Zobrazeno 1 - 10
of 4 396
pro vyhledávání: '"Tim, G."'
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 best possible pre-training for as-yet-unknown, lightly supervised downs
Externí odkaz:
http://arxiv.org/abs/2411.15931
Autor:
Abel, Elliott, Steindl, Andrew J., Mazioud, Selma, Schueler, Ellie, Ogundipe, Folu, Zhang, Ellen, Grinspan, Yvan, Reimann, Kristof, Crevasse, Peyton, Bhaskar, Dhananjay, Viswanath, Siddharth, Zhang, Yanlei, Rudner, Tim G. J., Adelstein, Ian, Krishnaswamy, Smita
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 neural networks. We learn manifolds where datapoints are neural networ
Externí odkaz:
http://arxiv.org/abs/2411.12626
Autor:
Yang, Qidong, Zhu, Weicheng, Keslin, Joseph, Zanna, Laure, Rudner, Tim G. J., Fernandez-Granda, Carlos
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 desirable to quantify the uncertainty associated with the prediction (in
Externí odkaz:
http://arxiv.org/abs/2410.23272
Autor:
Sun, Xingzhi, Liao, Danqi, MacDonald, Kincaid, Zhang, Yanlei, Liu, Chen, Huguet, Guillaume, Wolf, Guy, Adelstein, Ian, Rudner, Tim G. J., Krishnaswamy, Smita
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 also presents unique computational and statistical challenges. Traditi
Externí odkaz:
http://arxiv.org/abs/2410.12779
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 approach, excelling at unconditional sample generation and, with data-dr
Externí odkaz:
http://arxiv.org/abs/2407.11942
Autor:
Gupta, Gunshi, Yadav, Karmesh, Gal, Yarin, Batra, Dhruv, Kira, Zsolt, Lu, Cong, Rudner, Tim G. J.
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-specific data. This has led to the emergence of pre-trained vision-la
Externí odkaz:
http://arxiv.org/abs/2405.05852
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) are inherently robust to adversarial perturbations. In this work, we
Externí odkaz:
http://arxiv.org/abs/2404.19640
Machine learning models often perform poorly under subpopulation shifts in the data distribution. Developing methods that allow machine learning models to better generalize to such shifts is crucial for safe deployment in real-world settings. In this
Externí odkaz:
http://arxiv.org/abs/2403.09869
Autor:
Papamarkou, Theodore, Skoularidou, Maria, Palla, Konstantina, Aitchison, Laurence, Arbel, Julyan, Dunson, David, Filippone, Maurizio, Fortuin, Vincent, Hennig, Philipp, Hernández-Lobato, José Miguel, Hubin, Aliaksandr, Immer, Alexander, Karaletsos, Theofanis, Khan, Mohammad Emtiyaz, Kristiadi, Agustinus, Li, Yingzhen, Mandt, Stephan, Nemeth, Christopher, Osborne, Michael A., Rudner, Tim G. J., Rügamer, David, Teh, Yee Whye, Welling, Max, Wilson, Andrew Gordon, Zhang, Ruqi
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 datasets. However, a broader perspective reveals a multitude of overlooke
Externí odkaz:
http://arxiv.org/abs/2402.00809
Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved challenging in practice. Addressing the drawbacks of existing techniques, w
Externí odkaz:
http://arxiv.org/abs/2312.17210