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Computational Efficiency

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보존된 평가 기록: 1

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/Nkluge-correa/skateboarding-trick-classifier

공식 코드

Development of a skateboarding trick classifier using accelerometry and machine learning

https://github.com/Nouf-Alabbasi/oKUmura_AI_Telecom_challenge

공식 코드

Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards

https://github.com/OPTML-Group/DeepZero

공식 코드

DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training

https://github.com/October2001/ProLong

공식 코드

Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models

https://github.com/OctoberChang/MMD-GAN

공식 코드

MMD GAN: Towards Deeper Understanding of Moment Matching Network

https://github.com/OmarSababha/active_ML

공식 코드

Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation

https://github.com/Oneplus/Tweebank

공식 코드

Parsing Tweets into Universal Dependencies

https://github.com/OptimalPursuit/navigating_uncertainty_in_mpp

공식 코드

AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand Uncertainty

https://github.com/POSTECH-CVLab/FastPointTransformer

공식 코드

Fast Point Transformer

https://github.com/Pachark/SVQ-Forecasting

공식 코드

Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach

https://github.com/PaddlePaddle/ERNIE

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ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training

https://github.com/PaddlePaddle/Paddle

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FlashMask: Efficient and Rich Mask Extension of FlashAttention

https://github.com/PaddlePaddle/PaddleOCR

공식 코드

PP-OCR: A Practical Ultra Lightweight OCR System

https://github.com/Peiying-Yu/Table-Critic

공식 코드

Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning

https://github.com/PlusRoss/Hy-Transformer

공식 코드

Improving Hyper-Relational Knowledge Graph Completion

https://github.com/PolymathicAI/xVal

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xVal: A Continuous Numerical Tokenization for Scientific Language Models

https://github.com/PythonOT/POT

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A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data

https://github.com/Pyxel0524/EDE-LGPR

공식 코드

EDENet: Echo Direction Encoding Network for Place Recognition Based on Ground Penetrating Radar

https://github.com/QData/JEEK

공식 코드

A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

https://github.com/QVPR/Patch-NetVLAD

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Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition

https://github.com/QibangLiu/GINOT

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Geometry-Informed Neural Operator Transformer

https://github.com/QingbeiGuo/SG-CNN

공식 코드

Self-grouping Convolutional Neural Networks

https://github.com/QingyuanWang/tinysaver

공식 코드

Tiny Models are the Computational Saver for Large Models

https://github.com/QiufanLin/CLAP-I

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CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation

https://github.com/REIYANG/HiBCD

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Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution

https://github.com/Retinal-Research/Beyond-MobileNet

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Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification

https://github.com/RobinVogel/Trade-offs-in-Large-Scale-Distributed-Tuplewise-Estimation-and-Learning

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Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning

https://github.com/RockyXu66/GaussianSplattingViewer

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CrowdSplat: Exploring Gaussian Splatting For Crowd Rendering

https://github.com/SEU-WENJIA/ST-SepNet-Lightweight-LLMs-Meet-Adaptive-Hypergraphs

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Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs

https://github.com/STAC-USC/Graph_Learning

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Graph Learning from Data under Structural and Laplacian Constraints

https://github.com/SakanaAI/continuous-thought-machines

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Continuous Thought Machines

https://github.com/SarahQiong/CTDGMR

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Gaussian Mixture Reduction with Composite Transportation Divergence

https://github.com/Saurabhbhati/DASS

공식 코드

DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners

https://github.com/SayeedChowdhury/dct-snn

공식 코드

DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks

https://github.com/Shunli-Wang/TSA-Net

공식 코드

TSA-Net: Tube Self-Attention Network for Action Quality Assessment

https://github.com/SimiaoZuo/Transformer-Hawkes-Process

공식 코드

Transformer Hawkes Process

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.