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Tagging Mountaineering Accident Reports Using bart-large-mnli | by Karla Hernández | Feb, 2024

Data ScienceFebruary 5, 202445Views0Likes0Comments
I discovered the Himalayan Database a few weeks ago and decided to create a few “whimsical” visualizations based on this dataset. In two previous articles I created a simple elevation plot for Everest expeditions and a plot showing the relative number of deaths for 5 Himalayan peaks. This time I wanted to explore expedition accident…

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Encoding Categorical Variables: A Deep Dive into Target Encoding | by Juan Jose Munoz | Feb, 2024

Data ScienceFebruary 5, 202460Views0Likes0Comments
Data comes in different shapes and forms. One of those shapes and forms is known as categorical data. This poses a problem because most Machine Learning algorithms use only numerical data as input. However, categorical data is usually not a challenge to deal with, thanks to simple, well-defined functions that transform them into numerical values.…

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Cropping Landsat Scenes from their Bounding Box using Python | by Conor O’Sullivan | Feb, 2024

Data ScienceFebruary 4, 202452Views0Likes0Comments
Removing the outer border of Landsat satellite images using the stac file (source: author)Telling stories with satellite images is straightforward. The mesmerising landscapes do most of the work. Yet, visualising them takes some work such as selecting and scaling the RGB channels. In this article, we will go further. We will see how we can…

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This AI Paper from China Introduces SegMamba: A Novel 3D Medical Image Segmentation Mamba Model Designed to Effectively Capture Long-Range Dependencies within Whole Volume Features at Every Scale

AI NewsFebruary 4, 202455Views0Likes0Comments
Enhancing the receptive field of models is crucial for effective 3D medical image segmentation. Traditional convolutional neural networks (CNNs) often struggle to capture global information from high-resolution 3D medical images. One proposed solution is the utilization of depth-wise convolution with larger kernel sizes to capture a wider range of features. However, CNN-based approaches need help…

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This AI Paper from NTU and Apple Unveils OGEN: A Novel AI Approach for Boosting Out-of-Domain Generalization in Vision-Language Models

AI NewsFebruary 3, 202452Views0Likes0Comments
Large-scale pre-trained vision-language models, exemplified by CLIP (Radford et al., 2021), exhibit remarkable generalizability across diverse visual domains and real-world tasks. However, their zero-shot in-distribution (ID) performance faces limitations on certain downstream datasets. Additionally, when evaluated in a closed-set manner, these models often struggle with out-of-distribution (OOD) samples from novel classes, posing safety risks in…

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Google Deepmind and University of Toronto Researchers’ Breakthrough in Human-Robot Interaction: Utilizing Large Language Models for Generative Expressive Robot Behaviors

RoboticsFebruary 3, 202474Views0Likes0Comments
Numerous challenges underlying human-robot interaction exist. One such challenge is enabling robots to display human-like expressive behaviors. Traditional rule-based methods need more scalability in new social contexts, while the need for extensive, specific datasets limits data-driven approaches. This limitation becomes pronounced as the variety of social interactions a robot might encounter increases, creating a demand…

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