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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Anytime Inference with Distilled Hierarchical Neural Ensembles
Authors: Adria Ruiz, Jakob Verbeek9463-9471
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that, compared to previous anytime inference models, HNE provides state-of-the-art accuracy-computation trade-offs on the CIFAR-10/100 and Image Net datasets. |
| Researcher Affiliation | Collaboration | Adria Ruiz1, Jakob Verbeek2 1 Institut de Rob otica i Inform atica Industrial, CSIC-UPC, EMAIL 2 Facebook AI Research, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have released a Pytorch implementation of HNE 1. https://gitlab.com/adriaruizo/dhne-aaai21 |
| Open Datasets | Yes | We experiment with the CIFAR-10/100 (Krizhevsky 2009) and Image Net (Russakovsky et al. 2015) datasets. |
| Dataset Splits | Yes | CIFAR-10/100 contain 50k train and 10k test images from 10 and 100 classes, respectively. Following standard protocols (He et al. 2016), we pre-process the images by normalizing their mean and standard-deviation for each color channel. Additionally, during training we use a data augmentation process where we extract random crops of 32 32 after applying a 4-pixel zero padding to the original image or its horizontal flip. Imagenet is composed by 1.2M and 50k high-resolution images for training and validation, respectively, labelled across 1,000 different categories. |
| Hardware Specification | No | The paper mentions general use of GPUs but does not provide specific hardware details (e.g., specific GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch implementation' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | No | The paper states, 'In the supplementary material we present a detailed description of our HNE implementation using Res Net and Mobile Netv2 and provide all the training hyperparameters,' indicating that specific experimental setup details are not in the main text. |