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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/google-deepmind/csil
공식 코드https://github.com/google-deepmind/open_spiel
공식 코드https://github.com/google-research-datasets/simulated-dialogue
공식 코드https://github.com/google-research/chirp
공식 코드Global birdsong embeddings enable superior transfer learning for bioacoustic classification
https://github.com/google-research/deep_ope
공식 코드https://github.com/google-research/deep_representation_one_class
공식 코드Learning and Evaluating Representations for Deep One-class Classification
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/COSTAR
공식 코드COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning
https://github.com/google-research/google-research/tree/master/compositional_rl
공식 코드Adversarial Environment Generation for Learning to Navigate the Web
https://github.com/google-research/google-research/tree/master/pwil
공식 코드https://github.com/google-research/google-research/tree/master/tabnet
공식 코드https://github.com/google-research/perch
공식 코드Global birdsong embeddings enable superior transfer learning for bioacoustic classification
https://github.com/google-research/rlds
공식 코드RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
https://github.com/google-research/socialgood
공식 코드Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health
https://github.com/google/ihmehimmeli
공식 코드https://github.com/google/uncertainty-baselines
공식 코드Plex: Towards Reliability using Pretrained Large Model Extensions
https://github.com/google/yggdrasil-decision-forests
공식 코드Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web
https://github.com/gouxiangchen/SILCR
공식 코드Self-Imitation Learning for Robot Tasks with Sparse and Delayed Rewards
https://github.com/gpapagiannis/sinkhorn-imitation
공식 코드https://github.com/gracjangoral/when-all-options-are-wrong
공식 코드Wait, that's not an option: LLMs Robustness with Incorrect Multiple-Choice Options
https://github.com/grimmlab/gumbeldore
공식 코드Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
https://github.com/grockious/lcrl
공식 코드https://github.com/groshanlal/Fairness-Aware-Online-Personalization
공식 코드https://github.com/groshanlal/nn2rules
공식 코드https://github.com/groshanlal/te2rules
공식 코드https://github.com/gt-star-lab/corrective-demos-dexterous-manipulation
공식 코드https://github.com/gtfintechlab/LMs-Perceive-Social-Norms
공식 코드https://github.com/gtrll/rlfamily
공식 코드https://github.com/guangyizhangbci/eeg_riemannian
공식 코드Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space
https://github.com/guijinson/fiog
공식 코드Beyond Classification: Financial Reasoning in State-of-the-Art Language Models
https://github.com/guilherme439/nuzero
공식 코드Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks
https://github.com/guldoganozgur/long_term_fairness_pos_reinf
공식 코드Long-Term Fairness in Sequential Multi-Agent Selection with Positive Reinforcement
https://github.com/gulu42/System-0
공식 코드https://github.com/guobbin/PFL-MoE
공식 코드PFL-MoE: Personalized Federated Learning Based on Mixture of Experts
https://github.com/guojm14/ESPL
공식 코드Efficient Symbolic Policy Learning with Differentiable Symbolic Expression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.