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Found 24 papers, 11 papers with code
GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition
Skeleton-based action recognition (SAR) in videos is an important but challenging task in computer vision.
Part-Aware Transformer for Generalizable Person Re-identification
Based on the local similarity obtained in CSL, a Part-guided Self-Distillation (PSD) is proposed to further improve the generalization of global features.
Domain Generalization
Generalizable Person Re-identification
Generative Modelling of Lévy Area for High Order SDE Simulation
In this paper, we propose L\'{e}vyGAN, a deep-learning-based model for generating approximate samples of L\'{e}vy area conditional on a Brownian increment.
Minimally-Supervised Speech Synthesis with Conditional Diffusion Model and Language Model: A Comparative Study of Semantic Coding
However, existing methods suffer from three problems: the high dimensionality and waveform distortion of discrete speech representations, the prosodic averaging problem caused by the duration prediction model in non-autoregressive frameworks, and the information redundancy and dimension explosion problems of existing semantic encoding methods.
A Neural RDE-based model for solving path-dependent PDEs
The concept of the path-dependent partial differential equation (PPDE) was first introduced in the context of path-dependent derivatives in financial markets.
PCF-GAN: generating sequential data via the characteristic function of measures on the path space
Generating high-fidelity time series data using generative adversarial networks (GANs) remains a challenging task, as it is difficult to capture the temporal dependence of joint probability distributions induced by time-series data.
NODE-ImgNet: a PDE-informed effective and robust model for image denoising
Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks.
Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction
We developed the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold.
Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for End-to-End Speech Recognition
Experimental results show that the proposed model achieves competitive performance with 1/3 of the parameters of the encoder, compared with the full-parameter model.
Path Development Network with Finite-dimensional Lie Group Representation
To tackle this problem, we propose a novel, trainable path development layer, which exploits representations of sequential data with the help of finite-dimensional matrix Lie groups.
Meta Distribution Alignment for Generalizable Person Re-Identification
Domain Generalizable (DG) person ReID is a challenging task which trains a model on source domains yet generalizes well on target domains.
Domain Generalization
Generalizable Person Re-identification
+1
Forex Trading Volatility Prediction using Neural Network Models
In this paper, we investigate the problem of predicting the future volatility of Forex currency pairs using the deep learning techniques.
Sig-Wasserstein GANs for Time Series Generation
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines.
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition
In this paper, we propose a novel module, namely Logsig-RNN, which is the combination of the log-signature layer and recurrent type neural networks (RNNs).
Action Recognition In Videos
Skeleton Based Action Recognition
+3
An efficient representation of chronological events in medical texts
In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs).
Conditional Sig-Wasserstein GANs for Time Series Generation
The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterises the law of the time-series model.
Signature features with the visibility transformation
In this paper we put the visibility transformation on a clear theoretical footing and show that this transform is able to embed the effect of the absolute position of the data stream into signature features in a unified and efficient way.
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention
Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).
Learning stochastic differential equations using RNN with log signature features
We illustrate the approach by approximating the unknown functional as a controlled differential equation.
Ranked #54 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.
Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition
To this end, we regard the evolving landmark data as a high-dimensional path and apply non-linear path signature techniques to provide an expressive, robust, non-linear, and interpretable representation for the sequential events.
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences.
Cascading Bandits for Large-Scale Recommendation Problems
In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend $K$ most attractive items from a large set of $L$ candidate items.
Learning from the past, predicting the statistics for the future, learning an evolving system
We bring the theory of rough paths to the study of non-parametric statistics on streamed data.
