Active Mini-Batch Sampling Using Repulsive Point Processes
Authors: Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi5741-5748
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that our approach improves over standard SGD both in terms of convergence speed as well as final model performance. |
| Researcher Affiliation | Collaboration | 1. Microsoft Research, Cambridge, UK, Cheng.Zhang@microsoft.com 2. Disney Research, Zurich, Switzerland, cengiz.oztireli@disneyresearch.com 3. University of California,Irvine,Los Angeles, USA, stephan.mandt@gmail.com 4. KTH Royal Institute of Technology, Stockholm, Sweden, giampi@kth.se |
| Pseudocode | Yes | Algorithm 1 Draw throwing for Dense PDS |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper, nor does it explicitly state that its code is released. |
| Open Datasets | Yes | Oxford flower classification task as in (Zhang et al. 2017b), MNIST dataset (Le Cun et al. 1998a), speech command classification task as described in (Sainath and Parada 2015). |
| Dataset Splits | Yes | We use half of the training data and the full test data. (MNIST); Figure 6 shows the accuracy on the validation set evaluated every 50 training iterations. (Speech Command Recognition) |
| Hardware Specification | No | The paper mentions CPU time measurements but does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, or memory amounts) used for running its experiments. |
| Software Dependencies | Yes | standard multi-layer convolutional neural network (CNN) from Tensorflow3 is used in this experiment (footnote 3 links to https://www.tensorflow.org/versions/r0.12/tutorials/mnist/pros/). |
| Experiment Setup | Yes | We sample one mini-batch with batch size 30 using different sampling methods. For each method, we train a neural network classifier with one hidden layer of five units, using a single mini-batch. (Synthetic Data); A standard multi-layer convolutional neural network (CNN) from Tensorflow3 is used in this experiment with standard experimental settings (details in appendix). (MNIST) |