24 papers · ranked by Valyu relevance
Laurent Valentin Jospin, Hamid Laga, Farid Boussaïd, Wray Buntine + 1 more
'Mohammed Bennamoun'] Abstract—Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify. Bayesian statistics offer a…
Meet P. Vadera, Benjamin M. Marlin
Approximate Bayesian deep learning methods hold significant promise for addressing several issues that occur when deploying deep learning components in intelligent systems, including mitigating the occurrence of overconfident errors and providing enhanced robustness to out of distribution examples. However, the…
Wenlong Chen, Bolian Li, Ruqi Zhang, Yingzhen Li
Bayesian computation has achieved profound success in many modeling tasks with statistics tools such as generalized linear models (Dobson and Barnett, 2018; Nelder and Wedderburn, 1972). Yet these traditional tools fail to produce satisfactory predictions for high-dimensional and highly complex data such as images…
Filippo Bargagna, Lisa Anita De Santi, Nicola Martini, Dario Genovesi + 6 more
'Dario Genovesi' 'Brunella Favilli' 'Giuseppe Vergaro' 'Michele Emdin' 'Assuero Giorgetti' 'Vincenzo Positano' 'Maria Filomena Santarelli'] Deep neural networks (DNNs) have already impacted the field of medicine in data analysis, classification, and image processing. Unfortunately, their performance is drastically…
Dimitrije Marković, Karl Friston, Stefan J. Kiebel
Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and…
Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy
The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case, and that in fact these systems offer new opportunities for Bayesian modeling.…
Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad + 3 more
'Amr Yousef' 'Tagrid Abdullah N. Alshalali' 'Zaffar Ahmed Shaikh'] Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it…
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engineering has been thus far mostly driven by the goal of implementing energy-efficient machines that take inspiration from the time-based…
Malte Blattmann, Adrian Lindenmeyer, Stefan Franke, Thomas Neumuth + 2 more
'Daniel Schneider' 'Vinod Kumar Chauhan'] Deep learning models offer transformative potential for personalized medicine by providing automated, data-driven support for complex clinical decision-making. However, their reliability degrades on out-of-distribution inputs, and traditional point-estimate predictors can give…
Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, Vincent Fortuin
'Vincent Fortuin'] > Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence in predictions, lack of interpretability, and vulnerability to adversarial attacks. To address these challenges…
Djohan Bonnet, Tifenn Hirtzlin, Atreya Majumdar, Thomas Dalgaty + 9 more
'Eduardo Esmanhotto' 'Valentina Meli' 'Niccolo Castellani' 'Simon Martin' 'Jean-François Nodin' 'Guillaume Bourgeois' 'Jean-Michel Portal' 'Damien Querlioz' 'Elisa Vianello'] Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Ghada Atteia, Amel A. Alhussan, Nagwan Abdel Samee, Zongyuan Ge + 3 more
'Donghao Zhang' 'Deval Mehta' 'Dwarikanath Mahapatra'] Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial…
Patrick Kwabena Mensah, Mighty Abra Ayidzoe, Alex Akwasi Opoku, Kwabena Adu + 3 more
'Kwabena Adu' 'Benjamin Asubam Weyori' 'Isaac Kofi Nti' 'Peter Nimbe'] Capsule Networks have shown great promise in image recognition due to their ability to recognize the pose, texture, and deformation of objects and object parts. However, the majority of the existing capsule networks are deterministic with limited…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Gaspard Oliviers, Rafal Bogacz, Alexander Meulemans
It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis has been formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, predictive coding theory proposed how the probabilistic models can be learned by…
Jonas Verhellen
In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 12, 3567-3572] have, however, been shown to rival deep learning…
Ammon Thompson, Benjamin Liebeskind, Erik J. Scully, Michael Landis
Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are computationally expensive, which limits the complexity and realism of phylodynamic…
Derek van Tilborg, Francesca Grisoni
Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, e.g., in terms of size or molecular diversity. Active deep learning has an untapped potential for low-data drug discovery, as it allows to improve a model iteratively during the screening…
Anna A. Nagel, Michael J. Landis
Ancestral state reconstruction is a classical problem of broad relevance in phylogenetics. Likelihood-based methods for reconstructing ancestral states under discrete character models, such as Markov models, have proven extremely useful, but only work so long as the assumed model yields a tractable likelihood function.…
Soheil Saghafi, Timothy Rumbell, Viatcheslav Gurev, James Kozloski + 3 more
Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We introduce a…
Ismaël Lajaaiti, Sophia Lambert, Jakub Voznica, Hélène Morlon + 1 more
To infer the processes that gave rise to past speciation and extinction rates across taxa, space and time, we often formulate hypotheses in the form of stochastic diversification models and estimate their parameters from extant phylogenies using Maximum Likelihood or Bayesian inference. Unfortunately, however…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…