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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/elman295/cssm

공식 코드

Change State Space Models for Remote Sensing Change Detection

https://github.com/em-llm/EM-LLM-model

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Human-like Episodic Memory for Infinite Context LLMs

https://github.com/emadeldeen24/tslanet

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TSLANet: Rethinking Transformers for Time Series Representation Learning

https://github.com/emi-group/evomo

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GPU-accelerated Evolutionary Many-objective Optimization Using Tensorized NSGA-III

https://github.com/emi-group/gpbt-pl

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Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement Learning

https://github.com/emi-group/metade

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MetaDE: Evolving Differential Evolution by Differential Evolution

https://github.com/emi-group/tensorneat

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Tensorized NeuroEvolution of Augmenting Topologies for GPU Acceleration

https://github.com/emmajreid/MDF

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Multi-Resolution Data Fusion for Super Resolution Imaging

https://github.com/emorymlip/cot-flow

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Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

https://github.com/emorymlip/pcp-map

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Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

https://github.com/ennengyang/representationsurgery

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Representation Surgery for Multi-Task Model Merging

https://github.com/ensemble-art/Dynamic-Logistic-Ensembles

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Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification

https://github.com/enuzhin/ur

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Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problems

https://github.com/enyac-group/quamba

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Quamba: A Post-Training Quantization Recipe for Selective State Space Models

https://github.com/epfml/X2Static

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Obtaining Better Static Word Embeddings Using Contextual Embedding Models

https://github.com/eqtpartners/ptec

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Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation

https://github.com/ercobian/adaann-vi-nf

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AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximation

https://github.com/eric-tramel/SwAMP-Demo

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Sparse Estimation with the Swept Approximated Message-Passing Algorithm

https://github.com/esl-epfl/streaminnc

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Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs

https://github.com/esrschao/edgeregnet

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EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds

https://github.com/event-ahu/openevdet

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Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset

https://github.com/eydcao/bsms-gnn

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Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN

https://github.com/eydcao/vicon

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VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction

https://github.com/eyeline-research/survey-video-diffusion

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Survey of Video Diffusion Models: Foundations, Implementations, and Applications

https://github.com/fabecode/GraphML-FHE

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Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering

https://github.com/fangzhuoyi/xlstm-vmunet

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When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation

https://github.com/fdjingliu/dmad

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A Survey on Diffusion Models for Anomaly Detection

https://github.com/felixyu7/om2vec

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Learning Efficient Representations of Neutrino Telescope Events

https://github.com/feng-li/dlsa

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Least Squares Approximation for a Distributed System

https://github.com/fenghetan9/hi-end-mae

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Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

https://github.com/fengzhang427/LLF-LUT

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Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping

https://github.com/fengzicai/awesomewmad

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A Survey of World Models for Autonomous Driving

https://github.com/fengzicai/wmad-benchmarks

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A Survey of World Models for Autonomous Driving

https://github.com/ffancheng/paper-mlann

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Computationally Efficient Learning of Statistical Manifolds

https://github.com/ffyyytt/tsm-mmvpr

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Action Recognition Using Temporal Shift Module and Ensemble Learning

https://github.com/ffyyytt/vruid-aaai-dakiet

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Hierarchical Document Parsing via Large Margin Feature Matching and Heuristics

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