├── LLM.md └── classification_clustering.md /LLM.md: -------------------------------------------------------------------------------- 1 | # papers_with_code 2 | This contains some papers with respect to LLM. 3 | 4 | # 2022-2023 5 | |Year| Title | Conference/Journal | Code | 6 | |:-------:|:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 7 | |2022| GLM: General Language Model Pretraining with Autoregressive Blank Infilling | ACL | [Code](https://github.com/THUDM/GLM) | 8 | |2023| {GLM}-130B: An Open Bilingual Pre-trained Model | ICLR | [Code](https://github.com/THUDM/GLM-130B)| 9 | |2023|A Survey of Large Language Models| - |[Code](https://github.com/RUCAIBox/LLMSurvey) | 10 | |2022|M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems|-|-| 11 | |2023|Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System|-|-| 12 | |2022|Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)|-|-| 13 | ||||| 14 | -------------------------------------------------------------------------------- /classification_clustering.md: -------------------------------------------------------------------------------- 1 | # papers_with_code 2 | This contains some papers with respect to classification, clustering and etc. 3 | 4 | # 2020 5 | | Title | Conference/Journal | Code | 6 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 7 | | Ultra-Scalable Spectral Clustering and Ensemble Clustering | TKDE | [Code](https://github.com/huangdonghere/USPEC_USENC) | 8 | 9 | 10 | 11 | 12 | # 2019 13 | | Title | Conference/Journal | Code | 14 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 15 | | similarity learning via kernel preserving embedding | AAAI | [Code](https://github.com/sckangz/SLKE) | 16 | | spectral clustering of signed graphs via matrix power means | ICML | [Code](https://github.com/melopeo/SPM) | 17 | | model-based synthetic sampling for imbalanced data| TKDE | [Code](https://github.com/b10071007/Model-Based-Synthetic-Sampling) | 18 | | K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters| KDD | [Code](https://github.com/CHLWR/KDD2019_K-Multiple-Means) | 19 | | The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering| AAAI | [Code](https://bitbucket.org/Sibylse/spectacl/src/master/) | 20 | | Similarity Preserving Representation Learning for Time Series Clustering| IJCAI | [Code](https://github.com/cecilialeiqi/SPIRAL) | 21 | | Supervised Hierarchical Clustering with Exponential Linkage| ICML | [Code](https://github.com/iesl/expLinkage) | 22 | | Subspace Clustering via Good Neighbors| TPAMI | [Code](https://github.com/JLiangNKU/FGNSC) | 23 | 24 | 25 | 26 | 27 | # 2018 28 | | Title | Conference/Journal | Code | 29 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 30 | | unified spectral clustering with optimal graph | AAAI | [Code](https://github.com/sckangz/AAAI18) | 31 | | scalable spectral clustering using random binning features | KDD | [Code](https://github.com/IBM/SpectralClustering_RandomBinning) | 32 | | spectral clustering of large-scale data by directly solving normalized cut| KDD |[Code](http://www.escience.cn/people/chenxiaojun/index.html;jsessionid=400511E08064B68CF40EE01E4E191AC8-n1)| 33 | | understanding regularized spectral clustering via graph conductance | NIPS | [Code](https://github.com/crisbodnar/regularised-spectral-clustering) | 34 | | Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering | IEEE Transactions on Signal Processing | [Code](https://github.com/tgensol/AMOS) | 35 | | Multiview clustering via adaptively weighted procrustes | KDD | [Code](http://www.escience.cn/people/fpnie/papers.html) | 36 | | On the Spectrum of Random Features Maps of High Dimensional Data | ICML | [Code](https://github.com/Zhenyu-LIAO/RMT4RFM) | 37 | 38 | 39 | 40 | 41 | # 2017 42 | | Title | Conference/Journal | Code | 43 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 44 | | semi-supervised feature selection via rescaled linear regression | IJCAI | [Code](http://www.escience.cn/people/chenxiaojun/index.html;jsessionid=400511E08064B68CF40EE01E4E191AC8-n1) | 45 | | unsupervised large graph embedding | AAAI | [Code](http://chenglongli.cn/people/fpnie/index.html;jsessionid=050431E612A9A370272E702D09061DC0-n2) | 46 | |robust spectral clustering for noisy data-modeling sparse corruptions improves latent embeddings|KDD|[Code](https://github.com/abojchevski/rsc)| 47 | |twin learning for similarity and clustering: a unified kernel approach|AAAI|[Code](https://github.com/sckangz/AAAI17)| 48 | |AMOS: An automated model order selection algorithm for spectral graph clustering|ICASSP|[Code](https://github.com/tgensol/AMOS)| 49 | |Multiclass Capped Lp-Norm SVM for Robust Classifications|AAAI|[Code](http://www.escience.cn/people/fpnie/index.html)| 50 | |A Hierarchical Algorithm for Extreme Clustering|KDD|[Code](https://github.com/iesl/xcluster)| 51 | |scalable normalized cut with improved spectral rotation|IJCAI|[Code](http://www.xinhua-fluid.com/people/chenxiaojun/index.html;jsessionid=744A8E90AC2E7E9A6052536E11B01ECF-n1)| 52 | 53 | 54 | # 2016 55 | | Title | Conference/Journal | Code | 56 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 57 | | the constrained laplacian rank algorithm for graph-based clustering | AAAI | [Code](http://chenglongli.cn/people/fpnie/index.html;jsessionid=050431E612A9A370272E702D09061DC0-n2) | 58 | |unsupervised feature selection with structured graph optimization|AAAI|[Code](http://chenglongli.cn/people/fpnie/index.html;jsessionid=050431E612A9A370272E702D09061DC0-n2)| 59 | |compressive spectral clustering|ICML|[Code](http://cscbox.gforge.inria.fr/)| 60 | |FUSE: Full Spectral Clustering|KDD|[Code](https://github.com/yeweiysh/FUSE)| 61 | |cost-sensitive boosting algorithms: do we really need them?