Towards Hardware-Aware Tractable Learning of Probabilistic Models
Authors: Laura I. Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental evaluation We empirically evaluate the proposed techniques on a relevant embedded sensing use case: the Human Activity Recognition (HAR) benchmark [1]. Additionally, we show our method s general applicability on a number of other publicly available datasets [8, 15, 21, 26, 31], two of them commonly used for density estimation benchmarks and the rest commonly used for classification (see Table 1).4 |
| Researcher Affiliation | Academia | Electrical Engineering Department, KU Leuven Computer Science Department, KU Leuven Computer Science Department, University of California, Los Angeles {laura.galindez,nimish.shah,marian.verhelst}@esat.kuleuven.be wannes.meert@cs.kuleuven.be, guyvdb@cs.ucla.edu |
| Pseudocode | Yes | Algorithm 1: Scale SI(αk,Fprun,Sprun); Algorithm 2: Prune AC(α,F); Algorithm 3: Get Pareto(σ,acc,cost) |
| Open Source Code | Yes | Code available at https://github.com/laurago894/Hw Aware Prob. |
| Open Datasets | Yes | Human Activity Recognition (HAR) benchmark [1]. Additionally, we show our method s general applicability on a number of other publicly available datasets [8, 15, 21, 26, 31]... For the classification benchmarks... subjected them to a 75%-train, 10%-validation and 15%-test random split. |
| Dataset Splits | Yes | We then binarized them using a one-hot encoding and subjected them to a 75%-train, 10%-validation and 15%-test random split. |
| Hardware Specification | No | The paper states: 'All computation costs for this dataset are normalized according to the energy consumption trends of an embedded ARM M9 CPU, assuming 0.1n J per operation [39].' This refers to the hardware model used for cost estimation, not the specific hardware (e.g., GPU/CPU models) used to run the experiments and generate the reported accuracy figures. |
| Software Dependencies | No | The paper mentions specific algorithms and tools such as 'Learn PSDD algorithm [24]' and 'ACE compiler', but does not provide version numbers for general software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We learned the models on the train and validation sets with the Learn PSDD algorithm [24], using the same settings reported therein, and following the bottomup vtree induction strategy. To populate the model set α, we retained a model after every N/10 iterations, where N is the number of iterations needed for convergence (this is until the log-likelihood on validation data stagnates). |