Matrix Inference and Estimation in Multi-Layer Models
Authors: Parthe Pandit, Mojtaba Sahraee Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Experiments We consider the problem of learning the input layer of a two layer neural network as described in Section 2.3. ... The normalized test error for ADAM-MAP, ML-Mat-VAMP and the ML-Mat-VAMP SE are plotted in Fig. 2. |
| Researcher Affiliation | Academia | Parthe Pandit Dept. ECE UC, Los Angeles EMAIL Mojtaba Sahraee-Ardakan Dept. ECE UC, Los Angeles EMAIL Sundeep Rangan Dept. ECE NYU EMAIL Philip Schniter Dept. ECE The Ohio State Univ. EMAIL Alyson K. Fletcher Dept. Statistics UC, Los Angeles EMAIL |
| Pseudocode | Yes | Algorithm 1 Multilayer Matrix VAMP (ML-Mat-VAMP) |
| Open Source Code | Yes | Code available at https://github.com/parthe/ML-Mat-VAMP. |
| Open Datasets | No | The paper describes synthetic data generation ('The weight vectors F1 and F2 are generated as i.i.d. Gaussians with zero mean and unit variance. The input X is also i.i.d. Gaussians with variance 1/Nin so that the average pre-activation has unit variance.') but does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper states 'We generate 1000 test samples and a variable number of training samples that ranges from 200 to 4000.' but does not explicitly provide details about a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'ADAM optimizer [21] in the Keras package of Tensorflow' but does not specify version numbers for Keras or Tensorflow. |
| Experiment Setup | Yes | Our experiment take d = 4 hidden units, Nin = 100 input units, Nout = 1 output unit, sigmoid activations and variable number of samples N. The ADAM algorithm is run for 100 epochs with a learning rate = 0.01. Algorithm 1 run for 20 iterations |