🦅 Jury: A Comprehensive Evaluation Toolkit
🖥 Github: https://github.com/obss/jury
📕 Paper: https://arxiv.org/abs/2310.02040v1
🖥 Colab: https://colab.research.google.com/github/obss/jury/blob/main/examples/jury_evaluate.ipynb
⭐️ Demos: https://github.com/Parskatt/DeDoDe/blob/main/demo
https://news.1rj.ru/str/DataScienceT
pip install jury🖥 Github: https://github.com/obss/jury
📕 Paper: https://arxiv.org/abs/2310.02040v1
🖥 Colab: https://colab.research.google.com/github/obss/jury/blob/main/examples/jury_evaluate.ipynb
⭐️ Demos: https://github.com/Parskatt/DeDoDe/blob/main/demo
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⚡️ Memory Gym: Partially Observable Challenges to Memory-Based Agents in Endless Episodes
🖥 Github: https://github.com/marcometer/endless-memory-gym
🖥 Colab: https://colab.research.google.com/drive/1LjlUOEer8vjGrz0rLM8pP5UyeNCsURkY?usp=sharing
📕 Paper: https://openreview.net/forum?id=jHc8dCx6DDr
⭐️ Dataset: https://paperswithcode.com/dataset/arcade-learning-environment
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/marcometer/endless-memory-gym
🖥 Colab: https://colab.research.google.com/drive/1LjlUOEer8vjGrz0rLM8pP5UyeNCsURkY?usp=sharing
📕 Paper: https://openreview.net/forum?id=jHc8dCx6DDr
⭐️ Dataset: https://paperswithcode.com/dataset/arcade-learning-environment
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🔊 Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation
🖥 Github: https://github.com/guyyariv/TempoTokens
📕 Paper: https://arxiv.org/abs/2309.16429v1
⭐️ Dataset: https://paperswithcode.com/dataset/audioset
https://news.1rj.ru/str/DataScienceT
git clone git@github.com:guyyariv/TempoTokens.git🖥 Github: https://github.com/guyyariv/TempoTokens
📕 Paper: https://arxiv.org/abs/2309.16429v1
⭐️ Dataset: https://paperswithcode.com/dataset/audioset
https://news.1rj.ru/str/DataScienceT
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✅️ T3Bench: Benchmarking Current Progress in Text-to-3D Generation
🖥 Github: https://github.com/THU-LYJ-Lab/T3Bench
📕 Paper: https://arxiv.org/abs/2310.02977v1
⭐️ Dataset: https://paperswithcode.com/dataset/nerf
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/THU-LYJ-Lab/T3Bench
📕 Paper: https://arxiv.org/abs/2310.02977v1
⭐️ Dataset: https://paperswithcode.com/dataset/nerf
https://news.1rj.ru/str/DataScienceT
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ML Research Hub
✅️ T3Bench: Benchmarking Current Progress in Text-to-3D Generation 🖥 Github: https://github.com/THU-LYJ-Lab/T3Bench 📕 Paper: https://arxiv.org/abs/2310.02977v1 ⭐️ Dataset: https://paperswithcode.com/dataset/nerf https://news.1rj.ru/str/DataScienceT
Total interactions required: 20 👍
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Guideline following Large Language Model for Information Extraction
🖥 Github: https://github.com/hitz-zentroa/gollie
⏩ Tutorial: https://github.com/stanfordnlp/dspy/blob/main/intro.ipynb
⭐️ Project: https://hitz-zentroa.github.io/GoLLIE/
📕 Paper: https://arxiv.org/abs/2310.03668v1
⭐️ Dataset: https://paperswithcode.com/dataset/harveyner
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/hitz-zentroa/gollie
⏩ Tutorial: https://github.com/stanfordnlp/dspy/blob/main/intro.ipynb
⭐️ Project: https://hitz-zentroa.github.io/GoLLIE/
📕 Paper: https://arxiv.org/abs/2310.03668v1
⭐️ Dataset: https://paperswithcode.com/dataset/harveyner
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💻 Graph Structure Learning Benchmark (GSLB)
pip install GSLB
🖥 Github: https://github.com/gsl-benchmark/gslb
📕 Paper: https://arxiv.org/abs/2310.05163v1
⭐️ Paper collection: https://github.com/GSL-Benchmark/Awesome-Graph-Structure-Learning
https://news.1rj.ru/str/DataScienceT
pip install GSLB
🖥 Github: https://github.com/gsl-benchmark/gslb
📕 Paper: https://arxiv.org/abs/2310.05163v1
⭐️ Paper collection: https://github.com/GSL-Benchmark/Awesome-Graph-Structure-Learning
https://news.1rj.ru/str/DataScienceT
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✅ Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models.
🖥 Github: https://github.com/Zeqiang-Lai/Mini-DALLE3
📕 Paper: https://arxiv.org/abs/2310.07653v1
⭐️ Dataset: https://paperswithcode.com/dataset/mmlu
🖥 Github: https://github.com/Zeqiang-Lai/Mini-DALLE3
📕 Paper: https://arxiv.org/abs/2310.07653v1
⭐️ Dataset: https://paperswithcode.com/dataset/mmlu
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Augment to Interpret
🖥 Github: https://github.com/euranova/augment_to_interpret
📕 Paper: https://arxiv.org/pdf/2309.16564v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/cora
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/euranova/augment_to_interpret
📕 Paper: https://arxiv.org/pdf/2309.16564v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/cora
https://news.1rj.ru/str/DataScienceT
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Incremental Transfer Learning (ITL) Survey
🖥 Github: https://github.com/yixinghuang/itlsurvey
📕 Paper: https://arxiv.org/pdf/2309.17192v1.pdf
🔥 Datasets: https://paperswithcode.com/dataset/tiny-imagenet
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/yixinghuang/itlsurvey
📕 Paper: https://arxiv.org/pdf/2309.17192v1.pdf
🔥 Datasets: https://paperswithcode.com/dataset/tiny-imagenet
https://news.1rj.ru/str/DataScienceT
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🧠 LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios.
🖥 Github: https://github.com/opendilab/LightZero
📕 Paper: https://arxiv.org/abs/2310.08348v1
⭐️ Tasks: https://paperswithcode.com/task/atari-games
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/opendilab/LightZero
📕 Paper: https://arxiv.org/abs/2310.08348v1
⭐️ Tasks: https://paperswithcode.com/task/atari-games
https://news.1rj.ru/str/DataScienceT
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🔥 Burn - A Flexible and Comprehensive Deep Learning Framework in Rust
🖥 Github: https://github.com/burn-rs/burn
📕 Burn Book: https://burn-rs.github.io/book/
⭐️ Guide: https://www.kdnuggets.com/rust-burn-library-for-deep-learning
https://news.1rj.ru/str/DataScienceT
cargo new new_burn_app🖥 Github: https://github.com/burn-rs/burn
📕 Burn Book: https://burn-rs.github.io/book/
⭐️ Guide: https://www.kdnuggets.com/rust-burn-library-for-deep-learning
https://news.1rj.ru/str/DataScienceT
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✨ Cross-Episodic Curriculum for Transformer Agents
🖥 Github: https://github.com/CEC-Agent/CEC
📕 Paper: https://cec-agent.github.io/src/bib.txt
⭐️ Project: https://cec-agent.github.io
https://news.1rj.ru/str/DataScienceT
pip install git+https://github.com/cec-agent/CEC🖥 Github: https://github.com/CEC-Agent/CEC
📕 Paper: https://cec-agent.github.io/src/bib.txt
⭐️ Project: https://cec-agent.github.io
https://news.1rj.ru/str/DataScienceT
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