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

https://github.com/stressosaurus/a-statistical-model-of-word-rank-evolution

공식 코드

A Statistical Model of Word Rank Evolution

https://github.com/sun-hailong/aaai25-mos

공식 코드

MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning

https://github.com/sun-hailong/lamda-pilot

공식 코드

MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning

https://github.com/sunarker/lccn

공식 코드

Latent Class-Conditional Noise Model

https://github.com/superhero-7/altdiffuson

공식 코드

AltDiffusion: A Multilingual Text-to-Image Diffusion Model

https://github.com/surinahn/xai-uncertainty-quantification

공식 코드

Uncertainty Quantification for Local Model Explanations Without Model Access

https://github.com/surjray-repos/EXACT

공식 코드

Exact inference under the perfect phylogeny model

https://github.com/suyeecav/model-targeted-poisoning

공식 코드

Model-Targeted Poisoning Attacks with Provable Convergence

https://github.com/svengronauer/x-mpsc

공식 코드

Reinforcement Learning with Ensemble Model Predictive Safety Certification

https://github.com/symphonynet/SymphonyNet

공식 코드

Symphony Generation with Permutation Invariant Language Model

https://github.com/syon-li/coachhook

공식 코드

Consecutive Batch Model Editing with HooK Layers

https://github.com/tal-aurorax/scen

공식 코드

Scalable Model Editing via Customized Expert Networks

https://github.com/tanganke/fusion_bench

공식 코드

FusionBench: A Comprehensive Benchmark of Deep Model Fusion

https://github.com/tanganke/hmr

공식 코드

Improving Heterogeneous Model Reuse by Density Estimation

https://github.com/tatHi/optok

공식 코드

Joint Optimization of Tokenization and Downstream Model

https://github.com/tatHi/optok4at

공식 코드

Joint Optimization of Tokenization and Downstream Model

https://github.com/tatsu432/BDCM

공식 코드

Diffusion Model in Causal Inference with Unmeasured Confounders

https://github.com/taylorjocelyn/diffusion-model-quantization

공식 코드

Diffusion Model Quantization: A Review

https://github.com/tbwa233/ada-sam

공식 코드

Autoadaptive Medical Segment Anything Model

https://github.com/tech-srl/c3po

공식 코드

A Structural Model for Contextual Code Changes

https://github.com/tedmoskovitz/constrainedrl4lms

공식 코드

Confronting Reward Model Overoptimization with Constrained RLHF

https://github.com/tencent/flashvdm

공식 코드

Unleashing Vecset Diffusion Model for Fast Shape Generation

https://github.com/tensorflow/models

공식 코드

MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation

https://github.com/tensorflow/tensor2tensor

공식 코드

One Model To Learn Them All

https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

공식 코드

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/tenstep/DeepModel

공식 코드

Model-based Deep Hand Pose Estimation

https://github.com/testing-cs/codes

공식 코드

CodeS: Towards Code Model Generalization Under Distribution Shift

https://github.com/testing-cs/codes-distribution-shift-benchmark-datasets

공식 코드

CodeS: Towards Code Model Generalization Under Distribution Shift

https://github.com/tfburns/cdam

공식 코드

Semantically-correlated memories in a dense associative model

https://github.com/tgangwani/ailo

공식 코드

Imitation Learning from Observations under Transition Model Disparity

https://github.com/th2l/Eye_VR_Segmentation

공식 코드

Eye Semantic Segmentation with a Lightweight Model

https://github.com/thanard/me-trpo

공식 코드

Model-Ensemble Trust-Region Policy Optimization

https://github.com/thennal10/fisher-nodes-merging

공식 코드

Fisher Mask Nodes for Language Model Merging

https://github.com/thobotics/RoMBRL

공식 코드

Bayes-Adaptive Deep Model-Based Policy Optimisation

https://github.com/thomashelfer/multimodal-supernovae

공식 코드

Maven: A Multimodal Foundation Model for Supernova Science

https://github.com/thomfoster/treeQuadrature

공식 코드

Model Evidence with Fast Tree Based Quadrature

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