Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Network Approximation using Tensor Sketching
Authors: Shiva Prasad Kasiviswanathan, Nina Narodytska, Hongxia Jin
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we experimentally demonstrate the effectiveness of our proposed network approximation approach. Metrics. We deο¬ne compression rate as the ratio between the number of parameters in the reduced (compressed) network architecture and the number of parameters in the original (uncompressed) network architecture. The top-1 error of a trained model is denoted by ERRTOP-1. Datasets. We use 5 popular image datasets: CIFAR10, SVHN, STL10, Image Net10 (a subset of Image Net1000 dataset), and Places2. Network Architectures. We present our experiments on two different network architectures: Network-in-Network [Lin et al., 2014] (Nin N) and Goog Le Net [Szegedy et al., 2015] (which we use for the Places2 dataset). |
| Researcher Affiliation | Industry | Shiva Prasad Kasiviswanathan1 , Nina Narodytska2 and Hongxia Jin3 1 Amazon AWS AI, USA 2 VMware Research, USA 3 Samsung Research America, USA EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that source code for the described methodology is publicly available. |
| Open Datasets | Yes | Datasets. We use 5 popular image datasets: CIFAR10, SVHN, STL10, Image Net10 (a subset of Image Net1000 dataset), and Places2. |
| Dataset Splits | No | The paper mentions training on datasets and reports results on "top-1 error," implying a test set, but it does not explicitly specify the training, validation, and test splits (e.g., "80/10/10 split" or specific sample counts for each split) for its experiments. It does mention "validation" in the context of "batch normalization", but not as a dataset split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions general deep learning frameworks (e.g., "deep learning frameworks [Chetlur et al., 2014]") but does not provide specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper describes aspects of the model architecture (e.g., "stride of 1 and zeropadding of 0" for convolution) but does not provide specific experimental setup details such as hyperparameter values (learning rate, batch size, number of epochs) or optimizer settings. |