Approximate Feature Collisions in Neural Nets
Authors: Ke Li, Tianhao Zhang, Jitendra Malik
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we will apply our method to two standard neural net architectures trained on the MNIST and Image Net datasets. The trained model achieves a test accuracy of 96.64%. |
| Researcher Affiliation | Academia | Ke Li UC Berkeley ke.li@eecs.berkeley.edu Tianhao Zhang Nanjing University bryanzhang@smail.nju.edu.cn Jitendra Malik UC Berkeley malik@eecs.berkeley.edu |
| Pseudocode | No | The paper describes mathematical formulations and optimization problems but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'https://github.com/aymericdamien/TensorFlow-Examples' in a footnote, but this appears to be a general repository for TensorFlow examples and not the specific code implemented for the methodology described in this paper. |
| Open Datasets | Yes | First we train a simple fully-connected neural network with two hidden layers... on the MNIST dataset. We now perform the same experiment on Image Net. |
| Dataset Splits | No | The paper mentions training on MNIST and Image Net datasets and reports test accuracy, but it does not provide specific details regarding train/validation/test dataset splits (e.g., percentages, sample counts, or splitting methodology). |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications (e.g., specific GPU/CPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'TensorFlow' in a footnote but does not provide specific version numbers for TensorFlow or any other software libraries or dependencies used. |
| Experiment Setup | No | The paper describes neural network architectures used (e.g., 'fully-connected neural network with two hidden layers' for MNIST, 'pre-trained VGG-16 net' for Image Net) but does not provide specific experimental setup details such as hyperparameters (learning rate, batch size, epochs, optimizer settings) or training configurations. |