Topology and Geometry of Half-Rectified Network Optimization
Authors: C. Daniel Freeman, Joan Bruna
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 3 presents our path discovery algorithm and Section 4 covers the numerical experiments. For our numerical experiments, we calculated normalized geodesic lengths for a variety of regression and classification tasks. |
| Researcher Affiliation | Academia | C. Daniel Freeman Department of Physics University of California at Berkeley Berkeley, CA 94720, USA daniel.freeman@berkeley.edu Joan Bruna Courant Institute of Mathematical Sciences New York University New York, NY 10011, USA bruna@cims.nyu.edu |
| Pseudocode | Yes | Algorithm 1 Greedy Dynamic String Sampling |
| Open Source Code | Yes | For more complete architecture and implementation details, see our Git Hub page. |
| Open Datasets | Yes | Our algorithm uses dynamic programming and can be efficiently deployed to study mid-scale CNN architectures on MNIST, CIFAR-10 and RNN models on Penn Treebank next word prediction. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation dataset splits, such as percentages or sample counts, distinct from the test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We studied a 1-4-4-1 fully connected multilayer perceptron style architecture with sigmoid nonlinearities and RMSProp/ADAM optimization. |