Search · four archives
Search · four archives
26 papers · ranked by Valyu relevance
Ehsan Adeli, Luning Sun, Jianxun Wang, Alexandros A. Taflanidis
In this research paper, we study the capability of artificial neural network models to emulate storm surge based on the storm track/size/ intensity history, leveraging a database of synthetic storm simulations. Traditionally, Computational Fluid Dynamics (CFD) solvers are employed to numerically solve the storm surge…
Jamie A. O’Reilly, Jordan Wehrman, Aaron Carey, Jennifer Bedwin + 3 more
Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We developed a computational model of visual ERP generation to study this phenomenon which consisted of a…
Aran Nayebi
Humans and animals exhibit a range of interesting behaviors in dynamic environments, and it is unclear how our brains actively reformat this dense sensory information to enable these behaviors. Experimental neuroscience is undergoing a revolution in its ability to record and manipulate hundreds to thousands of neurons…
Grace W. Lindsay, Thomas D. Mrsic-Flogel, Maneesh Sahani
Behavioral studies suggest that recurrence in the visual system is important for processing degraded stimuli. There are two broad anatomical forms this recurrence can take, lateral or feedback, each with different assumed functions. Here we add four different kinds of recurrence—two of each anatomical form—to a…
Jessica Loke, Noor Seijdel, Lukas Snoek, Matthew van der Meer + 4 more
Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks (DCNNs) but the computations underlying recurrent…
Robin Gutzen, Grace W Lindsay
Convolutional Neural Networks (CNNs) trained for image recognition have demonstrated remarkable conceptual similarities to the primate ventral visual pathway, but their standard feedforward architectures lack the recurrent connections that are ubiquitous in visual cortex. Such recurrence is thought to underlie…
Sara Varetti, Sebastian Goldt, Eugenio Piasini
In vision neuroscience, the temporal dynamics of the sensory stream and of its neural representations are thought to be deeply linked to the function of the hierarchy of cortical areas that deal with object recognition, known as the visual ventral stream. Neural representations that are invariant under…
Dmitri, Lvov, Yair Smadar, Ran Bezen
— In this paper, we explore the application of Recurrent Neural Network (RNN) for still images. Typically, Convolutional Neural Networks (CNNs) are the prevalent method applied for this type of data, and more recently, transformers have gained popularity, although they often require large models. Unlike these methods…
Timothee Maniquet, Hans Op de Beeck, Andrea Ivan Costantino
Object recognition requires flexible and robust information processing, especially in view of the challenges posed by naturalistic visual settings. The ventral stream in visual cortex is provided with this robustness by its recurrent connectivity. Recurrent deep neural networks (DNNs) have recently emerged as promising…
Authors not listed
The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas…
Aakash Sarkar, Marc W. Howard
Human cognition integrates information across nested timescales. While the cortex exhibits hierarchical Temporal Receptive Windows (TRWs), local circuits often display heterogeneous time constants. To reconcile this, we trained biologically constrained deep networks, based on scale-invariant hippocampal time cells, on…
Gajraj Kuldeep
—This study compares sequential image classification methods based on recurrent neural networks. We describe methods based on recurrent neural networks such as Long-Short-Term memory(LSTM), bidirectional Long-Short-Term memory(BiLSTM) architectures, etc. We also review the state-ofthe-art sequential image…
Jian-wei Liu, Bingrong Xu, Zhiyan Song
Recursive and recurrent neural networks are the main realization forms of sequence models based on neural networks, which have been developed rapidly in recent years. Recurrent neural networks are basically standard processing methods for machine translation, question and answer, and sequence video analysis. It is also…
Hongwei Bai, Weiyan Tong, Zhenkun Geng, Cheng Gao + 1 more
As a critical component of industrial equipment, the fault diagnosis of rolling bearings is essential for reducing unplanned downtime and improving equipment reliability. Existing methods achieve an accuracy of no more than 92% in low signal-to-noise ratio environments. To address this issue, this paper proposes an…
Haohan Ding, Haoke Hou, Long Wang, Xiaohui Cui + 3 more
'David I. Wilson' 'Ioan Cristian Trelea'] This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful…
Sukhdeep Singh, Sudhir Rohilla, Anuj Sharma
handwriting recognition Authors: ['Sukhdeep Singh' 'Sudhir Rohilla' 'Anuj Sharma'] Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. These deep learning algorithms have demonstrated great results in different fields. Deep…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Sweta Kumari, C Vigneswaran, V. Srinivasa Chakravarthy
Sequential decision making tasks that require information integration over extended durations of time are challenging for several reasons including the problem of vanishing gradients, long training times and significant memory requirements. To this end we propose a neuron model fashioned after the JK flip-flops in…
Chuang Liu, Yang Chen, Chang Seok Bang
Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient neuromorphic computing, particularly when processing asynchronous event streams from dynamic vision sensors (DVSs). However, SNNs often suffer from limited representational capacity and suboptimal feature recalibration compared to…
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…
Sebastian Klavinskis-Whiting, Andrew J. King, Nicol S. Harper, Tim Christian Kietzmann
A major goal of neuroscience is to identify general principles that can explain the diverse structures and functions of the brain. The principle of temporal prediction provides one approach, arguing that the sensory brain is optimized to represent stimulus features that efficiently predict the immediate future input.…
Yuanqi Xie, Yichen Henry Liu, Christos Constantinidis, Xin Zhou
Understanding the neural mechanisms of working memory has been a long-standing Neuroscience goal. Bump attractor models have been used to simulate persistent activity generated in the prefrontal cortex during working memory tasks and to study the relationship between activity and behavior. How realistic the assumptions…
Rongpei Gou, Jingyi Yang, Menghan Guo, Yingjun Chen + 1 more
Central nervous system (CNS) drugs have had a significant impact on human health, e.g., treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models, particularly those for designing drugs from scratch, have shown great potential for accelerating drug…
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This…
Morgan Thomas, Noel M. O'Boyle, Andreas Bender, Chris de Graaf
A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator.…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…