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:2601.10768v1 Announce Type: new Abstract: This paper investigates complex product-innovation processes using models grounded in a set of heuristics. Each heuristic is expressed through simple trends — increasing, decreasing, or constant — which serve as minimally information-intensive quantifiers, avoiding reliance on numerical values or rough sets. A solution to a trend model is defined as a set of scenarios with possible transitions between them, represented by a transition graph. Any possible future or past behaviour of the system […]

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Word embeddings, attention, and positional encodings and the specific problem each one was built to solve. Blogging my understanding as I go through the lecture series of “CS 639: Introduction to Foundation Models” at University of Wisconsin-Madison. Photo by Gaelle Marcel on Unsplash How Do Machines Even Understand Words? Before we get into the fancy stuff, it’s worth pausing on this simple question. Text is just characters to a machine, so how do we get it to understand meaning? A […]

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Introduction In modern cloud-native architectures, it’s increasingly common to run workloads in one cloud provider while needing to access resources in another. Whether you’re running a multi-cloud strategy, migrating between providers, or building a distributed system, your Kubernetes pods need secure, passwordless authentication across AWS, Azure, and GCP. This guide demonstrates how to implement cross-cloud authentication using industry best practices: AWS IRSA (IAM Roles for Service Accounts) Azure Workload Identity GCP Workload Identity Federation We’ll cover three real-world […]

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arXiv:2602.04009v1 Announce Type: new Abstract: Prompt-guided generative AI models have rapidly expanded across vision and language domains, producing realistic and diverse outputs from textual inputs. The growing variety of such models, trained with different data and architectures, calls for principled methods to identify which types of prompts lead to distinct model behaviors. In this work, we propose PromptSplit, a kernel-based framework for detecting and analyzing prompt-dependent disagreement between generative models. For each compared model pair, PromptSplit constructs a […]

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arXiv:2601.10555v1 Announce Type: cross Abstract: The causalfe package provides a Python implementation of Causal Forests with Fixed Effects (CFFE) for estimating heterogeneous treatment effects in panel data settings. Standard causal forest methods struggle with panel data because unit and time fixed effects induce spurious heterogeneity in treatment effect estimates. The CFFE approach addresses this by performing node-level residualization during tree construction, removing fixed effects within each candidate split rather than globally. This paper describes the methodology, documents the […]

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arXiv:2601.19040v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. Existing augmentation methods, however, typically rely on heuristics and static paradigms. Motivated by dynamic data optimization, which shows that the contribution of samples varies across training stages, we propose OATS (Online Data Augmentation for Time Series Foundation Models), a principled strategy that generates synthetic data tailored […]

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