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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
| Date | Stars |
|---|---|
| 2026-07-24 | 283 |
| 2026-07-25 | 283 |
| 2026-07-28 | 283 |
| 2026-07-30 | 283 |
| 2026-07-31 | 283 |
| 2026-08-06 | 284 |
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# Some good papers I like **Basic Background** - Gradient regarding to algebra (TODO list) - Gaussian CheatSheet - Kronecker-Factored Approximations (TODO list) - Asynchronous stochastic gradient descent **Topics with detailed notes** - *Exponentiated Gradient (EG)* - *Expectation Propagation* - *Gaussian processes* - *Gaussian Processes with Notes* **Papers with detailed notes** - *Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations*: https://arxiv.org/abs/1603.04351 - *Neutral Relation Extraction with Selective Attention over Instances*: http://www.aclweb.org/anthology/P16-1200 - *Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks*: http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP203.pdf - *Modeling Relation Paths for Representation Learning of Knowledge Bases*: http://www.aclweb.org/anthology/D15-1082 - *Cross-Sentence N-ary Relation Extraction with Graph LSTMs*: https://arxiv.org/abs/1708.03743 - *Six Challenges for Neural Machine Translation*: http://www.aclweb.org/anthology/W/W17/W17-3204.pdf - *Learning to Generate Reviews and Discovering Sentiment*: https://arxiv.org/pdf/1704.01444.pdf - *Black Box Variational Inference*: https://arxiv.org/pdf/1401.0118.pdf - Stochastic Variational Deep Kernel Learning (i.e. *Deep Kernel Learning for Multi-task Classification*) - https://arxiv.org/pdf/1611.00336.pdf - *The Human Kernel* - http://papers.nips.cc/paper/5765-the-human-kernel - *Additive Gaussian Processes* - http://hannes.nickisch.org/papers/conferences/duvenaud11gpadditive.pdf - *Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)* - http://proceedings.mlr.press/v37/wilson15.pdf - *Gaussian Process Kernels for Pattern Discovery and Extrapolation* - https://arxiv.org/abs/1302.4245 - *Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks* - https://arxiv.org/pdf/1707.02476.pdf - *Gaussian Processes for Classification* - Chapter 3 of the book Gaussian Processes for Machine Learning http://www.gaussianprocess.org/gpml/chapters/RW3.pdf - *Sparse Gaussian Processes using Pseudo-inputs* - https://papers.nips.cc/paper/2857-sparse-gaussian-processes-using-pseudo-inputs - *Multi-task Sequence to Sequence Learning* - https://arxiv.org/abs/1511.06114 - *Zero-Resource Translation with Multi-Lingual Neural Machine Translation* - https://arxiv.org/pdf/1606.04164.pdf - *Multi-way, Multilingual neural machine translation with a shared attention mechanism* - http://www.aclweb.org/anthology/N16-1101 - *Differentiated Softmax* - http://www.aclweb.org/anthology/P16-1186 - *Noise-contrastive estimation: A new estimation principle for unnormalized statistical models* http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf - *Hierarchical Probabilistic Neural Network Language Model* - http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf - *Variational Inference with Normalizing Flows*: https://arxiv.org/abs/1505.05770 - *MADE: Masked Autoencoder for Distribution Estimation*: https://arxiv.org/abs/1502.03509 - *Improving Variational Inference with Inverse Autoregressive Flow*: https://arxiv.org/abs/1606.04934 - *Density estimation using Real NVP*: https://arxiv.org/abs/1605.08803 - *Wasserstein GAN*: https://arxiv.org/abs/1701.07875 - *ImageNet Classification with Deep Convolutional Neural Networks*: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks: - *Fully Convolutional Networks for Image Segmentation*: https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf - *Convolutional Sequence to Sequence Learning*: https://arxiv.org/pdf/1705.03122.pdf - *WaveNet: A Generative Model for Raw Audio*: https://arxiv.org/abs/1609.03499 - *A Deep Reinforced Model for Abstractive Summarization*: https://arxiv.org/pdf/1705.04304.pdf - *Attention Is All You Need*: https://arxiv.org/abs/1706.03762 - *Depthwise Separable Convolu
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:45127aed1356a05a, topic:deep-learning, topic:neural-network
matched fp:45127aed1356a05a, topic:computer-vision, readme:image segmentation
matched fp:45127aed1356a05a, topic:representation-learning, readme:representation learning
matched fp:45127aed1356a05a, topic:natural-language-processing, desc:natural language processing, readme:machine translation