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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/komdec/detectsleepnet

공식 코드

An Interpretable and Efficient Sleep Staging Algorithm: DetectsleepNet

https://github.com/konyul/FG-PFE

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Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection

https://github.com/koumajos/isp-forecasting-benchmark

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Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting

https://github.com/kristian-georgiev/privacy-induces-robustness

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Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation

https://github.com/ksnxr/rlaf

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Riemannian Laplace Approximation with the Fisher Metric

https://github.com/kth-rpl/dufomap

공식 코드

DUFOMap: Efficient Dynamic Awareness Mapping

https://github.com/kth-rpl/dynamicmap_benchmark

공식 코드

DUFOMap: Efficient Dynamic Awareness Mapping

https://github.com/kuangdai/sofa

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Deep Learning Evidence for Global Optimality of Gerver's Sofa

https://github.com/kuleshov-group/mdlm

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Partition Generative Modeling: Masked Modeling Without Masks

https://github.com/kulikovv/harmonic

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Instance Segmentation of Biological Images Using Harmonic Embeddings

https://github.com/kz29/-psiDAG

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$ψ$DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning

https://github.com/kztakemoto/mmllm

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Large-scale moral machine experiment on large language models

https://github.com/l-i-m-i-t/latentneuraloperator

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Latent Neural Operator for Solving Forward and Inverse PDE Problems

https://github.com/l-kid/video-object-detection-by-location-anticipation

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Objects do not disappear: Video object detection by single-frame object location anticipation

https://github.com/l-wd/lgmformer

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Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation

https://github.com/l1997i/rapid_seg

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RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation

https://github.com/labshuhanggu/lrti-vsr

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Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution

https://github.com/laixinn/ICWSM25-Influence-Maximization

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Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach

https://github.com/laiyao1/arithmetictree

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Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs

https://github.com/lambert-x/medical_mae

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Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification

https://github.com/leaplabthu/adafocusv2

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AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

https://github.com/leaplabthu/agent-attention

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Agent Attention: On the Integration of Softmax and Linear Attention

https://github.com/leaplabthu/uni-adafocus

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Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition

https://github.com/learning4optimization-hust/dualopt

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DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem

https://github.com/lee-va/ebs-cfl

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EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

https://github.com/leeguandong/MVNet-for-HSI

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MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture

https://github.com/leo4life2/algoml-final

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BERT-LSH: Reducing Absolute Compute For Attention

https://github.com/leonty1/deepldnn

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Efficient State Space Model via Fast Tensor Convolution and Block Diagonalization

https://github.com/leonty1/essm

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Efficient State Space Model via Fast Tensor Convolution and Block Diagonalization

https://github.com/lfranke/trips

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TRIPS: Trilinear Point Splatting for Real-Time Radiance Field Rendering

https://github.com/lianbobo/se-rbfnet

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Sparse Ellipsoidal Radial Basis Function Network for Point Cloud Surface Representation

https://github.com/liangchunyaobing/rcm-airl

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A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards

https://github.com/lihaoy-ux/dqo-map

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DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting

https://github.com/lihy0529/lossless_homomorphic_compression

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Accelerating Distributed Deep Learning using Lossless Homomorphic Compression

https://github.com/lilac-lee/fastkernelsf

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Fast Kernel Scene Flow

https://github.com/lileitech/awesome-cardiac-digital-twins

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Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey

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