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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/dphuongn/FedDLP

공식 코드

Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational Efficiency

https://github.com/dpiras/cosmopower-jax

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CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators

https://github.com/dqzhang17/clcnet.torch

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clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions

https://github.com/dragonlee258079/DMT

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Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection

https://github.com/dreamy-xay/tablecenternet

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TableCenterNet: A one-stage network for table structure recognition

https://github.com/drosemei/rome

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RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation

https://github.com/drscotthawley/signaltrain

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Exploring Quality and Generalizability in Parameterized Neural Audio Effects

https://github.com/ds3lab/corgipile-postgresql

공식 코드

Stochastic Gradient Descent without Full Data Shuffle

https://github.com/ds4dh/GLiNER-biomed

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GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition

https://github.com/dslaborg/germeval2024

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Detecting Sexism in German Online Newspaper Comments with Open-Source Text Embeddings (Team GDA, GermEval2024 Shared Task 1: GerMS-Detect, Subtasks 1 and 2, Closed Track)

https://github.com/dslisleedh/ESC

공식 코드

Emulating Self-attention with Convolution for Efficient Image Super-Resolution

https://github.com/dslisleedh/IGConv

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Implicit Grid Convolution for Multi-Scale Image Super-Resolution

https://github.com/dslisleedh/PLKSR

공식 코드

Partial Large Kernel CNNs for Efficient Super-Resolution

https://github.com/duanlab123/mgmc

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Fast Multi-grid Methods for Minimizing Curvature Energy

https://github.com/duo-yu/neural-gode

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Neural Generalized Ordinary Differential Equations with Layer-varying Parameters

https://github.com/dyahayumgw/HAAQI-Net

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HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

https://github.com/dyu62/PolyGNN

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PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph

https://github.com/eBay/maxDNN

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maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

https://github.com/eashanadhikarla/expomamba

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ExpoMamba: Exploiting Frequency SSM Blocks for Efficient and Effective Image Enhancement

https://github.com/eatbeanss/CSWin-UNet

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CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation

https://github.com/ebekkers/ponita

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Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space

https://github.com/ebilionis/variational-reformulation-of-inverse-problems

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Variational Reformulation of Bayesian Inverse Problems

https://github.com/ecnuacrush/hyatt-net

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HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection

https://github.com/ed-fish/Geo-Sign

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Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation

https://github.com/efm95/dream

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Modeling non-linear Effects with Neural Networks in Relational Event Models

https://github.com/ehsanhaghighat/stonet

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STONet: A novel neural operator for modeling solute transport in micro-cracked reservoirs

https://github.com/eidoslab/towards-efficient-capsule-networks

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Towards Efficient Capsule Networks

https://github.com/eitanspi/weather-image-classification

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Real-Time Weather Image Classification with SVM

https://github.com/ejhigson/bsr

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Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning

https://github.com/ejokhan/transnas_tsad

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TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection

https://github.com/ejwww/tangnn

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TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning

https://github.com/elasalle/pasco

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PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms

https://github.com/eleurent/highway-env

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Robust Predictable Control

https://github.com/elfsong/mercury

공식 코드

Mercury: A Code Efficiency Benchmark for Code Large Language Models

https://github.com/elkabzo/cont_disc_opt_dnn

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Continuous vs. Discrete Optimization of Deep Neural Networks

https://github.com/elki-project/elki

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Accelerating Spherical k-Means

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