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Probabilistic diffusion models have become essential for generating complex data structures such as images & videos. These models transform random noise into structured data, achieving high realism and utility across various domains. The model operates through two phases: a forward phase that gradually corrupts data with noise and a reverse phase that systematically reconstructs coherent… →

When applying Reinforcement Learning (RL) to real-world applications, two key challenges are often faced during this process. Firstly, the constant online interaction and update cycle in RL places major engineering demands on large systems designed to work with static ML models needing only occasional offline updates. Secondly, RL algorithms usually start from scratch, relying solely… →

Geometry problem-solving relies heavily on advanced reasoning skills to interpret visual inputs, process questions, and apply mathematical formulas accurately. Although vision-language models (VLMs) have shown progress in multimodal tasks, they still face significant limitations with geometry, particularly in executing unfamiliar mathematical operations, like calculating the cosine of non-standard angles. This challenge is amplified due to… →

The efficient training of vision models is still a major challenge in AI because Transformer-based models suffer from computational bottlenecks due to the quadratic complexity of self-attention mechanisms. Also, the ViTs, although extremely promising results on hard vision tasks, require extensive computational and memory resources, making them impossible to use under real-time or resource-constrained conditions.… →

Datasets and pre-trained models come with intrinsic biases. Most methods rely on spotting them by analyzing misclassified samples in a semi-automated human computer validation. Deep neural networks, typically fine-tuned foundational models, are widely used in sectors like healthcare, finance, and criminal justice, where biased predictions can have serious societal impacts. These models often function as… →

Text embedding, a central focus within natural language processing (NLP), transforms text into numerical vectors capturing the essential meaning of words or phrases. These embeddings enable machines to process language tasks like classification, clustering, retrieval, and summarization. By structuring data in vector form, embeddings provide a scalable and effective way for machines to interpret and… →

Meta has recently released NotebookLlama, an open version of Google’s NotebookLM that empowers researchers and developers with accessible, scalable solutions for interactive data analysis and documentation. NotebookLlama integrates large language models directly into an open-source notebook interface, similar to Jupyter or Google Colab, allowing users to interact with a trained LLM as they would with… →