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..
Hierarchical Class-Based Curriculum Loss
Authors: Palash Goyal, Divya Choudhary, Shalini Ghosh
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our loss function on real world image data sets, and show that it significantly outperforms state-of-the-art baselines. |
| Researcher Affiliation | Industry | 1Samsung Research America 2Amazon Alexa AI EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Class Selection for Hierarchical Class Based Curriculum Learning. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate our loss function on four real world image data sets (i) IMCLEF [Dimitrovski et al., 2011], (ii) Wipo [Rousu et al., 2006], (iii) Reuters [Lewis et al., 2004], and (iv) i Naturalist [Van Horn et al., 2018]. |
| Dataset Splits | Yes | We select the hyperparameters of the neural network using evaluation on a validation set with binary cross entropy loss. |
| Hardware Specification | Yes | We performed our experiments on 2 Nvidia Ge Force RTX 2080 Ti with 12 GB memory with 3.30 GHz CPU clock speed. |
| Software Dependencies | No | The paper mentions using a multi-layer perceptron and ResNet-18, and Adam optimizer, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For evaluation on i Naturalist, we used a Res Net-18 architecture (pre-trained on Image Net). We use Adam optimizer and a learning rate of 10 5. ... Based on this, we get a structure with 800 hidden neurons and a dropout of 0.25. |