Streaming Bayesian Deep Tensor Factorization
Authors: Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we examined SBDT in four real-world large-scale applications, including both binary and continuous tensors. We compared with the state-of-the-art streaming tensor factorization algorithm (Du et al., 2018) based on a multilinear form, and streaming nonlinear factorization methods implemented with SVB. In both running and final predictive performance, our method consistently outperforms the competing approaches, mostly by a large margin. The running accuracy of SBDT is also much more stable and smooth than the SVB based methods. |
| Researcher Affiliation | Collaboration | 1University of Uath 2Kwai Inc 3University of Rochester. |
| Pseudocode | Yes | Algorithm 1 Streaming Bayesian Deep Tensor Factorization (SBDT) |
| Open Source Code | No | The paper mentions that it implemented its method with Theano and TensorFlow, and refers to a MATLAB implementation for a baseline method (POST) at a GitHub link. However, it does not provide an explicit statement or link to the open-source code for SBDT itself. |
| Open Datasets | Yes | (1) DBLP (Du et al., 2018)... (2) Anime(https: //www.kaggle.com/Cooper Union/ anime-recommendations-database)... (3) ACC (Du et al., 2018)... (4) Movie Len1M (https: //grouplens.org/datasets/movielens/) |
| Dataset Splits | No | The paper explicitly describes training and test splits, but it does not mention a separate validation set or its corresponding split. |
| Hardware Specification | Yes | We ran all the methods on a desktop machine with Intel i9-9900K CPU and 32GB memory. We did not use GPU acceleration to run SBDT for a fair comparison. |
| Software Dependencies | No | The paper mentions software used (Theano, TensorFlow, Matlab) but does not provide specific version numbers for these dependencies, which are necessary for reproducibility. |
| Experiment Setup | Yes | For SVB/SS-GPTF, we set the number of pseudo inputs to 128 in their sparse approximations. We used Adam (Kingma & Ba, 2014) for the stochastic optimization in SVB-DTF and SVB/SS-GPTF, where we set the number of epochs to 100 in processing each streaming batch and tuned the learning rate from {10 5, 5 10 5, 10 4, 3 10 4, 5 10 4, 10 3, 3 10 3, 5 10 3, 10 2}. For SBDT and SVB-DTF, We used a 3-layer NN, with 50 nodes in each hidden layer. We tested Re LU and tanh activations. |