26 papers · ranked by Valyu relevance
Zaw Myo Hein, Dhanyashri Guruparan, Blaire Okunsai, Che Mohd Nasril Che Mohd Nassir + 4 more
Simple Summary Artificial intelligence (AI) and machine learning (ML), particularly deep learning methods like neural networks and transformers, are revolutionizing biology by analyzing massive amounts genetic and protein data. These technologies enhance the prediction of gene functions, the identification of…
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel + 2 more
Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye. However, conventional optical microscopy faces challenges such as optical aberrations, diffraction-limited resolution, low signal-to-noise ratio…
Sebastian Fischer, Lukas Burk, Carson Zhang, Bernd Bischl + 1 more
Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem. It is built upon the torch package, and simplifies the definition, training, and evaluation of neural networks for both tabular data and…
Ting, Yuan-Sen
Deep learning has generated diverse perspectives in astronomy, with ongoing discussions between proponents and skeptics motivating this review. We examine how neural networks complement classical statistics, extending our data analytical toolkit for modern surveys. Astronomy offers unique opportunities through encoding…
Jamie Simon, Daniel Kunin, Alexander Atanasov, Enric Boix-Adserà + 10 more
In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden representations, final weights, and performance of neural networks. We pull together major strands of ongoing research…
Aristide Tsemo
This paper investigates the foundations of deep learning through insight of geometry, algebra and differential calculus. At is core, artificial intelligence relies on assumption that data and its intrinsic structure can be embedded into vector spaces allowing for analysis through geometric and algebraic methods. We…
Chloe A. Game, Nils Piechaud, Kerry L. Howell
Deep learning (DL) is a powerful tool to extract ecological information from large image datasets efficiently and consistently. However, applying these methods remains challenging, due in part to the complexity of DL workflows and the dynamic nature of available tools. To address this, we created a practical guide and…
Guillaume Etter
Inspired by key neuroscience principles, deep learning has driven exponential breakthroughs in developing functional models of perception and other cognitive processes. A key to this success has been the implementation of crucial features found in biological neural networks: neurons as units of information transfer…
Kevin V. Lemley
This brief, focused review considers two of the more commonly used artificial intelligence (AI) methods encountered in nephrology publications: machine vision based on convolutional neural networks (CNNs) and chatbots, such as ChatGPT, based on large language models. It is intended to offer a mostly non-technical…
Tong Wang, Ran Tong, Ting Xu, Yue Li + 1 more
With the development of artificial intelligence (AI) in complicated imaging and remote sensing technologies, plant research is transitioning from manual measurements to automated data collecting. High-throughput image-based phenotyping enables the precise and automated acquisition of traits across various spatial and…
Utkarsh Mishra, Ansh Pandey, Logeswari G, Tamilarasi K
Timely and precise detection of diseases on plants is crucial for minimizing losses during crop production in order to sustain food supply demands worldwide. In this work, deep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants using two publicly…
Salomon Kabongo
The embedding of word sequences into continuous vector spaces has been one of the most important developments in Natural Language Processing in the recent past. Such embeddings have found application in areas such as Automatic Speech Recognition, Machine Translation, Sentiment Analysis and many more. This essay reviews…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Angela Wang, Elena Xiao, Jason Cheng, Xiaoxi Shen
Background As a driving force of the Fourth Industrial Revolution, deep learning methods have achieved significant success across various fields, including genetic and genomic studies. While individual-level genetic data is ideal for deep learning models, privacy concerns and data-sharing restrictions often limit its…
Authors not listed
Modeling of chemical reactions is essential for understanding kinetic mechanisms and predicting possible outcomes of reacting systems. Quantum mechanical calculations are accurate but often prohibitively expensive. Deep learning has emerged as a faster alternative, but progress is slowed by a fragmented software…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
Mo Zhou, Emily Schwartz, Arish Alreja, R. Mark Richardson + 2 more
Deep neural networks have shown high accuracy in modeling neural responses in the visual system, but most models rely on supervised learning, which requires training on ground-truth labels that are typically unavailable in real-world settings. While unsupervised models can address this limitation, they miss another key…
Duarte, Javier M., Seljak, Uros + 2 more
| 41.1 | | Introduction | 2 | |------|--------|-----------------------------------------------------------------------------|----| | | 41.1.1 | A gentle introduction with a representative example | 3 | | 41.2 | | Supervised learning | 4 | | | 41.2.1 | Loss, risk, empirical risk | 4 | | | 41.2.2 | Regression | 5 | | |…
Mohamed A. Shamseldin, Ahmed Farouk Deifalla, Denise-Penelope N. Kontoni, Medhat Araby
Predicting the shear strength of concrete elements is a complex challenge influenced by numerous factors, with the type of concrete playing a decisive role in structural performance. Lightweight concrete, offering a superior strength-to-weight ratio and improved thermal properties compared to conventional weight…
Authors not listed
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
Authors not listed
Meta-GGA density functional theory (DFT) is an important method in ab initio materials modelling; however, its computational cost limits applicability for generating large datasets or simulating extended length and time scales, as necessary for modern materials discovery. Deorbitalization is a promising strategy to…
Authors not listed
Active deep learning offers a promising approach for hit discovery starting from limited data by iteratively updating and improving models during screening by applying new data and adapting decisions. Key open questions include how best to explore chemical space, how it compares to non-iterative methods, and how to use…
Authors not listed
Computational toxicology plays a pivotal role in modern drug discovery and environmental risk assessment; however, the reliability of predictive models on unseen chemical scaffolds remains a critical bottleneck. Deep learning architectures, despite their prevalence, are susceptible to ’silent failures’—yielding…
Ashena Gorgan Mohammadi, Manu Srinath Halvagal, Friedemann Zenke
Tracking prey or recognizing a lurking predator is as crucial for survival as anticipating their actions. To guide behavior, the brain must extract information about object identities and their dynamics from entangled sensory inputs. How it accomplishes this feat remains an open question. Predictive coding theories…
Authors not listed
Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms—Deep Evidential Regression (DER) and Deep Ensembles—on the QM9 and WS22 datasets, with a specific emphasis on the role of post…
Matteo Farina, Pietro Zamberlan, Arno Onken, Ulisse Ferrari
For datasets with thousands of neurons and images, vision transformers have proven successful at predicting neural responses to stimuli. However, they are expected to underperform in low-data regimes, where CNNs and Gaussian processes are considered more effective. We ask whether transformers can be made competitive…