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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/thu-bpm/markllm

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An Unforgeable Publicly Verifiable Watermark for Large Language Models

https://github.com/thu-bpm/unforgeable_watermark

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An Unforgeable Publicly Verifiable Watermark for Large Language Models

https://github.com/thu-bpm/waterseeker

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WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents

https://github.com/thunguyen177/finnet

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FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural Network

https://github.com/thunlp/compression-plugin

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Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules

https://github.com/thunlp/subchartokenization

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Sub-Character Tokenization for Chinese Pretrained Language Models

https://github.com/thuwuyinjun/meta_sample_selections

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Learning to Select Pivotal Samples for Meta Re-weighting

https://github.com/tiago939/dp_monte_carlo

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A differentiable programming framework for spin models

https://github.com/tianrluo/AutoDiffPulses

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Joint Design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI

https://github.com/time-moe/time-moe

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Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

https://github.com/tims2d/spdnet

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SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting

https://github.com/tinh2044/YOLO12-UnderWater

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Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation

https://github.com/tizzzzy/demonstration_selection_overview

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Comparative Analysis of Demonstration Selection Algorithms for LLM In-Context Learning

https://github.com/tksaha/con-s2v

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Dis-S2V: Discourse Informed Sen2Vec

https://github.com/tlartigue/scalable-regularised-joint-mixture-models

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Scalable Regularised Joint Mixture Models

https://github.com/tmlr-group/fabflex

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Fast and Accurate Blind Flexible Docking

https://github.com/tongnie/nexusqn

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Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale

https://github.com/torchmd/torchmd-net

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TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

https://github.com/tpfi22/spectral-and-combinatorial

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Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings

https://github.com/tpys/fuxi

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FuXi Weather: A data-to-forecast machine learning system for global weather

https://github.com/transferwise/shap-select

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Shap-Select: Lightweight Feature Selection Using SHAP Values and Regression

https://github.com/troubadour000/amd

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Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting

https://github.com/trunpm/pmt-aaai23

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Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer

https://github.com/truongkhang/topicfm

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TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching

https://github.com/tsourolampis/Adagio

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ADAGIO: Fast Data-aware Near-Isometric Linear Embeddings

https://github.com/ttchengab/MetaSampler

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Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds

https://github.com/ttttttris/bebert

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BEBERT: Efficient and Robust Binary Ensemble BERT

https://github.com/tudelft-iv/voteflow

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VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow

https://github.com/tudelft/faith

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FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flow

https://github.com/tue-mps/cts-segmenter

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Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers

https://github.com/tueimage/gabmil

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A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology

https://github.com/tupyp7180/cft-rag-2025

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CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter

https://github.com/tusharsangam/transvisdrone

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TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos

https://github.com/tustailab/hmpnet

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HMPNet: A Feature Aggregation Architecture for Maritime Object Detection from a Shipborne Perspective

https://github.com/twke18/cast

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Learning Hierarchical Image Segmentation For Recognition and By Recognition

https://github.com/uclaml/vacdb

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Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits

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