Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Sylvain, Tristan"'
Autor:
Bialokozowicz, Lilian W., Le, Hoang M., Sylvain, Tristan, Forsyth, Peter A. I., Nagisetty, Vineel, Mori, Greg
This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis. OPSurv utilizes the in
Externí odkaz:
http://arxiv.org/abs/2402.01955
Machine-based prediction of real-world events is garnering attention due to its potential for informed decision-making. Whereas traditional forecasting predominantly hinges on structured data like time-series, recent breakthroughs in language models
Externí odkaz:
http://arxiv.org/abs/2310.01880
In recent years, the introduction of self-supervised contrastive learning (SSCL) has demonstrated remarkable improvements in representation learning across various domains, including natural language processing and computer vision. By leveraging the
Externí odkaz:
http://arxiv.org/abs/2306.12086
Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions. Recent research has also explored the integration of reinforcement learning (RL) into these models, i
Externí odkaz:
http://arxiv.org/abs/2305.18820
Autor:
Fedorov, Alex, Geenjaar, Eloy, Wu, Lei, Sylvain, Tristan, DeRamus, Thomas P., Luck, Margaux, Misiura, Maria, Hjelm, R Devon, Plis, Sergey M., Calhoun, Vince D.
Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameterized models.However, a single modality provides only a limited view of
Externí odkaz:
http://arxiv.org/abs/2209.02876
The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that can be applied to the state-of-the-art transformer-based time series for
Externí odkaz:
http://arxiv.org/abs/2206.04038
Autor:
Sylvain, Tristan
L'utilisation d'architectures neuronales profondes associée à des innovations spécifiques telles que les méthodes adversarielles, l’entraînement préalable sur de grands ensembles de données et l'estimation de l'information mutuelle a permis,
Autor:
Fedorov, Alex, Geenjaar, Eloy, Wu, Lei, Sylvain, Tristan, DeRamus, Thomas P., Luck, Margaux, Misiura, Maria, Mittapalle, Girish, Hjelm, R. Devon, Plis, Sergey M., Calhoun, Vince D.
Publikováno v:
In NeuroImage January 2024 285
Autor:
Fedorov, Alex, Sylvain, Tristan, Geenjaar, Eloy, Luck, Margaux, Wu, Lei, DeRamus, Thomas P., Kirilin, Alex, Bleklov, Dmitry, Calhoun, Vince D., Plis, Sergey M.
Sensory input from multiple sources is crucial for robust and coherent human perception. Different sources contribute complementary explanatory factors. Similarly, research studies often collect multimodal imaging data, each of which can provide shar
Externí odkaz:
http://arxiv.org/abs/2012.13623
Autor:
Fedorov, Alex, Wu, Lei, Sylvain, Tristan, Luck, Margaux, DeRamus, Thomas P., Bleklov, Dmitry, Plis, Sergey M., Calhoun, Vince D.
Introspection of deep supervised predictive models trained on functional and structural brain imaging may uncover novel markers of Alzheimer's disease (AD). However, supervised training is prone to learning from spurious features (shortcut learning)
Externí odkaz:
http://arxiv.org/abs/2012.13619