79 citations · 185 across the 14 of their papers we have counts for
16 papers
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Private PoEtry: Private In-Context Learning via Product of Experts
Rob Romijnders, Mohammad Mahdi Derakhshani, Jonathan Petit +3
In-context learning (ICL) enables Large Language Models (LLMs) to adapt to new tasks with only a small set of examples at inference time, thereby avoiding task-specific fine-tuning…
A Probabilistic Approach to Pose Synchronization for Multi-Reference Alignment with Applications to MIMO Wireless Communication Systems
Rob Romijnders, Gabriele Cesa, Christos Louizos +2
From molecular imaging to wireless communications, the ability to align and reconstruct signals from multiple misaligned observations is crucial for system performance. We study th…
NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation
Rob Romijnders, Stefanos Laskaridis, Ali Shahin Shamsabadi +1
Large Language Models (LLM) are typically trained on vast amounts of data from various sources. Even when designed modularly (e.g., Mixture-of-Experts), LLMs can leak privacy on th…
DNA: Differentially private Neural Augmentation for contact tracing
Rob Romijnders, Christos Louizos, Yuki M. Asano +1
The COVID19 pandemic had enormous economic and societal consequences. Contact tracing is an effective way to reduce infection rates by detecting potential virus carriers early. How…
Protect Your Score: Contact Tracing With Differential Privacy Guarantees
Rob Romijnders, Christos Louizos, Yuki M. Asano +1
The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in the early containment of the virus. Wh…
Beyond Transfer Learning: Co-finetuning for Action Localisation
Anurag Arnab, Xuehan Xiong, Alexey Gritsenko +6
Transfer learning is the predominant paradigm for training deep networks on small target datasets. Models are typically pretrained on large ``upstream'' datasets for classification…
Impact of Aliasing on Generalization in Deep Convolutional Networks
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin +3
We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limi…
Revisiting the Calibration of Modern Neural Networks
Matthias Minderer, Josip Djolonga, Rob Romijnders +5
Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks. Many instances of miscalibration in modern neural networ…
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
Jessica Yung, Rob Romijnders, Alexander Kolesnikov +6
Before deploying machine learning models it is critical to assess their robustness. In the context of deep neural networks for image understanding, changing the object location, ro…
Representation learning from videos in-the-wild: An object-centric approach
Rob Romijnders, Aravindh Mahendran, Michael Tschannen +4
We propose a method to learn image representations from uncurated videos. We combine a supervised loss from off-the-shelf object detectors and self-supervised losses which naturall…
On Robustness and Transferability of Convolutional Neural Networks
Josip Djolonga, Jessica Yung, Michael Tschannen +11
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. However, several recent breakthroughs in transfer learning suggest…
Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision
Panagiotis Meletis, Rob Romijnders, Gijs Dubbelman
Training convolutional networks for semantic segmentation with strong (per-pixel) and weak (per-bounding-box) supervision requires a large amount of weakly labeled data. We propose…
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
Rob Romijnders, Panagiotis Meletis, Gijs Dubbelman
We propose a normalization layer for unsupervised domain adaption in semantic scene segmentation. Normalization layers are known to improve convergence and generalization and are p…
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
Yu Zhao, Rennong Yang, Guillaume Chevalier +2
Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long s…
Applying Deep Learning to Basketball Trajectories
Rajiv Shah, Rob Romijnders
One of the emerging trends for sports analytics is the growing use of player and ball tracking data. A parallel development is deep learning predictive approaches that use vast qua…