|Machine Learning|[Code](https://github.com/nnikolaou/Cost-sensitive-Boosting-Tutorial)| 62 | |Multiple Kernel k-Means Clustering with Matrix-Induced Regularization|AAAI|[Code](https://github.com/wangsiwei2010/Multiple-Kernel-k-Means-Clustering-with-Matrix-Induced-Regularization)| 63 | |Structured Doubly Stochastic Matrix for Graph Based Clustering|KDD|[Code](https://github.com/joyxqwang/bistochastic_kcut)| 64 | 65 | 66 | # 2015 67 | | Title | Conference/Journal | Code | 68 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 69 | | a new simplex sparse learning model to measure data similarity for clustering | IJCAI | [Code](http://chenglongli.cn/people/fpnie/index.html;jsessionid=050431E612A9A370272E702D09061DC0-n2) | 70 | | unsupervised feature selection with adaptive structure learning | KDD | [Code](https://github.com/csliangdu/FSASL) | 71 | |robust multiple kernel K-means using l21 norm|IJCAI|[Code](https://github.com/csliangdu/RMKKM)| 72 | 73 | # 2014 74 | | Title | Conference/Journal | Code | 75 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 76 | | clustering and projected clustering with adaptive neighbors | KDD | [Code](http://chenglongli.cn/people/fpnie/index.html;jsessionid=050431E612A9A370272E702D09061DC0-n2) | 77 | | constructing robust affinity graphs for spectral clustering | CVPR | [Code](http://personal.ie.cuhk.edu.hk/~ccloy/project_robust_graphs/index.html) | 78 | 79 | # 2013 80 | | Title | Conference/Journal | Code | 81 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 82 | | spectral rotation versus K-means in spectral clustering | AAAI | [Code](http://chenglongli.cn/people/fpnie/index.html;jsessionid=050431E612A9A370272E702D09061DC0-n2) | 83 | | large-scale spectral clustering on graphs | AAAI | [Code](http://jialu.info/) | 84 | 85 | # 2012 86 | | Title | Conference/Journal | Code | 87 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 88 | | | | [Code]() | 89 | 90 | # 2011 91 | | Title | Conference/Journal | Code | 92 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 93 | | | | [Code]() | 94 | 95 | # 2010 96 | | Title | Conference/Journal | Code | 97 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 98 | | efficient and robust feature selection via joint L21 norms minimization | NIPS | [Code](http://chenglongli.cn/people/fpnie/index.html;jsessionid=050431E612A9A370272E702D09061DC0-n2) | 99 | |power iteration clustering|ICML|[Code](https://spark.apache.org/docs/latest/mllib-clustering.html)| 100 | | making large-scale nystrom approximation possible | ICML | [Code](https://github.com/mli/nystrom) | 101 | | large graph construction for scalable semi-supervised learning | ICML | [Code](https://github.com/ColumbiaDVMM/Anchor-Graph) | 102 | 103 | # 2009 104 | | Title | Conference/Journal | Code | 105 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 106 | | fast approximate spectral clustering | KDD | [Code](http://www.math.umassd.edu/~dyan/fasp.html) | 107 | 108 | # 2008 109 | | Title | Conference/Journal | Code | 110 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 111 | | | | [Code]() | 112 | 113 | # 2007 114 | | Title | Conference/Journal | Code | 115 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 116 | | | | [Code]() | 117 | 118 | # 2006 119 | | Title | Conference/Journal | Code | 120 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 121 | | | | [Code]() | 122 | 123 | # 2005 124 | | Title | Conference/Journal | Code | 125 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 126 | | | | [Code]() | 127 | 128 | # 2004 129 | | Title | Conference/Journal | Code | 130 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 131 | | | | [Code]() | 132 | 133 | # 2003 134 | | Title | Conference/Journal | Code | 135 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 136 | | | | [Code]() | 137 | 138 | # 2002 139 | | Title | Conference/Journal | Code | 140 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 141 | | on spectral clustering-analysis and algorithm | NIPS | [Code](https://ww2.mathworks.cn/matlabcentral/fileexchange/26354-spectral-clustering-algorithms?requestedDomain=zh) | 142 | 143 | # 2001 144 | | Title | Conference/Journal | Code | 145 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 146 | | | | [Code]() | 147 | 148 | # 2000 149 | | Title | Conference/Journal | Code | 150 | |:-----------------------------------------------------------------------------------:|:-----------:|:-------:| 151 | | | | [Code]() | 152 | --------------------------------------------------------------------------------