25 papers · ranked by Valyu relevance
Shuxin Guo, Chenxu Guo, Jianhua Jiang, Heming Jia
A Multi-Layer Perceptron (MLP), as the basic structure of neural networks, is an important component of various deep learning models such as CNNs, RNNs, and Transformers. Nevertheless, MLP training faces significant challenges, with a large number of saddle points and local minima in its non-convex optimization space…
Florian Raudies, Eric A. Zilli, Michael E. Hasselmo, Thomas Wennekers
'Thomas Wennekers'] With the goal of understanding behavioral mechanisms of generalization, we analyzed the ability of neural networks to generalize across context. We modeled a behavioral task where the correct responses to a set of specific sensory stimuli varied systematically across different contexts. The correct…
Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashok Samal + 2 more
'Prahalada K. Rao' 'Matthew R. Johnson'] In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other…
Neha Vinayak, Shandar Ahmad
A multi-layer perceptron (MLP) consists of a number of forward-connected weights (W_ijk_) from each feeding layer node (n_ij_) to the many initially equivalent nodes (n_i+1,k_) in the next layer. Exact a priori order and search space of these weights (W_ijk_) is random and prone to redundancy, irreproducibility and…
Frank Britto Bisso, Durga Shree, Yinan Zhu, Christian Cuba Samaniego
Cells have evolved to sense a wide range of input combinations and integrate those signals through signaling pathways to produce context-specific responses, such as differentiation, cell-type specification, and patterning. To replicate this information-processing capacity, synthetic biology has developed large-scale…
Chao Geng, Qingji Sun, Shigetoshi Nakatake
Perceptron is an essential element in neural network (NN)-based machine learning, however, the effectiveness of various implementations by circuits is rarely demonstrated from chip testing. This paper presents the measured silicon results for the analog perceptron circuits fabricated in a 0.6 $μ$m/±2.5 V complementary…
Arka Ghosh, Mriganka Chakraborty
Standard neural network based on general back propagation learning using delta method or gradient descent method has some great faults like poor optimization of error-weight objective function, low learning rate, instability .This paper introduces a hybrid supervised back propagation learning algorithm which uses…
Ashish Chandra, Mohammad Suaib, Md. Rizwan Beg
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural…
Klas-Göran Karlsson
The concept of a recently proposed Forward-Forward learning algorithm for fully connected artificial neural networks is applied to a single multi output perceptron for classification. The parameters of the system are trained with respect to increased (decreased) "goodness" for correctly (incorrectly) labelled input…
Haimonti Dutta, N. Nataraj, Saurabh Amarnath Mahindre
In recent years, storing large volumes of data on distributed devices has become commonplace. Applications involving sensors, for example, capture data in different modalities including image, video, audio, GPS and others. Novel algorithms are required to learn from this rich distributed data. In this paper, we present…
Yansel Gonzalez Tejeda, Helmut A. Mayer
Supervised Regression (Preprint) Authors: ['Yansel Gonzalez Tejeda' 'Helmut A. Mayer'] In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. While there are numerous books and articles on the individual topics we…
Christian Cuba Samaniego, Emily Wallace, Franco Blanchini, Elisa Franco + 1 more
The engineering of molecular programs capable of processing patterns of multi-input biomarkers holds great potential in applications ranging from in vitro diagnostics (e.g., viral detection, including COVID-19) to therapeutic interventions (e.g., discriminating cancer cells from normal cells). For this reason…
Raúl Rojas
This paper shows that a long chain of perceptrons (that is, a multilayer perceptron, or MLP, with many hidden layers of width one) can be a universal classifier. The classification procedure is not necessarily computationally efficient, but the technique throws some light on the kind of computations possible with…
Iara Cunha, Marcos Eduardo Valle
A morphological perceptron is a multilayer feedforward neural network in which neurons perform elementary operations from mathematical morphology. For multiclass classification tasks, a morphological perceptron with a competitive layer (MPCL) is obtained by integrating a winner-take-all output layer into the standard…
Nikolaus Kriegeskorte
Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain and their computations could be…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Hossein Moghimianavval, Ignacio Gispert, Santiago R. Castillo, Olaf B. W. H. Corning + 2 more
Constructing molecular classifiers that enable cells to recognize linear and non-linear input patterns would expand the biocomputational capabilities of engineered cells, thereby unlocking their potential in diagnostics and therapeutic applications. While several biomolecular classifier schemes have been designed, the…
Sherif Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph Shapter + 2 more
There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most…
Sherif Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph Shapter + 2 more
There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most…
Authors not listed
Metal–organic frameworks (MOFs) represent a versatile class of porous materials, yet efficiently exploring their vast chemical space for target gas adsorption properties remains a major challenge. MOFid, a text-based encoding of MOF structures, has enabled large-scale data mining using natural language processing (NLP)…
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
Jude Baby George, Grace Mathew Abraham, Zubin Rashid, Bharadwaj Amrutur + 1 more
'Bharadwaj Amrutur' 'Sujit Kumar Sikdar'] Conjunctive encoding of inputs has been hypothesized to be a key feature in the computational capabilities of the brain. This has been inferred based on behavioral studies and electrophysiological recording from animals. In this report, we show that random neuronal ensembles…
Yue Ban, E. Torrontegui, J. Casanova
We propose quantum neural networks that include multi-qubit interactions in the neural potential leading to a reduction of the network depth without losing approximative power. We show that the presence of multi-qubit potentials in the quantum perceptrons enables more efficient information processing tasks such as XOR…
Toviah Moldwin, Idan Segev
The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…