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Found 10 papers, 1 papers with code
To Aggregate or Not? Learning with Separate Noisy Labels
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e. g., via crowdsourcing).
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking
Research Replication Prediction (RRP) is the task of predicting whether a published research result can be replicated or not.
Compressed Predictive Information Coding
The key insight of our framework is to learn representations by minimizing the compression complexity and maximizing the predictive information in latent space.
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limited.
Research Replication Prediction Using Weakly Supervised Learning
In this paper, we propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets.
Machine Truth Serum
In this paper, we extend the idea proposed in Bayesian Truth Serum that "a surprisingly more popular answer is more likely the true answer" to classification problems.
Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test
Recent progress in neural learning demonstrated that machines can do well in regularized tasks, e. g., the game of Go.
Chinese Song Iambics Generation with Neural Attention-based Model
Learning and generating Chinese poems is a charming yet challenging task.
Stochastic Top-k ListNet
ListNet is a well-known listwise learning to rank model and has gained much attention in recent years.
Learning from LDA using Deep Neural Networks
Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian model for topic inference.
