Update README.md

README.md

@@ -42,116 +42,7 @@ See [.github/pull_request_template.md](.github/pull_request_template.md) for the
 
 # TODO
 
-* Emoji Meanings
-  * ❗ Indicates Priority
-  * 📖 Paper Read
-  * 📓 Notes Taken
-  * 💻 Implementation Completed
-
-
-* Reinforcement Learning
-    * Value Based Methods - I'm pretty much up to date with these methods, but might as well implement them. I may go into less explanation though.
-        * 📖📓💻 [$TD(\lambda)$](https://web.stanford.edu/class/cs234/notes/cs234-notes7.pdf)
-        * 📖📓💻❗ [Deep Q Learning](https://arxiv.org/abs/1312.5602)
-          * <https://lightning.ai/docs/pytorch/LTS/notebooks/lightning_examples/reinforce-learning-DQN.html>
-        * 📖❗[Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)
-        * 📖❗[Double Q Learning](https://arxiv.org/abs/1509.06461)
-        * [ ] [Dueling Q Learning](https://arxiv.org/abs/1511.06581)
-        * [ ] [Multi Step Learning](https://arxiv.org/abs/1901.02876)
-        * [ ] [Distributional DQN](https://arxiv.org/abs/1707.06887)
-        * [ ] [Noisy Nets](https://arxiv.org/abs/1706.10295)
-        * 📖 [RAINBOW](https://arxiv.org/abs/1710.02298)
-    * Policy Based Methods
-        * 📖📓💻 [REINFORCE](https://arxiv.org/abs/2010.11364) *
-        * 📖❗ [Actor-Critic](https://arxiv.org/pdf/1602.01783v2) (A2C, A3C) *
-        * [ ] [Trust Region Policy Optimization](https://arxiv.org/pdf/1502.05477) (TRPO)
-        * [ ]❗[Proximal Policy Optimization](https://arxiv.org/abs/1707.06347) (PPO) *
-        * [ ] [Deep Deterministic Policy Gradient](https://arxiv.org/abs/1509.02971v6) (DDPG)
-    * Model Based Reinforcement Learning
-        * 📖❗[AlphaZero](https://arxiv.org/abs/1712.01815)
-        * [ ] [MuZero](https://www.nature.com/articles/s41586-020-03051-4.epdf?sharing_token=kTk-xTZpQOF8Ym8nTQK6EdRgN0jAjWel9jnR3ZoTv0PMSWGj38iNIyNOw_ooNp2BvzZ4nIcedo7GEXD7UmLqb0M_V_fop31mMY9VBBLNmGbm0K9jETKkZnJ9SgJ8Rwhp3ySvLuTcUr888puIYbngQ0fiMf45ZGDAQ7fUI66-u7Y%3D)
-            * <https://deepmind.google/discover/blog/muzero-mastering-go-chess-shogi-and-atari-without-rules/>
-        * [ ] [Dreamer](https://arxiv.org/pdf/1912.01603)
-            * <https://research.google/blog/introducing-dreamer-scalable-reinforcement-learning-using-world-models/>
-        * [ ] [Efficient Zero](https://arxiv.org/abs/2111.00210)
-        * [ ] [Efficient Zero V2](https://arxiv.org/abs/2403.00564)
-        * [ ] [SIMA](https://arxiv.org/abs/2404.10179)
-            * <https://deepmind.google/discover/blog/sima-generalist-ai-agent-for-3d-virtual-environments/>
-        * [ ] [Genie 1](https://arxiv.org/abs/2402.15391)
-            * <https://deepmind.google/research/publications/60474/>
-        * [ ] [Genie 2](https://arxiv.org/pdf/2405.15489)
-            * <https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/>
-    * [ ] [Exploration in RL](https://github.com/opendilab/awesome-exploration-rl)
-         * [ ] [Go-Explore](https://www.nature.com/articles/s41586-020-03157-9)
-         * [ ] [NoisyNet](https://openreview.net/pdf?id=rywHCPkAW)
-         * [ ] [DQN-PixelCNN](https://arxiv.org/abs/1606.01868)
-         * [ ] [#Exploration](http://papers.neurips.cc/paper/6868-exploration-a-study-of-count-based-exploration-for-deep-reinforcement-learning.pdf)
-         * [ ] [EX2](https://papers.nips.cc/paper/2017/file/1baff70e2669e8376347efd3a874a341-Paper.pdf)
-         * [ ] [ICM](https://arxiv.org/abs/1705.05363)
-         * [ ] [RND](https://arxiv.org/abs/1810.12894)
-         * [ ] [NGU](https://arxiv.org/abs/2002.06038)
-         * [ ] [Agent57](https://arxiv.org/abs/2003.13350)
-         * [ ] [VIME](https://arxiv.org/abs/1605.09674)
