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Found 39 papers, 6 papers with code
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: a systematic review
Machine learning algorithms for seizure detection have shown great diagnostic potential, with recent reported accuracies reaching 100%.
FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications
This is achieved by fine-tuning the Llama2 7B model on a small portion of supervised financial sentiment analysis data, so as to jointly handle the complexities of financial lexicon and context, and further equipping it with a neural network based decision mechanism.
Tensor Star Decomposition
A novel tensor decomposition framework, termed Tensor Star (TS) decomposition, is proposed which represents a new type of tensor network decomposition based on tensor contractions.
Detecting gamma-band responses to the speech envelope for the ICASSP 2024 Auditory EEG Decoding Signal Processing Grand Challenge
The 2024 ICASSP Auditory EEG Signal Processing Grand Challenge concerns the decoding of electroencephalography (EEG) measurements taken from participants who listened to speech material.
Decoding of Selective Attention to Speech From Ear-EEG Recordings
We show that typical neural responses to the speech envelope, as well as its onsets, can be recovered from such a device, and that the morphology of the recorded responses is indeed modulated by selective attention to speech.
Decoding Envelope and Frequency-Following EEG Responses to Continuous Speech Using Deep Neural Networks
The electroencephalogram (EEG) offers a non-invasive means by which a listener's auditory system may be monitored during continuous speech perception.
Improving Diffusion Models for ECG Imputation with an Augmented Template Prior
In this work, to improve the imputation and forecasting accuracy for ECG with probabilistic models, we present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
On the dynamics of multi agent nonlinear filtering and learning
Multiagent systems aim to accomplish highly complex learning tasks through decentralised consensus seeking dynamics and their use has garnered a great deal of attention in the signal processing and computational intelligence societies.
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Existing relationships among time series can be exploited as inductive biases in learning effective forecasting models.
Relating EEG recordings to speech using envelope tracking and the speech-FFR
During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention.
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency.
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Generalizing Impermanent Loss on Decentralized Exchanges with Constant Function Market Makers
To this end, we provide a framework to generalize impermanent loss for multiple asset pools obeying any constant function market maker with optional concentrated liquidity.
Fair and skill-diverse student group formation via constrained k-way graph partitioning
Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration.
ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples.
Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation
Graph-based deep learning algorithms could utilise the graph structure but raise a few challenges, such as how to determine the weights of the edges and the shallow receptive field caused by the over-smoothing issue.
Demystifying CNNs for Images by Matched Filters
The success of convolution neural networks (CNN) has been revolutionising the way we approach and use intelligent machines in the Big Data era.
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks
However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists.
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Combining this novel perspective of two-stage synthesis with advanced generative models (i. e., the diffusion models), the proposed BinauralGrad is able to generate accurate and high-fidelity binaural audio samples.
Last-iterate convergence analysis of stochastic momentum methods for neural networks
The stochastic momentum method is a commonly used acceleration technique for solving large-scale stochastic optimization problems in artificial neural networks.
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training
In this paper, we propose InferGrad, a diffusion model for vocoder that incorporates inference process into training, to reduce the inference iterations while maintaining high generation quality.
Bayesian autoregressive spectral estimation
Autoregressive (AR) time series models are widely used in parametric spectral estimation (SE), where the power spectral density (PSD) of the time series is approximated by that of the \emph{best-fit} AR model, which is available in closed form.
Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets
Continuous double auctions such as the limit order book employed by exchanges are widely used in practice to match buyers and sellers of a variety of financial instruments.
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial
To help demystify CNNs, we revisit their operation from first principles and a matched filtering perspective.
Understanding the Basis of Graph Convolutional Neural Networks via an Intuitive Matched Filtering Approach
Graph Convolutional Neural Networks (GCNN) are becoming a preferred model for data processing on irregular domains, yet their analysis and principles of operation are rarely examined due to the black box nature of NNs.
Von Mises-Fisher Elliptical Distribution
A large class of modern probabilistic learning systems assumes symmetric distributions, however, real-world data tend to obey skewed distributions and are thus not always adequately modelled through symmetric distributions.
Improved Coherence Index-Based Bound in Compressive Sensing
Within the Compressive Sensing (CS) paradigm, sparse signals can be reconstructed based on a reduced set of measurements.
A Review of Hidden Markov Models and Recurrent Neural Networks for Event Detection and Localization in Biomedical Signals
How to develop a model that can describe the transition between processes in time?
Reciprocal Adversarial Learning via Characteristic Functions
For rigour, we first establish the physical meaning of the phase and amplitude in CF, and show that this provides a feasible way of balancing the accuracy and diversity of generation.
Methods of Adaptive Signal Processing on Graphs Using Vertex-Time Autoregressive Models
The concept of a random process has been recently extended to graph signals, whereby random graph processes are a class of multivariate stochastic processes whose coefficients are matrices with a \textit{graph-topological} structure.
Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution.
On the Importance of Opponent Modeling in Auction Markets
The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions.
Solving general elliptical mixture models through an approximate Wasserstein manifold
To relieve this issue, we introduce an efficient optimisation method on a statistical manifold defined under an approximate Wasserstein distance, which allows for explicit metrics and computable operations, thus significantly stabilising and improving the EMM estimation.
Widely Linear Complex-valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models
To resolve this issue, we design a new cost function, which is capable of controlling the balance between the phase and the amplitude contribution to the solution.
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion
In this paper, by exploiting the low-rank structure of the TR latent space, we propose a novel tensor completion method which is robust to model selection.
Rank Minimization on Tensor Ring: A New Paradigm in Scalable Tensor Decomposition and Completion
In low-rank tensor completion tasks, due to the underlying multiple large-scale singular value decomposition (SVD) operations and rank selection problem of the traditional methods, they suffer from high computational cost and high sensitivity of model complexity.
A universal framework for learning the elliptical mixture model
Mixture modelling using elliptical distributions promises enhanced robustness, flexibility and stability over the widely employed Gaussian mixture model (GMM).
Quadratic Programming Over Ellipsoids (with Applications to Constrained Linear Regression and Tensor Decomposition)
A novel algorithm to solve the quadratic programming problem over ellipsoids is proposed.
Optimization and Control
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
Very often data we encounter in practice is a collection of matrices rather than a single matrix.
