Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Active Mini-Batch Sampling Using Repulsive Point Processes
Authors: Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi5741-5748
AAAI 2019 | Venue PDF | 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, EMAIL 2. Disney Research, Zurich, Switzerland, EMAIL 3. University of California,Irvine,Los Angeles, USA, EMAIL 4. KTH Royal Institute of Technology, Stockholm, Sweden, EMAIL |
| 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) |