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

https://github.com/guang000/banko

공식 코드

Generative Dataset Distillation Based on Diffusion Model

https://github.com/guangjinpan/lwlm

공식 코드

Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks

https://github.com/guangqiangwu/diffgan

공식 코드

Multivariate Time Series Anomaly Detection using DiffGAN Model

https://github.com/guanhaogan/robust-model-watermarking

공식 코드

Towards Robust Model Watermark via Reducing Parametric Vulnerability

https://github.com/guhuangai/diffusionedge

공식 코드

DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection

https://github.com/guobaoxiao/lsc

공식 코드

Latent Semantic Consensus For Deterministic Geometric Model Fitting

https://github.com/guoyihonggyh/mobody-model-based-off-dynamics-offline-reinforcement-learning

공식 코드

MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning

https://github.com/guytevet/motion-diffusion-model

공식 코드

Human Motion Diffusion Model

https://github.com/hahnyuan/llm-viewer

공식 코드

LLM Inference Unveiled: Survey and Roofline Model Insights

https://github.com/hailsong/s3d

공식 코드

S3D: Sketch-Driven 3D Model Generation

https://github.com/haitaowutju/turing-machine-bench

공식 코드

Computational Reasoning of Large Language Models

https://github.com/haiyang-w/tokenformer

공식 코드

TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters

https://github.com/hamidrezaeiv/kpca-deeponet

공식 코드

Nonlinear model reduction for operator learning

https://github.com/hancheolp/compression_for_capturing_ambiguity

공식 코드

Deep Model Compression Also Helps Models Capture Ambiguity

https://github.com/hanglai/bmpo

공식 코드

Bidirectional Model-based Policy Optimization

https://github.com/hannafoe/cryptanalytical-extraction

공식 코드

Beyond Slow Signs in High-fidelity Model Extraction

https://github.com/hannight/soft_self_consistency

공식 코드

Soft Self-Consistency Improves Language Model Agents

https://github.com/hanningzhang/er-prm

공식 코드

Entropy-Regularized Process Reward Model

https://github.com/haozheji/exact-optimization

공식 코드

Towards Efficient Exact Optimization of Language Model Alignment

https://github.com/harryjo97/rdlm

공식 코드

Continuous Diffusion Model for Language Modeling

https://github.com/hasined/giffcf

공식 코드

Graph Signal Diffusion Model for Collaborative Filtering

https://github.com/hazyresearch/smoothie

공식 코드

Smoothie: Label Free Language Model Routing

https://github.com/healthylaife/mimic-iv-data-pipeline

공식 코드

An Extensive Data Processing Pipeline for MIMIC-IV

https://github.com/heaventian93/ImageNet-Models-on-Domain-Adaptation

공식 코드

Impact of ImageNet Model Selection on Domain Adaptation

https://github.com/heinrichst/adaptive-mtrnn-grounding

공식 코드

Crossmodal Language Grounding in an Embodied Neurocognitive Model

https://github.com/henningzakrisson/local-glm-boost

공식 코드

A tree-based varying coefficient model

https://github.com/hirofumi0810/neural_sp

공식 코드

CTC-synchronous Training for Monotonic Attention Model

https://github.com/hiroki13/span-based-srl

공식 코드

A Span Selection Model for Semantic Role Labeling

https://github.com/hitachinsk/samed

공식 코드

Customized Segment Anything Model for Medical Image Segmentation

https://github.com/hiyouga/AMP-Regularizer

공식 코드

Regularizing Neural Networks via Adversarial Model Perturbation

https://github.com/hiyouga/llama-factory

공식 코드

LaCo: Large Language Model Pruning via Layer Collapse

https://github.com/hj-ok/audiobert

공식 코드

AudioBERT: Audio Knowledge Augmented Language Model

https://github.com/hku-medai/cate

공식 코드

Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement

https://github.com/hkuds/anygraph

공식 코드

AnyGraph: Graph Foundation Model in the Wild

https://github.com/hkuds/diffgraph

공식 코드

DiffGraph: Heterogeneous Graph Diffusion Model

https://github.com/hkuds/diffkg

공식 코드

DiffKG: Knowledge Graph Diffusion Model for Recommendation

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