Neural field network doesn’t need to be trained on other samples
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Transforming edge computing through intelligent human interface solutions.
Latest Posts
A short guide to understanding the edge neural network design.
Proven benchmarks provide a structured method for comparing ML/DL products and services.
The student article explains an artificial intelligence-based system to detect human body patterns and movements for multiple targets in real-time to recognize their behaviors and classify them as either normal or abnormal.
A new type of neural network aced a virtual wine-tasting test and promises a less energy-hungry version of artificial intelligence.
The new design is stackable and reconfigurable, for swapping out and building on existing sensors and neural network processors.
Columbia computer scientists have built an algorithm that blocks a rogue microphone from correctly hearing your words—in English so far—80% of the time
Using an artificial neural network, researchers at ETH Zurich have created the first high-resolution global vegetation height map for 2020 from satellite images.
5 minutes read
In this episode, we talk about how robotic technology is being leveraged to create a system capable of handling pizza dough and to offer an autonomous alternative that should address the primary shortcomings of conventional wheelchairs.
A framework that uses both audio and image modality for event classification– tested on COVID-19 prescreening and battlefield object detection, later evaluated on the Raspberry Pi 4 single-board computer.
In this episode, we talk about how a group of researchers were inspired by the adaptive immune system found in humans to fortify vulnerable neural networks and a joint effort between universities to create electric skin with unmatched performance.
The adaptive immune system serves as a template for defending neural nets from confusion-sowing attacks.
4 minutes read
Designed to address the OOD data false prediction by adaptively synthesizing virtual outliers that can maintain the model’s decision boundary during training.
Over the last decade, deep neural networks have emerged as the solution to several AI complex applications from speech recognition and object detection to autonomous vehicular systems.
By reconfiguring neural networks in artificial intelligence (AI) devices, a multi-institute team that included Penn State researchers facilitated AI systems to continually learn and adapt new data and tasks in ways that were not possible or practical before.
4 minutes read
