Overall - Test

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (34 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
  29 Jul 2020 3 repositories listed
  There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans.
• FreeLB: Enhanced Adversarial Training for Natural Language Understanding
  25 Sep 2019 2 repositories listed
  Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models.
• Deep Modeling and Optimization of Medical Image Classification
  29 May 2025 1 repository listed
  To face these challenges, we considered three scenarios: 1) we introduce a novel CLIP variant using four CNNs and eight ViTs as image encoders for the classification of brain cancer and skin cancer, 2) we combine 12…
• Efficient Training of Deep Neural Operator Networks via Randomized Sampling
  20 Sep 2024 1 repository listed
  The proposed random sampling over the inputs of the trunk net mitigates these challenges, improving generalization and reducing memory requirements during training, resulting in significant computational gains.
• WATT: Weight Average Test-Time Adaptation of CLIP
  19 Jun 2024 1 repository listed Syntology ran 5 of 7 samples · 2 unverified
  In response, we present Weight Average Test-Time Adaptation (WATT) of CLIP, a pioneering approach facilitating full test-time adaptation (TTA) of this VLM.
• Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning
  21 Oct 2023 1 repository listed Syntology ran 5 of 7 samples · 2 unverified · 7 pointer-only (licence)
  Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e.
• Transferable Availability Poisoning Attacks
  8 Oct 2023 1 repository listed
  We consider availability data poisoning attacks, where an adversary aims to degrade the overall test accuracy of a machine learning model by crafting small perturbations to its training data.
• Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models
  24 May 2023 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs.
• Amplifying Membership Exposure via Data Poisoning
  1 Nov 2022 1 repository listed
  In this paper, we investigate the third type of exploitation of data poisoning - increasing the risks of privacy leakage of benign training samples.
• Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
  6 Apr 2022 1 repository listed
  We train a neural model with this feedback data that can generate explanations and re-score answer candidates.
• Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
  10 Oct 2020 1 repository listed
  We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.