Statistical mechanics of low-rank tensor decomposition
Authors: Jonathan Kadmon, Surya Ganguli
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theory reveals the existence of phase transitions between easy, hard and impossible inference regimes, and displays an excellent match with simulations. Finally, we compare our AMP algorithm to the most commonly used algorithm, alternating least squares (ALS), and demonstrate that AMP significantly outperforms ALS in the presence of noise. |
| Researcher Affiliation | Collaboration | Jonathan Kadmon Department of Applied Physics, Stanford University kadmonj@stanford.edu Surya Ganguli Department of Applied Physics, Stanford University and Google Brain, Mountain View, CA sganguli@stanford.edu |
| Pseudocode | No | The paper presents iterative update equations (11)-(14) but does not format them within a distinct pseudocode block or algorithm environment. |
| Open Source Code | Yes | Code to reproduce all simulations presented in this paper is available at https://github.com/ganguli-lab/tensor AMP. |
| Open Datasets | No | The paper mentions generating "order-3 tensors generated randomly according to (2)" for simulations, implying synthetic data without providing access information or formal citations for a publicly available dataset used for training, validation, or testing. |
| Dataset Splits | No | The paper describes numerical simulations but does not specify train/validation/test dataset splits, percentages, or sample counts for experimental reproduction. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, cloud instance types) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions) that are critical for reproducing the experiments. |
| Experiment Setup | Yes | Figure 2.A caption: "[σα = 1, µ1 = µ2 = 0.1 (blue), µ3 = 0.3 (orange), N = 500, nα = 1]". Figure 3 caption: "[p = 3, r = 1, σα = 1, µα = 0.2, nα = {1, 8/8 }, N = 500 ]" |