A Unified Approach to Count-Based Weakly Supervised Learning
Authors: Vinay Shukla, Zhe Zeng, Kareem Ahmed, Guy Van den Broeck
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on three common weakly-supervised learning paradigms and observe that our proposed approach achieves state-of-the-art or highly competitive results across all three of the paradigms. |
| Researcher Affiliation | Academia | Vinay Shukla Department of Computer Science University of California, Los Angeles vshukla@g.ucla.eduZhe Zeng Department of Computer Science University of California, Los Angeles zhezeng@cs.ucla.eduKareem Ahmed Department of Computer Science University of California, Los Angeles ahmedk@cs.ucla.eduGuy Van den Broeck Department of Computer Science University of California, Los Angeles guyvdb@cs.ucla.edu |
| Pseudocode | Yes | Algorithm 1 Count Probability p(Pk i=1 ˆyi = s) |
| Open Source Code | Yes | 1Code and experiments are available at https://github.com/UCLA-Star AI/Count Loss |
| Open Datasets | Yes | We first experiment on the MNIST dataset [30] and follow the MIL experimental setting in Ilse et al. [24]... We also experiment on the Colon Cancer dataset [41]... We experiment on dataset MNIST and CIFAR10 [29]... Publicly available at archive.ics.uci.edu/ml |
| Dataset Splits | No | The paper mentions training and testing, and that it chooses the "single best epoch based on validation for our approaches" in Table 6. However, it does not explicitly provide numerical splits (percentages or counts) for training, validation, and test sets. It implies a validation set was used for hyperparameter tuning but doesn't quantify its size or proportion. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper refers to a PyTorch framework (Pylon) in its related work, but does not specify the versions of any software dependencies used for its own experiments. |
| Experiment Setup | No | The paper states: "We refer the readers to the appendix for additional experimental details." However, the provided text does not include these details, such as hyperparameters or specific training configurations in the main body. |