Search Results for author:
Found 14 papers, 10 papers with code
VL-ICL Bench: The Devil in the Details of Benchmarking Multimodal In-Context Learning
Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, reasoning, and grounding.
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs.
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
We demonstrate empirically that FairTune leads to improved fairness on a range of medical imaging datasets.
Label Calibration for Semantic Segmentation Under Domain Shift
Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain.
Feed-Forward Source-Free Domain Adaptation via Class Prototypes
Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data.
Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks
Enhancing the generalisation abilities of neural networks (NNs) through integrating noise such as MixUp or Dropout during training has emerged as a powerful and adaptable technique.
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction.
Fairness in AI and Its Long-Term Implications on Society
Successful deployment of artificial intelligence (AI) in various settings has led to numerous positive outcomes for individuals and society.
Feed-Forward Latent Domain Adaptation
Recognizing that device's data are likely to come from multiple latent domains that include a mixture of unlabelled domain-relevant and domain-irrelevant examples, we focus on the comparatively under-studied problem of latent domain adaptation.
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly to run.
A Channel Coding Benchmark for Meta-Learning
We use our MetaCC benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift, which can be controlled in the coding problem.
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters.
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error
The problem is especially noticeable when using modern neural networks, for which there is a significant difference between the confidence of the model and the probability of correct prediction.
Flexible Dataset Distillation: Learn Labels Instead of Images
In particular, we study the problem of label distillation - creating synthetic labels for a small set of real images, and show it to be more effective than the prior image-based approach to dataset distillation.
