Caveats for information bottleneck in deterministic scenarios
Authors: Artemy Kolchinsky, Brendan D. Tracey, Steven Van Kuyk
ICLR 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the three caveats on the MNIST dataset. |
| Researcher Affiliation | Academia | Artemy Kolchinsky & Brendan D. Tracey Santa Fe Institute Santa Fe, NM 87501, USA EMAIL Steven Van Kuyk School of Engineering and Computer Science Victoria University of Wellington, New Zealand EMAIL Dept of Aeronautics & Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA |
| Pseudocode | No | The paper describes the neural network architecture and training process in text, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Tensor Flow code can be found at https://github.com/artemyk/ibcurve . |
| Open Datasets | Yes | We demonstrate the three caveats using the MNIST dataset of hand-written digits. ... This dataset contains a training set of 60,000 images and a test set of 10,000 images, each labeled according to digit. |
| Dataset Splits | Yes | This dataset contains a training set of 60,000 images and a test set of 10,000 images, each labeled according to digit. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Tensor Flow code" and the "Adam algorithm", but does not provide specific version numbers for TensorFlow or any other software libraries or dependencies. |
| Experiment Setup | Yes | The neural network was trained using the Adam algorithm (Kingma & Ba, 2014) with a mini-batch size of 128 and a learning rate of 10 4. ... Training was run for 200 epochs. |