Latent space models are widely used for analyzing high-dimensional discrete data matrices, such as patient-feature matrices in electronic health records (EHRs), by capturing complex dependence structures through low-dimensional embeddings. However, estimation becomes challenging in the imbalanced regime, where one matrix dimension is much larger than the other. In EHR applications, cohort sizes are often limited by disease prevalence or data availability, whereas the feature space remains extremely large due to the breadth of medical coding system. Motivated by […]

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This is not an article delving into the details of AWS Secrets Manager or SSM Parameter Store, but a quick overview of how to use them to store secrets like API keys as I did in this project. https://github.com/avr2002/files-api/tree/main?embedable=true AWS Secrets Manager AWS Secrets Manager helps you to securely store, rotate, and manage sensitive information like API keys, database credentials, OAuth tokens, and other secrets. Instead of hardcoding credentials in your application code, you can store them in […]

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Recognizing emotions from written text is a very important part of Natural Language Processing (NLP) and is commonly used for feeling or sentiment analysis or keeping track of someone’s mental health status. This study uses a readable emotion-detecting framework with a RoBERTa-base model that has been modified and trained specifically for the Emotions for NLP dataset and provides an accuracy of 0.924% and f1 score of 0.925%. The main contributions of this study are the use of four […]

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One crucial factor behind the success of deep learning lies in the implicit bias induced by noise inherent in gradient-based training algorithms. Motivated by empirical observations that training with noisy labels improves model generalization, we delve into the underlying mechanisms behind stochastic gradient descent (SGD) with label noise. Focusing on a two-layer over-parameterized linear network, we analyze the learning dynamics of label noise SGD, unveiling a two-phase learning behavior. In emph{Phase I}, the magnitudes of model weights progressively […]

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arXiv:2603.26731v1 Announce Type: new Abstract: How much scene context a single object carries is a well-studied question in human scene perception, yet how this capacity is organized in vision-language models (VLMs) remains poorly understood, with direct implications for the robustness of these models. We investigate this question through a systematic behavioral and mechanistic analysis of contextual inference from single objects. Presenting VLMs with single objects on masked backgrounds, we probe their ability to infer both fine-grained scene category […]

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The launch of the Elon Musk Grok has caused a massive storm within the AI community. Grok is an xAI-powered tool, connected with X (formerly Twitter), and its sole purpose is to improve the level of engagement and intelligence of the chatbot. But the big question is, what are the different Grok AI versions, and why is it important to know the differences before using them? This Grok AI Review will go into detail about Grok 1.0 to […]

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arXiv:2603.05518v1 Announce Type: new Abstract: Recent advances in large multimodal models (LMMs) have enabled instruction-based image editing, allowing users to modify visual content via natural language descriptions. However, existing approaches often struggle with high-level semantic reasoning and visual consistency, particularly under ambiguous or complex instructions. To address these challenges, we propose CoEditor++, a cognitively structured, training-free framework that decomposes editing into “what to edit” and “how to edit” through two cognitive stages with a reflective self-selection mechanism, enabling […]

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