Maximum Entropy Flow Networks
Authors: Gabriel Loaiza-Ganem *, Yuanjun Gao *, John P. Cunningham
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results demonstrate the effectiveness of our method, and applications to finance and computer vision show the flexibility and accuracy of using maximum entropy flow networks. |
| Researcher Affiliation | Academia | Gabriel Loaiza-Ganem , Yuanjun Gao & John P. Cunningham Department of Statistics Columbia University New York, NY 10027, USA {gl2480,yg2312,jpc2181}@columbia.edu |
| Pseudocode | Yes | Algorithm 1 Training the MEFN |
| Open Source Code | No | The paper adapts existing codebases (e.g., 'https://github.com/Proof By Construction/texture-networks' and 'https://github.com/taesung89/real-nvp') but does not explicitly state that the authors' own implementation code for MEFN is open-source or provide a link to it. |
| Open Datasets | No | The paper uses real-world data like 'European call options on Nov. 1 2016 for the stock AAPL (Apple inc.)' and 'RGB representation of the 224 224 pixel images' for texture synthesis, and also constructs a synthetic problem with a known solution (Dirichlet), but it does not provide concrete access information, citations to established benchmark datasets, or links for any of the data used. |
| Dataset Splits | No | The paper mentions using 'training data' and 'testing data' for the option pricing application, and specifies sample sizes for SGD steps (n, n), but it does not provide explicit train/validation/test dataset splits, percentages, or absolute counts, nor does it refer to predefined standard splits for any dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or cloud computing specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'ADADELTA', 'ADAM', and 'Tensor Flow' but does not specify their version numbers, which is necessary for reproducible software dependencies. |
| Experiment Setup | Yes | For 4.1 and B, We use 10 layers of planar flow... For 4.3 we use real NVP structure and use ADAM (Kingma & Ba, 2014) with learning rate = 0.001. For all our experiments, we use imax = 3000, β = 4, γ = 0.25. For 4.1 and B we use n = 300, n = 1000, kmax = 10; For 4.3 we use n = n = 2, kmax = 8. |