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..
Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings
Authors: Shweta Mahajan, Iryna Gurevych, Stefan Roth
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our model on diverse tasks, including image captioning and text-to-image synthesis. To that end, we perform experiments on the COCO dataset (Lin et al., 2014). |
| Researcher Affiliation | Academia | Shweta Mahajan, Iryna Gurevych, Stefan Roth Department of Computer Science, TU Darmstadt, Germany |
| Pseudocode | No | The paper provides architectural diagrams and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or providing links to a code repository. |
| Open Datasets | Yes | To that end, we perform experiments on the COCO dataset (Lin et al., 2014). |
| Dataset Splits | Yes | It contains 82,783 training and 40,504 validation images, each with five captions. Following Wang et al. (2016); Mao et al. (2015) for image captioning, we use 118,287 data points for training and evaluate on 1,000 test images. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like VGG-16, GRU, and LSTM in the architecture details but does not provide specific version numbers for any libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | The overall objective of our semi-supervised generative model framework to be minimized is given by Lµ(xt,xv) = λ1DKL(qθ1 zs|xt, xv) pφs(zs) + λ2DKL(qθ2 z t|xt, zs) pφt(z t|zs) + λ3DKL qθ3(z v|xv, zs) pφv(z v|zs) + λ4Lrec t (xt, xt) + λ5Lrec v (xv, xv). |