Learning to Pivot with Adversarial Networks
Authors: Gilles Louppe, Michael Kagan, Kyle Cranmer
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically demonstrate the effectiveness of the approach with a toy example and examples from particle physics. |
| Researcher Affiliation | Academia | Gilles Louppe New York University g.louppe@nyu.edu Michael Kagan SLAC National Accelerator Laboratory makagan@slac.stanford.edu Kyle Cranmer New York University kyle.cranmer@nyu.edu |
| Pseudocode | Yes | Algorithm 1 Adversarial training of a classifier f against an adversary r. |
| Open Source Code | Yes | The source code to reproduce the experiments is available online 1. 1https://github.com/glouppe/paper-learning-to-pivot |
| Open Datasets | Yes | We reuse the datasets used in (Baldi et al., 2016a). |
| Dataset Splits | No | The paper mentions 'a subset of 150000 samples for training while AMS is evaluated on an independent test set of 5000000 samples' but does not specify a separate validation set or detailed splits (percentages, counts) for the training data or for the toy example. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory specifications, or types of computing resources used for experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as Python versions, deep learning frameworks (e.g., PyTorch, TensorFlow), or other relevant libraries. |
| Experiment Setup | Yes | The network architecture comprises 2 dense hidden layers of 20 nodes respectively with tanh and Re LU activations, followed by a dense output layer with a single node with a sigmoid activation. ... adversarial training was performed for 200 iterations, mini-batches of size M = 128, K = 500 and λ = 50. ... The architecture of f comprises 3 hidden layers of 64 nodes respectively with tanh, Re LU and Re LU activations, and is terminated by a single final output node with a sigmoid activation. The architecture of r is the same, but uses only Re LU activations in its hidden nodes. |