26 papers · ranked by Valyu relevance
Xing Chen, Dongshu Liu, Jérémie Laydevant, Julie Grollier
The Forward-Forward (FF) algorithm is a recent, purely forward-mode learning method, that updates weights locally and layer-wise and supports supervised as well as unsupervised learning. These features make it ideal for applications such as brain-inspired learning, low-power hardware neural networks, and distributed…
Yohei M. Rosen, Benedict J. Paten
Hidden Markov models of haplotype inheritance such as the Li and Stephens model allow for computationally tractable probability calculations using the forward algorithms as long as the representative reference panel used in the model is sufficiently small. Specifically, the monoploid Li and Stephens model and its…
Reece Adamson
The Forward-Forward algorithm is an alternative learning method which consists of two forward passes rather than a forward and backward pass employed by backpropagation. Forward-Forward networks employ layer local loss functions which are optimized based on the layer activation for each forward pass rather than a…
Yohei M. Rosen, Benedict J. Paten
Background Hidden Markov models of haplotype inheritance such as the Li and Stephens model allow for computationally tractable probability calculations using the forward algorithm as long as the representative reference panel used in the model is sufficiently small. Specifically, the monoploid Li and Stephens model and…
Mitra Bakhshi
—Incorporating the Forward-Forward algorithm into neural network training represents a transformative shift from traditional methods, introducing a dual-forward mechanism that streamlines the learning process by bypassing the complexities of derivative propagation. This method is noted for its simplicity and efficiency…
Desmond Y. M. Tang
The backpropagation algorithm, despite its widespread use in neural network learning, may not accurately emulate the human cortex's learning process. Alternative strategies, such as the Forward-Forward Algorithm (FFA), offer a closer match to the human cortex's learning characteristics. However, the original FFA paper…
Saumya Gandhi, Ritu Gala, Jonah Kornberg, Advaith Sridhar
The Forward Forward algorithm, proposed by Geoffrey Hinton in November 2022, is a novel method for training neural networks as an alternative to backpropagation. In this project, we replicate Hinton's experiments on the MNIST dataset, and subsequently extend the scope of the method with two significant contributions.…
Ai Azuma, Masashi Shimbo, Yuji Matsumoto
In this paper, we propose an algebraic formalization of the two important classes of dynamic programming algorithms called forward and forward-backward algorithms. They are generalized extensively in this study so that a wide range of other existing algorithms is subsumed. Forward algorithms generalized in this study…
Tusar Kanti Dash, Anurag Raj, Satyajit Mahapatra, Ganapati Panda
The presence of pests in soil costs the agriculture industry billions of dollars every year since it reduces crop yields and raises preventive costs. The pest detection in soil is vital for maintaining healthy crops, optimizing pest management, and ensuring economic and ecological sustainability. There are several…
Seyed Mohammad Ghoreyshi, Alireza Shahrabi, Tuleen Boutaleb, Jaime Lloret Mauri
'Jaime Lloret Mauri'] Increasing attention has recently been devoted to underwater sensor networks (UWSNs) because of their capabilities in the ocean monitoring and resource discovery. UWSNs are faced with different challenges, the most notable of which is perhaps how to efficiently deliver packets taking into account…
Fabio F. de Oliveira, Leonardo A. Dias, Marcelo A. C. Fernandes
In bioinformatics, alignment is an essential technique for finding similarities between biological sequences. Usually, the alignment is performed with the Smith-Waterman (SW) algorithm, a well-known sequence alignment technique of high-level precision based on dynamic programming. However, given the massive data volume…
Qi Zhang, Chang Liu, Stephen Wu, Ryo Yoshida
In the last few years, de novo molecular design using machine learning has made great technical progress but its practical deployment has not been as successful. This is mostly owing to the cost and technical difficulty of synthesizing such computationally designed molecules. To overcome such barriers, various methods…
Fengyin Li, Junhui Wang, Mengxue Shang, Dandan Zhang + 5 more
'Rosario Lo Franco' 'Hua-Lei Yin' 'Kaizhi Huang' 'Guan-Jie Fan-Yuan'] The security of digital signatures depends significantly on the signature key. Therefore, to reduce the impact of leaked keys upon existing signatures and subsequent ones, a digital signature scheme with strong forward security could be an effective…
Xing Fu, Zucheng Huang, Gongxue Zhang, Weijun Wang + 2 more
'Sándor Szénási'] To address the issues of low search efficiency, excessive node expansion, and the presence of redundant nodes in the traditional A algorithm, this article proposes an improved A algorithm for mobile robot path planning. Firstly, a multi-neighborhood hybrid search method is introduced, optimizing the…
Tung Dang, Hirohisa Kishino
Random forest (RF) captures complex feature patterns that differentiate groups of samples and is rapidly being adopted in microbiome studies. However, a major challenge is the high dimensionality of microbiome datasets. They include thousands of species or molecular functions of particular biological interest. This…
Nagaiah Mohanan Balamurugan, Raju Kannadasan, Mohammed H. Alsharif, Peerapong Uthansakul + 1 more
'Peerapong Uthansakul' 'Leon Rothkrantz'] In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN)…
Eric Alcaide, Stella Biderman, Amalio Telenti, M. Cyrus Maher
The conversion of proteins between internal and cartesian coordinates is a limiting step in many pipelines, such as molecular dynamics simulations and machine learning models. This conversion is typically carried out by sequential or parallel applications of the Natural extension of Reference Frame (NeRF) algorithm.…
Tung Dang, Alan S. R. Fermin, Maro G. Machizawa
Neuroimaging data is complex and high-dimensional that poses challenges for machine learning (ML) applications. Of varieties of reasons contributing on accuracy decoding, variable feature selection is one of crucial steps for determining target feature in data analysis, especially in the context of neuroimaging studies…
Haotian Wang, Xiaolong Zhou, Jianyong Li, Zhilun Yang + 2 more
'Sergio Toral Marín'] In this paper, an improved APF-GFARRT (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate…
Jordan M. Eizenga, Benedict Paten
Modern genomic sequencing data is trending toward longer sequences with higher accuracy. Many analyses using these data will center on alignments, but classical exact alignment algorithms are infeasible for long sequences. The recently proposed WFA algorithm demonstrated how to perform exact alignment for long, similar…
Sunday O. Olatunji, Aisha Alansari, Heba Alkhorasani, Meelaf Alsubaii + 7 more
'Meelaf Alsubaii' 'Rasha Sakloua' 'Reem Alzahrani' 'Yasmeen Alsaleem' 'Reem Alassaf' 'Mehwash Farooqui' 'Mohammed Imran Basheer Ahmed' 'Jamal Alhiyafi'] Alzheimer's Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to…
William Borrelli, Joshua Schrier
Forward and retrosynthetic organic reaction prediction are challenging applications of artificial intelligence (AI) research in chemistry. IBM’s freely available RXN for Chemistry (https://rxn.res.ibm.com) treats reaction prediction as a translation problem, by using transformer-based machine learning models trained on…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
Michael Alverson, Sterling Baird, Ryan Murdock, Taylor Sparks
The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal…
Authors not listed
For applications in gas sensing, purification, and capture, we often wish to search a large set of metal-organic frameworks (MOFs) for the top-K in terms of their Henry coefficient of an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample…
Lionel Zoubritzky, François-Xavier Coudert
We present here an open-source Julia library for the topological identification of crystalline materials, with algorithmic and computational improvements over the previously available software in the field, resulting in a speed increase of one order of magnitude. This new algorithm and implementation can therefore be…