Search Results for author:
Found 8 papers, 1 papers with code
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning
Our results demonstrate that Mafin significantly enhances the performance of the black-box embeddings by only requiring the training of a small augmented model.
Active Preference Learning for Large Language Models
A key consideration for aligning these models is how to most effectively use human resources, or model resources in the case where LLMs themselves are used as oracles.
Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and statistics applications.
Integrated Weak Learning
We introduce a label model that can learn to aggregate weak supervision sources differently for different datapoints and takes into consideration the performance of the end-model during training.
Generalization Gap in Amortized Inference
The ability of likelihood-based probabilistic models to generalize to unseen data is central to many machine learning applications such as lossless compression.
Sample Efficient Model Evaluation
Labelling data is a major practical bottleneck in training and testing classifiers.
Estimating the Uncertainty of Neural Network Forecasts for Influenza Prevalence Using Web Search Activity
In this paper, we demonstrate how Bayesian Neural Networks (BNNs) can be used to both provide a forecast and a corresponding uncertainty without significant loss in forecasting accuracy compared to traditional NNs.
Spread Divergence
For distributions $\mathbb{P}$ and $\mathbb{Q}$ with different supports or undefined densities, the divergence $\textrm{D}(\mathbb{P}||\mathbb{Q})$ may not exist.
