73 citations · 235 across the 51 of their papers we have counts for
8 papers · 1 filter
Exploring the Manifold of Neural Networks Using Diffusion Geometry
Elliott Abel, Andrew J. Steindl, Selma Mazioud +12
Drawing motivation from the manifold hypothesis, which posits that most high-dimensional data lies on or near low-dimensional manifolds, we apply manifold learning to the space of…
Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization
Deep Chakraborty, Yann LeCun, Tim G. J. Rudner +1
A number of different architectures and loss functions have been applied to the problem of self-supervised learning (SSL), with the goal of developing embeddings that provide the b…
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
Qidong Yang, Weicheng Zhu, Joseph Keslin +3
Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive applications. In these settings, it is often de…
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun, Danqi Liao, Kincaid MacDonald +7
Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportunities for scientific discovery, but it…
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
Leo Klarner, Tim G. J. Rudner, Garrett M. Morris +2
Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials. Diffusion models have recently emerged as a powerful ap…
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control
Gunshi Gupta, Karmesh Yadav, Yarin Gal +4
Embodied AI agents require a fine-grained understanding of the physical world mediated through visual and language inputs. Such capabilities are difficult to learn solely from task…
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis +1
Adversarial examples have been shown to cause neural networks to fail on a wide range of vision and language tasks, but recent work has claimed that Bayesian neural networks (BNNs)…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…