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Found 10 papers, 3 papers with code
Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning
In contrast to conventional methods that fuse features from multiple modalities, our proposed approach simplifies the neural architecture by retaining a single time series encoder, consequently leading to preserved scalability.
Contrastive Shapelet Learning for Unsupervised Multivariate Time Series Representation Learning
Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels.
TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification
Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of time series, which lack sufficient feature extraction capability to obtain satisfactory classification accuracy.
UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning
Machine learning has emerged as a powerful tool for time series analysis.
FedST: Secure Federated Shapelet Transformation for Time Series Classification
Thus, we design a basic protocol for the FedST kernel that we prove to be secure and accurate.
The Geometry of Nonlinear Embeddings in Kernel Discriminant Analysis
We study the geometry of nonlinear embeddings in discriminant analysis with polynomial kernels and Gaussian kernel by identifying the population-level discriminant function that depends on the data distribution and the kernel.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
