Scalable Subset Sampling with Neural Conditional Poisson Networks
Authors: Adeel Pervez, Phillip Lippe, Efstratios Gavves
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach extensively, on image and text model explanation, image subsampling and stochastic k-nearest neighbor tasks outperforming existing methods in accuracy, efficiency and scalability. |
| Researcher Affiliation | Academia | Adeel Pervez QUVA Lab, Informatics Institute University of Amsterdam a.a.pervez@uva.nl |
| Pseudocode | Yes | The full algorithm is described in Algorithm 3 and pseudocode is given in the appendix in Algorithm 4. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | For the text classification experiments we use the Large Movie Review Dataset (Maas et al., 2011). We also use the 20Newsgroups dataset (Rennie & Lang, 2008). CIFAR-10 (Krizhevsky, 2009) and STL-10 (Coates et al., 2011). Celeb A-HQ (Lee et al., 2020) dataset |
| Dataset Splits | Yes | The models to be explained in both cases are convolutional neural networks, which achieve 90% and 70% test set accuracy on IMDB and 20Newgsroups, respectively. For CIFAR-10 we explain a simple CNN model with 8 convolutional layers that achieves 80% val. accuracy, and for STL-10 a Res Net-10 model that achieves 75% val. accuracy. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments, we choose t between 5 and 8. For this we add a squared loss term in the loss expression as γ(µk ˆk)2, where µk is the mini-batch average k computed the network, and γ is the regularization strength chosen from {0.1, 0.01, 0.001}. We use a small 6-layer CNN with max-pooling layers for downsampling, a final global average pooling layer for the output, and train the model for 80 epochs. |