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Search · four archives
14 papers · ranked by Valyu relevance
Mamyrbayev Orken, Oralbekova Dina, Alimhan Keylan, Turdalykyzy Tolganay + 1 more
Today, the Transformer model, which allows parallelization and also has its own internal attention, has been widely used in the field of speech recognition. The great advantage of this architecture is the fast learning speed, and the lack of sequential operation, as with recurrent neural networks. In this work…
Sumit Madan, Manuel Lentzen, Johannes Brandt, Daniel Rueckert + 2 more
'Martin Hofmann-Apitius' 'Holger Fröhlich'] Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence (AI) field. The transformer model is a type of DNN that was originally used for the natural language processing tasks and has since gained more and more attention for processing various…
Hatem Ibrahem, Ahmed Salem, Hyun-Soo Kang, Jiayi Ma
The latest research in computer vision highlighted the effectiveness of the vision transformers (ViT) in performing several computer vision tasks; they can efficiently understand and process the image globally unlike the convolution which processes the image locally. ViTs outperform the convolutional neural networks in…
Tien Thanh Thach, Antonio M. Scarfone
Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel framework that leverages…
Qiumei Pu, Zuoxin Xi, Shuai Yin, Zhe Zhao + 1 more
Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has…
Hossein Adeli, Sun Minni, Nikolaus Kriegeskorte
The Algonauts challenge [9] called on the community to provide novel solutions for predicting brain activity of humans viewing natural scenes. This report provides an overview and technical details of our submitted solution. We use a general transformer encoder-decoder model to map images to fMRI responses. The encoder…
Joseph G. Makin, David A. Moses, Edward F. Chang
A decade after the first successful attempt to decode speech directly from human brain signals, accuracy and speed remain far below that of natural speech or typing. Here we show how to achieve high accuracy from the electrocorticogram at natural-speech rates, even with few data (on the order of half an hour of spoken…
Harry Dong, Sean Donegan, Megna Shah, Yuejie Chi
Three dimensional electron back-scattered diffraction (EBSD) microscopy is a critical tool in many applications in materials science, yet its data quality can fluctuate greatly during the arduous collection process, particularly via serial-sectioning. Fortunately, 3D EBSD data is inherently sequential, opening up the…
Chen-Hsiu Huang, Ja-Ling Wu, Jun Chen
End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational…
Joel Ye, Chethan Pandarinath
Neural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, such as those based on recurrent neural networks (RNNs). However, using recurrence to explicitly model dynamics necessitates sequential…
Marco Nicolini, Emanuele Saitto, Ruben Emilio Jimenez Franco, Emanuele Cavalleri + 7 more
We introduce Finenzyme, a Protein Language Model (PLM) that employs a multifaceted learning strategy based on transfer learning from a decoder-based Transformer, conditional learning using specific functional keywords, and fine-tuning to model specific Enzyme Commission (EC) categories. Using Finenzyme, we investigate…
Ian T. Ellwood
Transformers have revolutionized machine learning models of language and vision, but their connection with neuroscience remains tenuous. Built from attention layers, they require a mass comparison of queries and keys that is difficult to perform using traditional neural circuits. Here, we show that neurons can…
Yi Wang
Deciphering the non-coding language of DNA is one of the fundamental questions in genomic research. Previous bioinformatics methods often struggled to capture this complexity, especially in cases of limited data availability. Enhancers are short DNA segments that play a crucial role in biological processes, such as…
Igor Sadalski
Single-cell foundational models have emerged as a powerful tool for learning generalizable cellular representations from large-scale data. Most models in this domain use transformer backbones, which require careful engineering of gene and expression encoding strategies, yet there is no consensus on which encoding…