This paper proposes an AI-enhanced Marketing Mix Modeling (MMM) framework that integrates machinelearning (ML), explainable AI (XAI), and large language models (LLMs) to evaluate marketing effectiveness withimproved predictive accuracy, interpretability, and practical applicability. Moving beyond traditional MMMapproaches, the framework employs the XGBoost algorithm to capture nonlinear relationships between multichannelmarketing investments and business outcomes. SHAP analysis further enhances model interpretability throughfeature-importance rankings, beeswarm visualizations, and dependence plots that quantify each channel’s marginalcontribution. In addition, a Claude-based LLM module translates […]

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Gowtham Reddy Kunduru is a lead software engineer with a successful career spanning healthcare, FinTech, and cloud architecture. His success wasn’t always assured; in fact, as he puts it, his “story began with setbacks.”  In college, I was written off as someone who would never amount to much. Being detained twice was humiliating, but it became the turning point in my life. I realized that if I didn’t take control, no one else would. So, I rebuilt myself […]

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Large Language Models (LLMs) suffer severe catastrophic forgetting when adapted sequentially to new tasks in a continual learning (CL) setting. Existing approaches are fundamentally limited: replay-based methods are impractical and privacy-violating, while strict orthogonality-based methods collapse under scale: each new task is projected onto an orthogonal complement, progressively reducing the residual degrees of freedom and eliminating forward transfer by forbidding overlap in shared representations. In this work, we introduce ELLA, a training framework built on the principle of […]

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arXiv:2601.12253v1 Announce Type: new Abstract: Efficient fine-tuning of visual-language models like CLIP has become crucial due to their large-scale parameter size and extensive pretraining requirements. Existing methods typically address either the issue of unseen classes or unseen domains in isolation, without considering a joint framework for both. In this paper, we propose textbf{Fed}erated Joint Learning for textbf{D}omain and textbf{C}lass textbf{G}eneralization, termed textbf{FedDCG}, a novel approach that addresses both class and domain generalization in federated learning settings. Our method […]

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arXiv:2601.12257v1 Announce Type: new Abstract: Conventional imaging requires a line of sight to create accurate visual representations of a scene. In certain circumstances, however, obtaining a suitable line of sight may be impractical, dangerous, or even impossible. Non-line-of-sight (NLOS) imaging addresses this challenge by reconstructing the scene from indirect measurements. Recently, passive NLOS methods that use an ordinary photograph of the subtle shadow cast onto a visible wall by the hidden scene have gained interest. These methods are […]

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Sharing some notes from two papers from the Physics of Language Models line of work Part 2.1 – Hidden Reasoning Process – https://shreyansh26.github.io/post/2024-09-21_physics-of-lms-2-1-grade-school-math-and-the-hidden-reasoning-process/ Part 3.1 – Knowledge Storage and Extraction – https://shreyansh26.github.io/post/2026-01-17_physics-of-lms-3-1-knowledge-storage-and-extraction/ submitted by /u/shreyansh26 [link] [comments]

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I have implemented dreamer but cannot get it to solve the walker2d environment. I copied and pasted much of the code from public repositories, but wrote the loss computation part myself. I’ve spent several days trying to debug the code and would really appreciate your help.. I’ve put a github link to the code. I’m suspecting the indexing might be wrong in the computation of lambda returns, but there could many other mistakes. I usually don’t post anything […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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arXiv:2601.01619v1 Announce Type: new Abstract: We show that for unconstrained Deep Linear Discriminant Analysis (LDA) classifiers, maximum-likelihood training admits pathological solutions in which class means drift together, covariances collapse, and the learned representation becomes almost non-discriminative. Conversely, cross-entropy training yields excellent accuracy but decouples the head from the underlying generative model, leading to highly inconsistent parameter estimates. To reconcile generative structure with discriminative performance, we introduce the emph{Discriminative Negative Log-Likelihood} (DNLL) loss, which augments the LDA log-likelihood with […]

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