Topology and Geometry of Half-Rectified Network Optimization
Authors: C. Daniel Freeman, Joan Bruna
ICLR 2017 | Venue 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 EMAIL Joan Bruna Courant Institute of Mathematical Sciences New York University New York, NY 10011, USA EMAIL |
| 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. |