Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Hasanzadeh, Arman"'
Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes. Despite providing well-calibrated predictions, especially in regression problems, and quick adaptation to new
Externí odkaz:
http://arxiv.org/abs/2305.18777
Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical chall
Externí odkaz:
http://arxiv.org/abs/2203.08149
Autor:
Hasanzadeh, Arman, Armandpour, Mohammadreza, Hajiramezanali, Ehsan, Zhou, Mingyuan, Duffield, Nick, Narayanan, Krishna
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data. Despite their success, existing graph contrastive learning methods are incapable of uncertainty quantification for node representations
Externí odkaz:
http://arxiv.org/abs/2112.07823
Autor:
Hajiramezanali, Ehsan, Hasanzadeh, Arman, Duffield, Nick, Narayanan, Krishna R, Qian, Xiaoning
Publikováno v:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying molecular interactions across different data types helps reveal signal
Externí odkaz:
http://arxiv.org/abs/2010.05895
Autor:
Hasanzadeh, Arman, Hajiramezanali, Ehsan, Boluki, Shahin, Zhou, Mingyuan, Duffield, Nick, Narayanan, Krishna, Qian, Xiaoning
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs. The proposed framework not only alleviates over-smoothing and over-fitting
Externí odkaz:
http://arxiv.org/abs/2006.04064
Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing enormous chemical sp
Externí odkaz:
http://arxiv.org/abs/2004.07782
Autor:
Hajiramezanali, Ehsan, Hasanzadeh, Arman, Duffield, Nick, Narayanan, Krishna, Zhou, Mingyuan, Qian, Xiaoning
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models. However, the majority of existing methods have limited exp
Externí odkaz:
http://arxiv.org/abs/1910.12819
Autor:
Hajiramezanali, Ehsan, Hasanzadeh, Arman, Duffield, Nick, Narayanan, Krishna R, Zhou, Mingyuan, Qian, Xiaoning
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we develop a novel hierarchical variational model that introduces additional
Externí odkaz:
http://arxiv.org/abs/1908.09710
Autor:
Hasanzadeh, Arman, Hajiramezanali, Ehsan, Duffield, Nick, Narayanan, Krishna R., Zhou, Mingyuan, Qian, Xiaoning
Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a hierarchical variational framework to enable neighboring node sharing for be
Externí odkaz:
http://arxiv.org/abs/1908.07078
In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network. While most of the works in literature impose $\ell_1$ regularization, DropOut or DropConnect techniques t
Externí odkaz:
http://arxiv.org/abs/1907.02051