Últimas noticias sobre redes neuronales

The California two-spot octopus (Bimaculoides) is the first octopus species to have its genome sequenced and is very helpful for studying cephalopods. Roger T. HanlonA new study dives deeper into the amazing adaptations of the cephalopod brain. The post Octopuses rewrite their own genes to survive freezing temperatures appeared first...

Robotics insights FluidLab is a simulation tool from researchers at the MIT CSAIL designed to enhance robot learning for complex fluid manipulation tasks. It uses a physics simulator capable of seamlessly calculating and simulating various materials and their interactions, all while harnessing the power of graphics processing units (GPUs) for...

A new multimodal technique blends major self-supervised learning methods to learn more similarly to humans....

Today, AI based on neural networks is at a very interesting stage of its development. It has clearly taken off: we see numerous applications from reading CT scans to picking fruits. But adoption rates vary a lot. Recommendation engines, customer support bots, and other stuff that's been called ”internet AI”...

The robot even created its own salad recipe after learning from examples. University of CambridgeIt may not make it on 'Top Chef,' but the robot's learning abilities are still impressive. The post This robot ‘chef’ can follow video instructions to make a very simple salad appeared first on Popular Science....

In this post we bring you all the paper awards finalists and winners presented during the 2023 edition of the IEEE International Conference on Robotics and Automation (ICRA). Congratulations to the winners and finalists! ICRA 2023 Outstanding Paper Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion, by Fawcett, Randall;...

The mind-brain problem is to bridge relations between in higher mental events and in lower neural events. To address this, some mathematical models have been proposed to explain how the brain can represent the discriminative structure of qualia, but they remain unresolved due to a lack of validation methods. To...

In recent years, "U-shaped" neural networks featuring encoder and decoder structures have gained popularity in the field of medical image segmentation. Various variants of this model have been developed. Nevertheless, the evaluation of these models has received less attention compared to model development. In response, we propose a comprehensive method...

We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and lightweight neural network that uses Progressive Learning (PL) to perform audio-visual speech separation in noisy environments. To this end, we adopt the Asynchronous Fully Recurrent Convolutional Neural Network (A-FRCNN), which has shown successful results in audio-only speech separation. Our architecture...

This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrollers in the power domain of milliwatts, with less than 0.5MB memory available for storing convolutional neural network (CNN) weights. The proposed quantized network architecture with 422k parameters,...

Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year. Clinicians use medical imaging modalities such as computed tomography and positron emission tomography to detect the presence of a tumor, and...

Shortcut learning, or `Clever Hans effect` refers to situations where a learning agent (e.g., deep neural networks) learns spurious correlations present in data, resulting in biased models. We focus on finding shortcuts in deep learning based spoofing countermeasures (CMs) that predict whether a given utterance is spoofed or not. While...