On Blackbox Backpropagation and Jacobian Sensing
Authors: Krzysztof M. Choromanski, Vikas Sindhwani
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate efficient backpropagation through noisy blackbox layers in a deep neural net, improved data-efficiency in the task of linearizing the dynamics of a rigid body system, and the generic ability to handle a rich class of input-output dependency structures in Jacobian estimation problems. (...) 4 Experiments (...) 4.1. Sparse Jacobian Recovery: (...) 4.2. Training Convolutional Neural Networks with Blackbox Nodes: (...) 4.3. Jacobian of manipulator dynamics: |
| Researcher Affiliation | Industry | Krzysztof Choromanski Google Brain New York, NY 10011 kchoro@google.com Vikas Sindhwani Google Brain New York, NY 10011 sindhwani@google.com |
| Pseudocode | No | The paper describes algorithmic steps (e.g., ADMM iterations) in text and equations, but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing their source code for the work described, nor does it provide a direct link to a code repository for their methodology. |
| Open Datasets | Yes | We introduce a blackbox layer between the convolutional layers and the fully connected layers of a standard MNIST convnet. |
| Dataset Splits | No | The paper mentions 'training and validation error' and 'validation error' but does not provide specific percentages or sample counts for dataset splits (e.g., 80/10/10 split) or reference predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, processor types, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Tensor Flow [5]', 'Torch [2]', and 'MIT’s Drake planning and control toolbox [25]', but does not provide specific version numbers for any of these components. |
| Experiment Setup | Yes | The blackbox node is a standard Re LU layer that takes as input 32-dimensional vectors, 32 32-sized weight matrix and a bias vector of length 32, and outputs a 32 dimensional representation. The minibatch size is 16. We inject truncated Gaussian noise in the output of the layer and override its default gradient operator in Tensor Flow with our LP-based rainbow procedure. We use Gaussian perturbation directions and sample measurements by forward evaluation calls to the Tensor Flow Op inside our custom blackbox gradient operator. |