Learning representations for binary-classification without backpropagation
Authors: Mathias Lechner
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we experimentally evaluate the learning performance of m DFA on a set of empirical benchmarks. We aim to answer the following two questions: How well does m DFA perform compared to DFA, FA, and backpropagation in natural conditions, i.e., in binary classification tasks, and how much does the performance of m DFA degrade in multi-class classification tasks? |
| Researcher Affiliation | Academia | Mathias Lechner IST Austria Am Campus 1, Klosterneuburg, Austria mlechner@ist.ac.at |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | We make an efficient Tensor Flow implementation of all tested algorithms publicly available1 |
| Open Datasets | Yes | We created a series of binary classification benchmarks by randomly sampling two classes from the well-studied CIFAR-100 and Image Net datasets. |
| Dataset Splits | Yes | Secondly, for each method, we tuned the hyperparameters on a separate validation set and selected the best performing configuration to be evaluated on the test data. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instances) were mentioned for the experiments. |
| Software Dependencies | No | We make an efficient Tensor Flow implementation of all tested algorithms publicly available1 (Explanation: While TensorFlow is mentioned, no specific version number or other software dependencies with versions are provided.) |
| Experiment Setup | Yes | For all training methods, we fixed the batch size to 64, applied no regularization, no normalization, and no data augmentation. Optimizer, i.e., { Vanilla Gradient Descent, Adam (Kingma & Ba, 2014), Rmsprop (Tieleman & Hinton, 2012) }, learning rate, training epochs, and weight initialization method were tuned on the validation set. We tested three different weight initialization schemes; all zeros, a scaled uniform, and a normal distribution. |