Papers with Code 아카이브
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/ac-freeman/adder-codec-rs
An Asynchronous Intensity Representation for Framed and Event Video Sources
https://github.com/adielashrov/trust-ai-roma-for-llm
Towards Robust LLMs: an Adversarial Robustness Measurement Framework
https://github.com/ag1988/dss
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
https://github.com/ahban/GMMSP
https://github.com/aibluefisher/graphoptim
A Pose-only Solution to Visual Reconstruction and Navigation
https://github.com/aimagelab/mammoth
https://github.com/aiot-mlsys-lab/famba-v
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/ajfurlong/fortran_dnn_from_tf
Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian Neural Networks
https://github.com/alexey-pronkin/annealed
https://github.com/alexis-jacq/numpy_ARS
Simple random search provides a competitive approach to reinforcement learning
https://github.com/alexriedel1/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/ali-vilab/VGen
https://github.com/ali-vilab/i2vgen-xl
https://github.com/alisha17/benchmarks
https://github.com/allenai/commonsense-kg-completion
Commonsense Knowledge Base Completion with Structural and Semantic Context
https://github.com/amobiny/Fast_CapsNet
https://github.com/andravin/algebranets
https://github.com/andreped/PLS-Net
Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images
https://github.com/anked10/CNNs_tensorflow2_InceptionV3-maderas
https://github.com/anonymous-dense-submission/DensE
https://github.com/antoninschrab/agginc
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
https://github.com/antoninschrab/agginc-paper
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
https://github.com/antoninschrab/mmdagg
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
https://github.com/anzeyimana/deepkin
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/aparecidovieira/Road_extraction
https://github.com/appleparan/mise.py
A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/araffin/sbx
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
https://github.com/areffarhadi/mamba-asr
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/aria-systems-group/formal-deep-kernel-synthesis
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/arobey1/RobustNN
https://github.com/arturjordao/arturjordao
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
https://github.com/arungansi/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/arungansi/EfficientAD-1
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/ashutosh1919/FaceGenerationStyleGAN
https://github.com/asuller/rwkv-musicgenerator
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.