Learning on graphs using Orthonormal Representation is Statistically Consistent
Authors: Rakesh Shivanna, Chiranjib Bhattacharyya
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct two sets of experiments. ... Table 1 summarizes the results. Each entry is accuracy in % w.r.t. 0-1 loss, and the results were averaged over 100 iterations. |
| Researcher Affiliation | Academia | Rakesh S Department of Electrical Engineering Indian Institute of Science Bangalore, 560012, INDIA rakeshsmysore@gmail.com Chiranjib Bhattacharyya Department of CSA Indian Institute of Science Bangalore, 560012, INDIA chiru@csa.iisc.ernet.in |
| Pseudocode | Yes | Algorithm 1 Input: U, y S and C > 0. Get α , y S by solving ΛC(K, y S) (2) for ℓhinge and K = U U. Return: ˆy = U h S, where h S = UYα ; Y Dn , Y = yi, if i S, otherwise y i . |
| Open Source Code | No | No explicit statement providing concrete access to source code for the methodology. Footnote 9 mentions 'Relevant resources at: mllab.csa.iisc.ernet.in/rakeshs/nips14' which is too general to confirm code availability. |
| Open Datasets | Yes | We use two datasets similarity matrices from [11] and RBF kernel10 as similarity matrices for the UCI datasets [8]. ... We investigate the recently launched Google dataset [17], which contains multiple views of video game You Tube videos |
| Dataset Splits | No | The paper states '10% of labelled nodes observable' and 'We considered 20% of the data to be labelled' for the input, and 'We choose the parameters λ, λ1 and λ2 by cross validation.' but does not provide specific training/validation/test dataset splits (e.g., exact percentages or sample counts for each partition). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific library versions) needed to replicate the experiment. |
| Experiment Setup | No | The paper states that parameters (λ, λ1, λ2) were chosen by cross-validation, but it does not provide the specific hyperparameter values or other concrete experimental setup details such as learning rates, batch sizes, or optimizer settings. |