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

https://github.com/indigo-ai/bertino

공식 코드

BERTino: an Italian DistilBERT model

https://github.com/inhwanbae/singulartrajectory

공식 코드

SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model

https://github.com/insilicomedicine/nach0

공식 코드

nach0: Multimodal Natural and Chemical Languages Foundation Model

https://github.com/insta360-research-team/defom-stereo

공식 코드

DEFOM-Stereo: Depth Foundation Model Based Stereo Matching

https://github.com/intelligent-computing-lab-yale/energy-separation-training

공식 코드

Adversarial Detection without Model Information

https://github.com/intersun/PKD-for-BERT-Model-Compression

공식 코드

Patient Knowledge Distillation for BERT Model Compression

https://github.com/invprobs/a-adaptive-model-based-methods

공식 코드

Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures

https://github.com/iqua/vggbase

공식 코드

Language-Guided Diffusion Model for Visual Grounding

https://github.com/ishikura-a/modelgpt

공식 코드

ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation

https://github.com/issararab/pevolm

공식 코드

PEvoLM: Protein Sequence Evolutionary Information Language Model

https://github.com/ivanslapnicar/EvolutionaryModel.jl

공식 코드

Universal evolutionary model for periodical species

https://github.com/izmailovpavel/bnn_covariate_shift

공식 코드

Dangers of Bayesian Model Averaging under Covariate Shift

https://github.com/jaabmar/cp_fuse

공식 코드

Copyright-Protected Language Generation via Adaptive Model Fusion

https://github.com/jackboyla/glirel

공식 코드

GLiREL -- Generalist Model for Zero-Shot Relation Extraction

https://github.com/jacopotagliabue/dag-card-is-the-new-model-card

공식 코드

DAG Card is the new Model Card

https://github.com/jadyahya/roll-yaw-and-lateral-velocity-mpc

공식 코드

Model Predictive Control for Integrated Lateral Stability

https://github.com/jaearly/milli

공식 코드

Model Agnostic Interpretability for Multiple Instance Learning

https://github.com/jakespringer/echo-embeddings

공식 코드

Repetition Improves Language Model Embeddings

https://github.com/jamesliley/model_updating

공식 코드

Model updating after interventions paradoxically introduces bias

https://github.com/jamesliley/optholdoutsize_pipelines

공식 코드

Holdouts set for safe predictive model updating

https://github.com/janfb/mcabc

공식 코드

Model Comparison in Approximate Bayesian Computation

https://github.com/jankowskichristopher/red-teaming-segment-anything-model

공식 코드

Red-Teaming Segment Anything Model

https://github.com/janosh/pymatviz

공식 코드

MatterGen: a generative model for inorganic materials design

https://github.com/jaripeeperkorn/lstm_process_model_structure

공식 코드

Can recurrent neural networks learn process model structure?

https://github.com/jasongross/guarantees-based-mechanistic-interpretability-with-data

공식 코드

Compact Proofs of Model Performance via Mechanistic Interpretability

https://github.com/jasonyux/Learning-with-Adaptive-Labels

공식 코드

Improving Model Training via Self-learned Label Representations

https://github.com/jazzbob/bv

공식 코드

Bayesian Verification under Model Uncertainty

https://github.com/jbr-ai-labs/mamba

공식 코드

Scalable Multi-Agent Model-Based Reinforcement Learning

https://github.com/jbrry/Irish-BERT

공식 코드

gaBERT -- an Irish Language Model

https://github.com/jcoreyes/OP3

공식 코드

Entity Abstraction in Visual Model-Based Reinforcement Learning

https://github.com/jdbrunner/friendlynets

공식 코드

Metabolic Model-based Ecological Modeling for Probiotic Design

https://github.com/jenninglim/model-comparison-test

공식 코드

Kernel Stein Tests for Multiple Model Comparison

https://github.com/jeroneandrews/cama

공식 코드

Conditional Adversarial Camera Model Anonymization

https://github.com/jerry-chee/modelpreservecompressionnn

공식 코드

Model Preserving Compression for Neural Networks

https://github.com/jhlau/topically-driven-language-model

공식 코드

Topically Driven Neural Language Model

https://github.com/jhoon-cho/mbtl

공식 코드

Model-Based Transfer Learning for Contextual Reinforcement Learning

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