Search Results for "joint"

A design paradigm and corresponding enabling technologies, in which sensing and comms systems are integrated to efficiently utilize congested wireless/hardware resources, and even to pursue mutual benefits. Waveform Design and Signal Processing Aspects for Fusion of Wireless Communications and Radar Sensing. Supported by IEEE ComSoc ISAC Emerging Technology Initiative (ETI). Integrating Sensing and Communications for Ubiquitous IoT. Include reproducible codes, good papers and a research libary.

...Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units). Given certain structural properties, PCs enable different range of tractable exact probabilistic queries such as computing marginals, conditionals, maximum a posteriori (MAP), and more advanced probabilistic queries.

i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture.

This is a SDK which can nest/layout the part/geometry/polygon on the material or sheet intelligently, you can get the best utilization of the materials, this SDK also support read/write DXF/DWG files and part-in-part nesting. New nesting lagorithm is implenmented, more good to support true shape part or material.

A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations.

CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. Our work allows computers to recognize objects in images, what is distinctive about our work is that we also...