​ Fine-grained image classification is a computer vision task aiming to classify images into subcategories within a larger category. It involves the intricate identification of specific, often rare animals. Yet, they grapple with a need for more extensive training data, leading classifiers to struggle with… Read More »UC Berkeley Researchers Develop ALIA: A Breakthrough in Automated Language-Guided Image Augmentation for Fine-Grained Classification Tasks Mahmoud Ghorbel Artificial Intelligence Category – MarkTechPost

​ A big problem in space research is whether the same stars or galaxies are seen in different sky surveys. Telescopes today gather a ton of data about thousands or even billions of objects using various types of light. However, connecting this data from different… Read More »This Paper from Johns Hopkins Highlights Data Science’s Role in Accelerating Probabilistic Catalog Matching for Space Discoveries Across Time and Telescopes Niharika Singh Artificial Intelligence Category – MarkTechPost

​ Providing a virtual environment that matches the actual world, the recent widespread rise of 3D applications, including metaverse, VR/AR, video games, and physical simulators, has improved human lifestyle and increased productive efficiency. These programs are based on triangle meshes, which stand in for the… Read More »Can We Map Large-Scale Scenes in Real-Time without GPU Acceleration? This AI Paper Introduces ‘ImMesh’ for Advanced LiDAR-Based Localization and Meshing Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​ Researchers from Google Research and UIUC propose ZipLoRA, which addresses the issue of limited control over personalized creations in text-to-image diffusion models by introducing a new method that merges independently trained style and subject Linearly Recurrent Attentions (LoRAs). It allows for greater control and… Read More »Researchers from Google and UIUC Propose ZipLoRA: A Novel Artificial Intelligence Method for Seamlessly Merging Independently Trained Style and Subject LoRAs Sana Hassan Artificial Intelligence Category – MarkTechPost

​ The soaring capabilities of language models in real-world applications are often hindered by the intricate challenges associated with their large-scale training using conventional methods like standard backpropagation. Google DeepMind’s latest breakthrough, DiLoCo (Distributed Low-Communication), sets a new precedent in language model optimization. In the… Read More »Google DeepMind Researchers Introduce DiLoCo: A Novel Distributed, Low-Communication Machine Learning Algorithm for Effective and Resilient Large Language Model Training Niharika Singh Artificial Intelligence Category – MarkTechPost

​ FER is pivotal in human-computer interaction, sentiment analysis, affective computing, and virtual reality. It helps machines understand and respond to human emotions. Methodologies have advanced from manual extraction to CNNs and transformer-based models. Applications include better human-computer interaction and improved emotional response in robots,… Read More »Can AI Truly Understand Our Emotions? This AI Paper Explores Advanced Facial Emotion Recognition with Vision Transformer Models Mahmoud Ghorbel and Asif Razzaq Artificial Intelligence Category – MarkTechPost

​ Researchers from UC Berkeley introduce an unexplored approach to learning-based control problems, integrating reinforcement learning (RL) with user intervention signals. Utilizing off-policy RL on DAgger-style interventions, where human corrections guide the learning process, the proposed method performs superior on high-dimensional continuous control benchmarks and… Read More »Researchers at UC Berkeley Introduced RLIF: A Reinforcement Learning Method that Learns from Interventions in a Setting that Closely Resembles Interactive Imitation Learning Adnan Hassan Artificial Intelligence Category – MarkTechPost

​ The problem of sparsity and degeneracy issues in LiDAR SLAM has been addressed by introducing Quatro++, a robust global registration framework developed by researchers from the KAIST. This method has surpassed previous success rates and improved loop closing accuracy and efficiency through ground segmentation.… Read More »KAIST Researchers Introduce Quatro++: A Robust Global Registration Framework Exploiting Ground Segmentation for Loop Closing in LiDAR SLAM Sana Hassan Artificial Intelligence Category – MarkTechPost

​ In the pursuit of refining cancer therapies, researchers have introduced a groundbreaking solution that significantly elevates our comprehension of tumor dynamics. This study centers on precisely predicting intratumoral fluid pressure (IFP) and liposome accumulation, unveiling a pioneering physics-informed deep learning model. This innovative approach… Read More »This AI Research Presents a Physics-Based Deep Learning for Predicting IFP and Liposome Accumulation Madhur Garg Artificial Intelligence Category – MarkTechPost

​ The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise. From data collection and cleaning to feature engineering, model building, tuning, and deployment, ML… Read More »Boosting developer productivity: How Deloitte uses Amazon SageMaker Canvas for no-code/low-code machine learning Chida Sadayappan AWS Machine Learning Blog