Papers with Code 아카이브

Inference Optimization

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/NetEase-FuXi/EET

공식 코드

Easy and Efficient Transformer : Scalable Inference Solution For large NLP model

https://github.com/amazon-science/llm-rank-pruning

공식 코드

LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models

https://github.com/bcmi/phdiffusion-painterly-image-harmonization

공식 코드

Painterly Image Harmonization using Diffusion Model

https://github.com/codelion/optillm

공식 코드

Patched MOA: optimizing inference for diverse software development tasks

https://github.com/codelion/optillm/blob/main/optillm/moa.py

공식 코드

Patched MOA: optimizing inference for diverse software development tasks

https://github.com/fedeloper/CycleBNN

공식 코드

CycleBNN: Cyclic Precision Training in Binary Neural Networks

https://github.com/fedeloper/binary_deepfake_detection

공식 코드

CycleBNN: Cyclic Precision Training in Binary Neural Networks

https://github.com/git-disl/eenet

공식 코드

Adaptive Deep Neural Network Inference Optimization with EENet

https://github.com/intel/xfastertransformer

공식 코드

Inference Performance Optimization for Large Language Models on CPUs

https://github.com/joelouismarino/amortized-variational-filtering

공식 코드

A General Method for Amortizing Variational Filtering

https://github.com/joelouismarino/iterative_inference

공식 코드

Iterative Amortized Inference

https://github.com/josefhoppe/cell-flower

공식 코드

Representing Edge Flows on Graphs via Sparse Cell Complexes

https://github.com/josefhoppe/edge-flow-cell-complexes

공식 코드

Representing Edge Flows on Graphs via Sparse Cell Complexes

https://github.com/locuslab/icnn

공식 코드

Input Convex Neural Networks

https://github.com/microsoft/vidur

공식 코드

Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints

https://github.com/mjhydri/1d-statespace

공식 코드

A Novel 1D State Space for Efficient Music Rhythmic Analysis

https://github.com/moe-inf/awesome-moe-inference

공식 코드

A Survey on Inference Optimization Techniques for Mixture of Experts Models

https://github.com/mzeegers/ADJUST

공식 코드

ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography

https://github.com/remigenet/TLN

공식 코드

A Temporal Linear Network for Time Series Forecasting

https://github.com/sohampoddar26/llm-brevity

공식 코드

Brevity is the soul of sustainability: Characterizing LLM response lengths

https://github.com/suyan-liang/EshopInstruct

공식 코드

LLaSA: Large Language and E-Commerce Shopping Assistant

https://github.com/wdrink/simplear

공식 코드

SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL

https://github.com/LuK2019/ICNN_BA

Input Convex Neural Networks

https://github.com/lit-leo/inner-convex-nn

Input Convex Neural Networks

https://github.com/magic-research/Sa2VA

The 1st Solution for 4th PVUW MeViS Challenge: Unleashing the Potential of Large Multimodal Models for Referring Video Segmentation

https://github.com/reysam93/adaptive_agg_gcn

Enhanced graph-learning schemes driven by similar distributions of motifs

https://github.com/reysam93/cvx_dag_learning

Enhanced graph-learning schemes driven by similar distributions of motifs

https://github.com/reysam93/online_ntf_expanding

Enhanced graph-learning schemes driven by similar distributions of motifs

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