Zobrazeno 1 - 10
of 139
pro vyhledávání: '"van Dijk, David P."'
Autor:
He, Sizhuang, Levine, Daniel, Vrkic, Ivan, Bressana, Marco Francesco, Zhang, David, Rizvi, Syed Asad, Zhang, Yangtian, Zappala, Emanuele, van Dijk, David
We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large language models (LLMs) for continuous data generation. CaLMFlow enables
Externí odkaz:
http://arxiv.org/abs/2410.05292
Autor:
Zhang, Shiyang, Patel, Aakash, Rizvi, Syed A, Liu, Nianchen, He, Sizhuang, Karbasi, Amin, Zappala, Emanuele, van Dijk, David
We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (
Externí odkaz:
http://arxiv.org/abs/2410.02536
Deep neural networks, despite their success in numerous applications, often function without established theoretical foundations. In this paper, we bridge this gap by drawing parallels between deep learning and classical numerical analysis. By framin
Externí odkaz:
http://arxiv.org/abs/2310.01618
Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. However, a limitation of trans
Externí odkaz:
http://arxiv.org/abs/2301.13338
Autor:
Rizvi, Syed Asad, Pallikkavaliyaveetil, Nazreen, Zhang, David, Lyu, Zhuoyang, Nguyen, Nhi, Lyu, Haoran, Christensen, Benjamin, Caro, Josue Ortega, Fonseca, Antonio H. O., Zappala, Emanuele, Bagherian, Maryam, Averill, Christopher, Abdallah, Chadi G., Karbasi, Amin, Ying, Rex, Brbic, Maria, Dhodapkar, Rahul Madhav, van Dijk, David
Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstructured data, making the development of graph foundation models challen
Externí odkaz:
http://arxiv.org/abs/2210.09475
Autor:
Zappala, Emanuele, Fonseca, Antonio Henrique de Oliveira, Caro, Josue Ortega, Moberly, Andrew Henry, Higley, Michael James, Cardin, Jessica, van Dijk, David
Publikováno v:
Nat Mach Intell (2024)
Nonlinear operators with long distance spatiotemporal dependencies are fundamental in modeling complex systems across sciences, yet learning these nonlocal operators remains challenging in machine learning. Integral equations (IEs), which model such
Externí odkaz:
http://arxiv.org/abs/2209.15190
Autor:
Zappala, Emanuele, Fonseca, Antonio Henrique de Oliveira, Moberly, Andrew Henry, Higley, Michael James, Abdallah, Chadi, Cardin, Jessica, van Dijk, David
Modeling continuous dynamical systems from discretely sampled observations is a fundamental problem in data science. Often, such dynamics are the result of non-local processes that present an integral over time. As such, these systems are modeled wit
Externí odkaz:
http://arxiv.org/abs/2206.14282
Understanding the space of probability measures on a metric space equipped with a Wasserstein distance is one of the fundamental questions in mathematical analysis. The Wasserstein metric has received a lot of attention in the machine learning commun
Externí odkaz:
http://arxiv.org/abs/2010.05820
A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning to study the biology of SARS-CoV-2 infection and
Externí odkaz:
http://arxiv.org/abs/2006.12971
Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification and link prediction. In recent years, there has been a lot of work inc
Externí odkaz:
http://arxiv.org/abs/2007.04777