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In image recognition, researchers and developers constantly seek innovative approaches to enhance the accuracy and efficiency of computer vision systems. Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and ... | 1 INTRODUCTION Self-attention-based architectures, in particular Transformers (Vaswani et al., 2017), have become the model of choice in natural language processing (NLP). The dominant approach is to pre-train on a large text corpus and then fine-tune on a smaller task-specific dataset (Devlin et al., 2019). Thanks to Tr... |
Software is quickly replacing what was previously a mechanical realm. These recent technological advances are replacing human expertise to some extent as well, especially when it comes to domains related to image processing and software-driven mobility. In the past, sensors were used to capture clear photos in low ligh... | 1. Preliminaries: mip-NeRF Let us first describe how a fully-trained mip-NeRF [3] renders the color of a single ray cast into the scene r(t) = o+td, where o and d are the origin and direction of the ray respectively, and t denotes distance along the ray. In mip- NeRF, a sorted vector of distances t is defined and the ray... |
The capacity to infer user preferences from past behaviors is crucial for effective personalized suggestions. The fact that many products don’t have star ratings makes this task exponentially more challenging. Past actions are generally interpreted in a binary form to indicate whether or not a user has interacted with ... | 1 Introduction Understanding preferences based on users’ historical activities is key for personalized recommendation. This is particularly challenging when explicit ratings on many items are not available. In this scenario, historical activities are typically viewed as binary, representing whether a user has interacte... |
In the 1920s, numerical weather prediction (NWP) emerged. They are pervasive and help with economic planning in important industries, including transportation, logistics, agriculture, and energy production. Numerous lives have been saved by accurate weather predictions that warned of severe catastrophes in advance. Ove... | 1 Introduction The beginnings of modern numerical weather prediction (NWP) can be traced to the 1920s. Now ubiquitous, they contribute to economic planning in key sectors such as transport, logistics, agriculture, and energy production. Accurate weather forecasts have saved countless human lives by providing advance no... |
"Recent advancements in deep neural networks have enabled new approaches to address anatomical segme(...TRUNCATED) | "GAGGION et al.: HYBRIDGNET 1 Improving anatomical plausibility in medical image segmentation via hy(...TRUNCATED) |
"In a remarkable breakthrough, researchers from Google, Carnegie Mellon University, and Bosch Center(...TRUNCATED) | "1 INTRODUCTION Evaluating the robustness of deep learning models to norm bounded adversarial pertur(...TRUNCATED) |
"Effective robot operation requires more than just blind obedience to predetermined commands. Robots(...TRUNCATED) | "TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priors Gabriel Sarch1∗, Zhaoyuan F(...TRUNCATED) |
"The highly parameterized nature of complex prediction models makes describing and interpreting the (...TRUNCATED) | "§1 Our GTDA method for Reeb nets & prediction functions In this paper, we developed a framework to(...TRUNCATED) |
"The number of CT scans performed and the data processing capacity available have grown over the pas(...TRUNCATED) | "1 TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT images Jakob Wasserthal (...TRUNCATED) |
"Imagine your quadruped friend playing outside or your car showcased in an exclusive showroom. Creat(...TRUNCATED) | "in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text pro(...TRUNCATED) |
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