partial point cloud registration

https://github.com/XuezheMax/macow

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MaCow: Masked Convolutional Generative Flow

https://github.com/YanfangLiu11/Conditional-Diffusion-Model-for-SDE-Learning

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A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical Systems

https://github.com/YasserElj/Synthetic-Granular-Gen

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Instant Particle Size Distribution Measurement Using CNNs Trained on Synthetic Data

https://github.com/YhuoyuH/RetinexMamba

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Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement

https://github.com/Yinghao-Li/UQAC

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Language Model Uncertainty Quantification with Attention Chain

https://github.com/Yqcca/CMLoRA

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Cached Multi-Lora Composition for Multi-Concept Image Generation

https://github.com/Yu-Group/adaptive-wavelet-distillation

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Adaptive wavelet distillation from neural networks through interpretations

https://github.com/Yu-Group/adaptive-wavelets

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Adaptive wavelet distillation from neural networks through interpretations

https://github.com/Yubo02/Sketch-and-Lift-Scalable-Subsampled-Semidefinite-Program-for-K-means-Clustering

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Sketch-and-Lift: Scalable Subsampled Semidefinite Program for $K$-means Clustering

https://github.com/Yui010206/CREMA

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CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular Fusion

https://github.com/Yusufguvenx/Adapting-GT2-FLS-for-Uncertainty-Quantification-A-Blueprint-Calibration-Strategy

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Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy

https://github.com/Yuto-Matsunaga/Prompt_Tuning_for_Audio_Deepfake_Detection

공식 코드

Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset

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