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

https://github.com/cmry/augtox

공식 코드

Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations

https://github.com/cmu-catalyst/gradsign

공식 코드

GradSign: Model Performance Inference with Theoretical Insights

https://github.com/coderpat/learning-scaffold

공식 코드

Learning to Scaffold: Optimizing Model Explanations for Teaching

https://github.com/cohortshapley/cohortintgrad

공식 코드

Model free variable importance for high dimensional data

https://github.com/colinkohler/localdynamicsmodel

공식 코드

Visual Foresight With a Local Dynamics Model

https://github.com/computational-cell-analytics/peft-sam

공식 코드

Parameter Efficient Fine-Tuning of Segment Anything Model

https://github.com/conglu1997/acd

공식 코드

Automated Capability Discovery via Model Self-Exploration

https://github.com/conorosully/interpreting-coastline-unet

공식 코드

Interpreting a Semantic Segmentation Model for Coastline Detection

https://github.com/contextualai/halos

공식 코드

KTO: Model Alignment as Prospect Theoretic Optimization

https://github.com/controllability/jailbreak-evaluation

공식 코드

Rethinking How to Evaluate Language Model Jailbreak

https://github.com/cooelf/instancereg

공식 코드

Instance Regularization for Discriminative Language Model Pre-training

https://github.com/copenlu/flexible-probing

공식 코드

A Latent-Variable Model for Intrinsic Probing

https://github.com/cornell-relaxml/yaqa

공식 코드

Model-Preserving Adaptive Rounding

https://github.com/cornell-zhang/allo

공식 코드

Allo: A Programming Model for Composable Accelerator Design

https://github.com/cornell-zhang/allo-pldi24-artifact

공식 코드

Allo: A Programming Model for Composable Accelerator Design

https://github.com/coswindywang/HdLM

공식 코드

Making Language Model a Hierarchical Classifier and Generator

https://github.com/cosylabiiit/recipe-knowledge-mining

공식 코드

A Named Entity Based Approach to Model Recipes

https://github.com/coteries/cedille-ai

공식 코드

Cedille: A large autoregressive French language model

https://github.com/cr7anand/neural_temporal_models

공식 코드

A Neural Temporal Model for Human Motion Prediction

https://github.com/cristal-3dsam/bigraphdiff

공식 코드

Bipartite Graph Diffusion Model for Human Interaction Generation

https://github.com/cruiseresearchgroup/i-align

공식 코드

i-Align: an interpretable knowledge graph alignment model

https://github.com/csjunxu/STAR

공식 코드

STAR: A Structure and Texture Aware Retinex Model

https://github.com/csu-signal/axomiyaberta

공식 코드

AxomiyaBERTa: A Phonologically-aware Transformer Model for Assamese

https://github.com/cvejoski/Neural-Dynamic-Focused-Topic-Model

공식 코드

Neural Dynamic Focused Topic Model

https://github.com/cvlab-epfl/voxel2mesh

공식 코드

Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data

https://github.com/cvlab-stonybrook/pathldm

공식 코드

PathLDM: Text conditioned Latent Diffusion Model for Histopathology

https://github.com/cychomatica/audiopure

공식 코드

Defending against Adversarial Audio via Diffusion Model

https://github.com/czg1225/verithinker

공식 코드

VeriThinker: Learning to Verify Makes Reasoning Model Efficient

https://github.com/czyssrs/Few-Shot-NLG

공식 코드

Few-Shot NLG with Pre-Trained Language Model

https://github.com/d223302/albert-embryology

공식 코드

Pretrained Language Model Embryology: The Birth of ALBERT

https://github.com/d909b/cxplain

공식 코드

CXPlain: Causal Explanations for Model Interpretation under Uncertainty

https://github.com/da03/criticize_text_generation

공식 코드

Model Criticism for Long-Form Text Generation

https://github.com/daiquocnguyen/Walk-Transformer

공식 코드

A Self-Attention Network based Node Embedding Model

https://github.com/daixiangau/acl2020-transition-discontinuous-ner

공식 코드

An Effective Transition-based Model for Discontinuous NER

https://github.com/dalab/pboh-entity-linking

공식 코드

Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

https://github.com/daniel-bogdoll/model_contradictions

공식 코드

Complementary Learning for Real-World Model Failure Detection

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