12 papers · ranked by Valyu relevance
Jianqiang Sun, Wei Cao, Ikumi Kamachi, Kentaro K. Shimizu + 1 more
Time-series RNA sequencing provides a powerful framework for studying dynamic gene regulation, yet conventional analyses usually represent gene expression profiles as real-valued vectors in Euclidean space and quantify similarity using correlation or distance. Inspired by quantum information theory, we present a…
Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling
Object-centric representations form the basis of human perception, and enable us to reason about the world and to systematically generalize to new settings. Currently, most works on unsupervised object discovery focus on slot-based approaches, which explicitly separate the latent representations of individual objects.…
Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li + 2 more
'Jakob Grue Simonsen'] Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that position embeddings capture the position of individual words, but not the ordered relationship (e.g., adjacency or…
Fu Chen, Qinglin Zhao, Li Feng, Longfei Tang + 2 more
'Haitao Huang'] Abstract—Self-attention has revolutionized classical machine learning, yet existing quantum self-attention models underutilize quantum states' potential due to oversimplified or incomplete mechanisms. To address this limitation, we introduce the Quantum Complex-Valued Self-Attention Model (QCSAM), the…
Kien Nguyen, Clinton Fookes, Sridha Sridharan, Arun Ross
—In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the…
HaDi MaBouDi, Krishna Subramani, Hamid Soltanian-Zadeh, Shun-ichi Amari + 1 more
Natural scenes contain higher-order statistical structures that can be encoded in their spatial phase information. Nevertheless, little progress has been made in modeling phase information of images, and understanding efficient representation of the image phases in the brain. In order to capture spatial phase structure…
Dipayan Biswas, P. Sooryakiran, V. Srinivasa Chakravarthy
Recurrent neural networks with associative memory properties are typically based on fixed-point dynamics, which is fundamentally distinct from the oscillatory dynamics of the brain. There have been proposals for oscillatory associative memories, but here too, in the majority of cases, only binary patterns are stored as…
Hanna Bugler, Rodrigo Pommot Berto, Roberto Souza, Ashley D. Harris
To determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in Convolutional Neural Networks (CNNs) for frequency and phase correction (FPC) of GABA-edited Magnetic Resonance Spectroscopy (MRS) data. An ablation study was performed…
Xihe Xie, Chang Cai, Pablo F. Damasceno, Srikantan Nagarajan + 1 more
How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along…
Scott Purdy
Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence technology that mimics the architecture and processes of the neocortex. In this white paper we describe how to encode data as Sparse Distributed Representations (SDRs) for use in HTM systems. We explain several existing encoders, which…
Alaa Eldin Rohiem Shehata, Hassan Yakout El-Arsh
—Arithmetic Coding is an efficient lossless compression scheme applied for many multimedia standards such as JPEG, JPEG2000, H.263, H.264 and H.265. Due to nonlinearity, high error propagation and high error sensitivity of arithmetic coders, many techniques have been developed for extending the usage of arithmetic…
Yibo Yang, Stephan Mandt, Lucas Theis
Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such…