-         * [ ] [EMI](https://openreview.net/forum?id=H1exf64KwH)
-         * [ ] [DIYAN](https://arxiv.org/abs/1802.06070)
-         * [ ] [SAC](https://arxiv.org/abs/1801.01290)
-         * [ ] [BootstrappedDQN](https://arxiv.org/abs/1602.04621)
-         * [ ] [PSRL](https://arxiv.org/pdf/1306.0940.pdf)
-         * [ ] [HER](https://arxiv.org/pdf/1707.01495.pdf)
-         * [ ] [DQfD](https://arxiv.org/abs/1704.03732)
-         * [ ] [R2D3](https://arxiv.org/abs/1909.01387)
-    * Multi Agent RL
-        * [ ] [Emergent Communication through Negotiation](https://arxiv.org/abs/1804.03980)
-        * [ ] Warp Drive
-           * <https://lightning.ai/docs/pytorch/LTS/notebooks/lightning_examples/warp-drive.html>
-    * [Human-Timescale Adaptation in an Open-Ended Task Space](https://sites.google.com/view/adaptive-agent/)
-        * [ ] [Muesli](https://arxiv.org/pdf/2104.06159)
-        * [ ] [Transformer-XL](https://arxiv.org/abs/1901.02860)
-        * [ ] [Robust PLR](https://arxiv.org/pdf/2110.02439)
-    * Distributed RL
-        * [ ] [Survey](https://arxiv.org/pdf/2011.11012)
-        * [ ] [RLLib](https://docs.ray.io/en/master/rllib.html)
-* Transformers
-    * [ ] [Tokenization](https://huggingface.co/learn/nlp-course/en/chapter6/1?fw=pt)
-    * [ ] [Word Embeddings](https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html)
-    * 📖❗[Transformers](https://arxiv.org/abs/1706.03762)
-      * <https://pytorch.org/tutorials/beginner/transformer_tutorial.html>
-      * <https://lightning.ai/docs/pytorch/stable/notebooks/course_UvA-DL/05-transformers-and-MH-attention.html>
-    * 📖❗[BERT](https://arxiv.org/abs/1810.04805)
-    * [ ]❗[Sentence-BERT](https://arxiv.org/pdf/1908.10084)
-    * [ ] [Fine Tuning](https://huggingface.co/learn/nlp-course/en/chapter3/1?fw=pt)
-    * [ ] [RLHF](https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo)
-    * [ ] [Direct Preference Optimization](https://arxiv.org/pdf/2305.18290)
-    * [ ] [Multimodality](https://lightning.ai/docs/pytorch/stable/notebooks/course_UvA-DL/11-vision-transformer.html)
-    * [ ] [Mamba and SSM's](https://towardsdatascience.com/mamba-ssm-theory-and-implementation-in-keras-and-tensorflow-32d6d4b32546)
-    * [ ] [Sentence Transformers](https://medium.com/@vipra_singh/building-llm-applications-sentence-transformers-part-3-a9e2529f99c1)
-    * [ ] [Multi token prediction](https://arxiv.org/pdf/2404.19737)
-    * [ ] Time Series
-        * <https://www.datadoghq.com/blog/datadog-time-series-foundation-model/>
-* RAG
-    * 📖 [Survey on RAG](https://arxiv.org/abs/2405.06211)
-        * [ ]❗REALM
-        * [ ]❗Hyde
-        * [ ]❗DPR
-        * [ ]❗Raft
-        * [ ] PRCA
-        * [ ] EAE
-        * [ ] MIPS
-        * [ ] Self reinforce
-    * [Survey on Graph RAG](https://arxiv.org/abs/2408.08921)
-* [ ] Diffusion Models
-  * <https://lightning.ai/lightning-community-labs/studios/build-diffusion-models-with-pytorch-lightning-hf-diffusers>
-* [ ]❗Graph Neural Networks (GNN)
-  * <https://lightning.ai/docs/pytorch/stable/notebooks/course_UvA-DL/06-graph-neural-networks.html>
-* Cognitive Science
-   * [ ] [Hopfield Network](https://www.youtube.com/watch?v=1WPJdAW-sFo)
-   * [ ] [Boltzman Machine](https://www.youtube.com/watch?v=_bqa_I5hNAo)
-   * [ ] [Conformal Prediction](https://blog.dataiku.com/measuring-models-uncertainty-conformal-prediction?utm_source=pocket_saves)
-   * [ ] [Predictive Coding Models](https://arxiv.org/abs/2202.09467)
-   * [ ] [Liquid Neural Networks](https://arxiv.org/pdf/2006.04439)
-* Techniques
-    * Profiling
-    * Debugging Metrics
+https://ryanpeach.com/Publish/Machine+Learning/Research+Papers
 
 ## Sources for further work