Papers with Code 아카이브
Playing the Game of 2048
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 5
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AhmedElkelesh/Genetic-Algorithm-based-Polar-Code-Construction
공식 코드Genetic Algorithm-based Polar Code Construction for the AWGN Channel
https://github.com/DSobscure/cgi_drl_platform
공식 코드Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games
https://github.com/HKBU-HPML/MG-WFBP
공식 코드MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning
https://github.com/Jeffrey-Ede/Electron-Micrograph-Denoiser
공식 코드Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder
https://github.com/Shilun-Allan-Li/2048-reinforcement_learning
공식 코드https://github.com/amazon-research/long-short-term-transformer
공식 코드https://github.com/aszczepanski/2048
공식 코드https://github.com/deepsense-ai/Distributed-BA3C
공식 코드Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes
https://github.com/google-research/google-research/tree/master/smith
공식 코드https://github.com/masterhow/eventpointpose
공식 코드https://github.com/moporgic/tdl2048
공식 코드https://github.com/newbeeer/pfgmpp
공식 코드PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
https://github.com/ofirpress/attention_with_linear_biases
공식 코드Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/ozcelikfu/ic-gan_fmri_reconstruction
공식 코드https://github.com/phyllisayk/nlp-efficiency-tradeoff
공식 코드Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models
https://github.com/stefanleong/s2crnet
공식 코드Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization
https://github.com/wangguanan/light-reid
공식 코드https://github.com/wjaskowski/mastering-2048
공식 코드https://github.com/DHDev0/Stochastic-muzero
https://github.com/MindCode-4/code-6/tree/main/alibi
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/MindSpore-scientific/code-3/tree/main/alibi
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/abachurin/2048
https://github.com/amazon-science/long-short-term-transformer
https://github.com/answerdotai/modernbert
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/apple/axlearn
GSPMD: General and Scalable Parallelization for ML Computation Graphs
https://github.com/baichuan-inc/baichuan-13b
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/brkkaya/a_linear_attention_with_biases
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/facebookresearch/fairscale
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
https://github.com/fuentesdt/livermask
https://github.com/jaketae/alibi
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/lucaslingle/mu_transformer
GSPMD: General and Scalable Parallelization for ML Computation Graphs
https://github.com/lucidrains/PaLM-jax
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/lucidrains/mixture-of-experts
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
https://github.com/mu-transformer-authors/mu_transformer
GSPMD: General and Scalable Parallelization for ML Computation Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.