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Found 24 papers, 4 papers with code
Balancing Spectral, Temporal and Spatial Information for EEG-based Alzheimer's Disease Classification
Here, we systematically investigate the importance of spatial information relative to spectral or temporal information by varying the proportion of each dimension for AD classification.
Stochastic Graph Heat Modelling for Diffusion-based Connectivity Retrieval
Here, we combine the graph heat equation with the stochastic heat equation, which ultimately yields a model for multivariate time signals on a graph.
Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed
The prevalent methods of segregating the ad auction and allocation into two distinct stages face two problems: 1) Ad auction does not consider externalities, such as the influence of actual display position and context on ad Click-Through Rate (CTR); 2) The ad allocation, which utilizes the auction-winning ad's payment to determine the display position dynamically, fails to maintain incentive compatibility (IC) for the advertisement.
Understanding Concepts in Graph Signal Processing for Neurophysiological Signal Analysis
The experimental section focuses on the role of graph frequency in data classification, with applications to neuroimaging.
Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs
Cryo-electron microscopy (cryo-EM) remains pivotal in structural biology, yet the task of protein particle picking, integral for 3D protein structure construction, is laden with manual inefficiencies.
Graph Neural Network-based EEG Classification: A Survey
Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders.
Model-Free Market Risk Hedging Using Crowding Networks
Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies.
Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease using EEG Data
Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data.
InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation
When using such a pair to predict an object instance on the current frame, not only the generated instance is automatically associated with its precursors on previous frames, but the model gets a good prior for predicting the same object.
Learning Disentangled Label Representations for Multi-label Classification
The specificity of the framework lies in a feature disentangle module, which contains learnable semantic queries and a Semantic Spatial Cross-Attention (SSCA) module.
QueryProp: Object Query Propagation for High-Performance Video Object Detection
The proposed QueryProp contains two propagation strategies: 1) query propagation is performed from sparse key frames to dense non-key frames to reduce the redundant computation on non-key frames; 2) query propagation is performed from previous key frames to the current key frame to improve feature representation by temporal context modeling.
Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease
Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands.
PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
To overcome these limitations, we propose a unified framework for the DPS task by applying a dynamic convolution technique to both the PS and depth prediction tasks.
Multi-Phase Locking Value: A Generalized Method for Determining Instantaneous Multi-frequency Phase Coupling
Methods such as $n:m$ phase locking value and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e. g. $f$, $2f/3$) and across three frequencies (e. g. $f_1$, $f_2$, $f_1+f_2$), respectively.
Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis
Energy landscape analysis is a method that can be used to quantify these dynamics.
Google Crowdsourced Speech Corpora and Related Open-Source Resources for Low-Resource Languages and Dialects: An Overview
This paper presents an overview of a program designed to address the growing need for developing freely available speech resources for under-represented languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems
We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu, which are six of the twenty two official languages of India spoken by 374 million native speakers.
Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech
In this paper we present a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources, focusing on various South American dialects of Spanish.
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking
Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods.
Fast OBDD Reordering using Neural Message Passing on Hypergraph
Ordered binary decision diagrams (OBDDs) are an efficient data structure for representing and manipulating Boolean formulas.
Doubly Robust Data-Driven Distributionally Robust Optimization
Data-driven Distributionally Robust Optimization (DD-DRO) via optimal transport has been shown to encompass a wide range of popular machine learning algorithms.
Query-Adaptive Late Fusion for Image Search and Person Re-Identification
However, in a more realistic situation, one does not know in advance whether a feature is effective or not for a given query.
Beyond $χ^2$ Difference: Learning Optimal Metric for Boundary Detection
To improve the performance of boundary detection, a Learning-based Boundary Metric (LBM) is proposed to replace $\chi^2$ difference adopted by the classical algorithm mPb.
Seeing the Big Picture: Deep Embedding with Contextual Evidences
Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches, leading to the so-called "Deep Embedding" framework.
