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..
Long-Tailed Learning Requires Feature Learning
Authors: Thomas Laurent, James von Brecht, Xavier Bresson
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, we investigate empirically a few questions that we couldn t resolve analytically. In particular, our error bounds are restricted to the case in which a nearest neighbor classification rule is applied on the top of the features we provide empirical evidence in this last section that replacing the nearest neighbor classifier by a linear classifier leads to very minimal improvement. |
| Researcher Affiliation | Academia | 1 Loyola Marymount University, EMAIL 2 National University of Singapore, EMAIL |
| Pseudocode | No | The paper describes the neural network architecture textually and provides a diagram (Figure 2), but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/xbresson/Long_Tailed_Learning_Requires_Feature_Learning. |
| Open Datasets | No | The paper uses a custom-designed data model (described in Section 2) to generate synthetic data for its experiments. It does not use or provide access information for a pre-existing public dataset. |
| Dataset Splits | No | The paper specifies the generation of "A training set containing R nspl sentences" and "A test set containing 10,000 unfamiliar sentences" but does not mention a separate validation set or provide details about how the data is split for validation. |
| Hardware Specification | No | The paper states "Constructing each of these Gram matrices takes a few days on CPU" but does not specify any particular CPU model, GPU models, memory, or other detailed hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using "SVC function of Scikit-learn Pedregosa et al. (2011), which itself relies on the LIBSVM library Chang & Lin (2011)". While it names the software, it does not provide specific version numbers for Scikit-learn or LIBSVM that were used in their experiments. |
| Experiment Setup | Yes | MLP 1: din = 150, dhidden = 500, dout = 10, MLP 2: din = 90, dhidden = 2000, dout = 1000... The learning rate is set to 0.01 (constant learning rate), and the batch size to 100... We chose C = 1... the parameter γ involved in the definition of the kernel was set to γ = 0.25 when n {1, 2} and to γ = 0.1 when n {3, 4, 5}. |