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Found 10 papers, 4 papers with code
Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN
The power and the potential of deep learning models attract many researchers to design advanced and sophisticated architectures.
Multi Label Text Classification
Multi-Label Text Classification
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Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models
Today, large language models (LLMs) are taught to use new tools by providing a few demonstrations of the tool's usage.
Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance
Score-based generative models (SGMs) are a popular family of deep generative models that achieve leading image generation quality.
Linear Classifier: An Often-Forgotten Baseline for Text Classification
Large-scale pre-trained language models such as BERT are popular solutions for text classification.
TSMixer: An All-MLP Architecture for Time Series Forecasting
Extending them, in this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), a novel architecture designed by stacking multi-layer perceptrons (MLPs).
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration
To improve GAN in terms of model compatibility, we propose Boundary-Calibration GANs (BCGANs), which leverage the boundary information from a set of pre-trained classifiers using the original data.
A Unified View of cGANs with and without Classifiers
Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions.
Parameter Selection: Why We Should Pay More Attention to It
In the topic of multi-label classification for medical code prediction, one influential paper conducted a proper parameter selection on a set, but when moving to a subset of frequently occurring labels, the authors used the same parameters without a separate tuning.
Active Deep Q-learning with Demonstration
In this work, we propose Active Reinforcement Learning with Demonstration (ARLD), a new framework to streamline RL in terms of demonstration efforts by allowing the RL agent to query for demonstration actively during training.
libact: Pool-based Active Learning in Python
libact is a Python package designed to make active learning easier for general users.
