Learning to solve the credit assignment problem
Authors: Benjamin James Lansdell, Prashanth Ravi Prakash, Konrad Paul Kording
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In both feedforward and convolutional networks, we empirically show that our approach learns to approximate the gradient, and can match or the performance of exact gradient-based learning. |
| Researcher Affiliation | Academia | Benjamin James Lansdell Department of Bioengineering University of Pennsylvania Pennsylvania, PA 19104 lansdell@seas.upenn.edu Prashanth Ravi Prakash Department of Bioengineering University of Pennsylvania Pennsylvania, PA 19104 Konrad Paul Kording Department of Bioengineering University of Pennsylvania Pennsylvania, PA 19104 |
| Pseudocode | No | No structured pseudocode or algorithm blocks, labeled as 'Pseudocode' or 'Algorithm', were found in the paper. |
| Open Source Code | Yes | Code to reproduce these results can be found at: https://github.com/benlansdell/synthfeedback |
| Open Datasets | Yes | MNIST input data |
| Dataset Splits | No | The paper mentions using training and test sets (e.g., 'Dashed lines represent training loss, solid lines represent test loss' in Figure 3), and standard datasets like MNIST and CIFAR, but it does not explicitly provide specific percentages, sample counts, or detailed methodologies for training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or cloud computing instances with specifications) used to run the experiments. |
| Software Dependencies | No | The paper states 'All code is implemented in Tensor Flow.' but does not specify a version number for TensorFlow or any other software dependencies with their versions. |
| Experiment Setup | Yes | Details of each task and parameters are provided here. All code is implemented in Tensor Flow. C.1 FIGURE 2: Networks are 784-50-20-10 with an MSE loss function. A sigmoid non-linearity is used. A batch size of 32 is used. B is updated using synthetic gradient updates with learning rate η = 0.0005, W is updated with learning rate 0.0004, standard deviation of noise is 0.01. |