AI

  In the quickly advancing field of Artificial Intelligence (AI), Deep Learning is becoming significantly more popular and stepping into every industry to make lives easier. Simultaneous Localization and Mapping (SLAM) in AI, which is an essential component of robots, driverless vehicles, and augmented reality systems, has been experiencing revolutionary advancements recently. SLAM involves reconstructing…

  Researchers have explored the potential of using synthetic images generated by text-to-image models to learn visual representations and pave the way for more efficient and bias-reduced machine learning. This new study from MIT researchers focuses on Stable Diffusion and demonstrates that training self-supervised methods on synthetic images can match or even surpass the performance…

  Recently, deep learning has been marked by a surge in research aimed at optimizing models for dynamic sparsity. In this scenario, sparsity patterns only reveal themselves at runtime, posing a formidable challenge to efficient computation. Addressing this challenge head-on, a group of researchers proposed a novel solution called Permutation Invariant Transformation (PIT), showcased in…

  In payments, understanding transactions is crucial for assessing risks in businesses. However, deciphering messy bank transaction data poses a challenge, as it is expressed in various ways across different banks. Existing solutions like Plaid and ChatGPT have limitations, such as low coverage and wordiness. To address this, a new solution called Slope TransFormer has…

  A key component of many creative projects is the capacity of the created visual content to remain consistent across different situations, as seen in Figure 1. These include drawing book illustrations, building brands, making comics, presentations, websites, and more. Establishing brand identification, enabling narrative, improving communication, and fostering emotional connection all depend on this…

  A group of researchers from Nvidia have developed a new technique called Tied-LoRA, which aims to improve the parameter efficiency of the Low-rank Adaptation (LoRA) method. The course uses weight tying and selective training to find the optimal balance between performance and trainable parameters. The researchers conducted experiments on different tasks and base language…

  The highly parameterized nature of complex prediction models makes describing and interpreting the prediction strategies difficult. Researchers have introduced a novel approach using topological data analysis (TDA), to solve the issue. These models, including machine learning, neural networks, and AI models, have become standard tools in various scientific fields but are often difficult to…

  The GPT-Vision model has caught everyone’s attention. People are excited about its ability to understand and generate content related to text and images. However, there’s a challenge – we don’t know precisely what GPT-Vision is good at and where it falls short. This lack of understanding can be risky, primarily if the model is…

  There has been a noticeable trend in Artificial General Intelligence (AGI) systems toward using pre-trained, adaptable representations, which provide task-agnostic advantages in various applications. Natural language processing (NLP) is a good example of this tendency since sophisticated models demonstrate flexibility with thorough knowledge covering several domains and tasks with straightforward instructions. The popularity of…

  A team of researchers from the University of Illinois Urbana-Champaign, Carnegie Mellon University, Georgia Institute of Technology, University of California Berkeley, Meta AI Research, and Mistral AI has developed a universal navigation system called GO To Any Thing (GOAT). This system is designed for extended autonomous operation in home and warehouse environments. GOAT is…