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..
Contrastive Learning with Hard Negative Samples
Authors: Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our hard negative sampling strategy improves the downstream task performance for image, graph and text data. |
| Researcher Affiliation | Academia | Massachusetts Institute of Technology Cambridge, MA, USA EMAIL |
| Pseudocode | Yes | Py Torch-style pseudocode for the objective is given in Fig. 13 in Appendix D. |
| Open Source Code | Yes | Code available at: https://github.com/joshr17/HCL |
| Open Datasets | Yes | We begin by testing the hard sampling method on vision tasks using the STL10, CIFAR100 and CIFAR10 data. |
| Dataset Splits | Yes | Each embedding is evaluated using the average accuracy 10-fold cross-validation using an SVM as the classifier |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, or memory specifications) were provided for the experimental setup. |
| Software Dependencies | No | The paper mentions software like Py Torch and the Adam optimizer, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For all experiments β is treated as a hyper-parameter (see ablations in Fig. 2 for more understanding of how to pick β). Values for M and τ + must also be determined. We fix M = 1 for all experiments...all models are trained for 400 epochs. We use the Adam optimizer (Kingma & Ba, 2015) with learning rate 0.001 and weight decay 10-6. Each model is trained for 200 epochs, with batch size 128 using the Adam optimizer (Kingma & Ba, 2015). with learning rate 0.001, and weight decay of 10-6. |