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Found 13 papers, 3 papers with code
Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles
Practical Quantum Machine Learning (QML) is challenged by noise, limited scalability, and poor trainability in Variational Quantum Circuits (VQCs) on current hardware.
Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention for Cognitive and Psychiatric Applications
Understanding how the brain's complex nonlinear dynamics give rise to cognitive function remains a central challenge in neuroscience.
SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding
Through the evaluation of existing visual brain decoding models, we further reveal that crucial information is often lost in translation, even in state-of-the-art models that achieve near-perfect scores on existing metrics.
Mind the Gap: Aligning the Brain with Language Models Requires a Nonlinear and Multimodal Approach
However, traditional prediction models rely on linear mappings from unimodal features, despite the complex integration of auditory signals with linguistic and semantic information across widespread brain networks during speech comprehension.
Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures
Spanning multiple scales-from macroscopic anatomy down to intricate microscopic architecture-the human brain exemplifies a complex system that demands integrated approaches to fully understand its complexity.
Swin fMRI Transformer Predicts Early Neurodevelopmental Outcomes from Neonatal fMRI
Brain development in the first few months of human life is a critical phase characterized by rapid structural growth and functional organization.
A Training-Free Approach for Music Style Transfer with Latent Diffusion Models
Music style transfer, while offering exciting possibilities for personalized music generation, often requires extensive training or detailed textual descriptions.
Revisiting Your Memory: Reconstruction of Affect-Contextualized Memory via EEG-guided Audiovisual Generation
In this paper, we introduce RecallAffectiveMemory, a novel task designed to reconstruct autobiographical memories through audio-visual generation guided by affect extracted from electroencephalogram (EEG) signals.
Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning
The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge.
AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer
The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely.
SwiFT: Swin 4D fMRI Transformer
To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner.
GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis
Synthesizing medical images, such as PET, is a challenging task due to the fact that the intensity range is much wider and denser than those in photographs and digital renderings and are often heavily biased toward zero.
GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI
This paper proposes an alternative approach to the aforementioned, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance.
