arXiv:2406.01552v2 Announce Type: replace Abstract: Tensors are a fundamental data structure for many scientific contexts, such as time series analysis, materials science, and physics, among many others. Improving our ability to produce and handle tensors is essential to efficiently address problems in these domains. In this paper, we show how to exploit the underlying symmetries of functions that map tensors to tensors. More concretely, we develop universally expressive equivariant machine learning architectures on tensors that exploit that, in […]

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arXiv:2602.07152v1 Announce Type: new Abstract: The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI Trojans. These AI trojans are malicious, hidden backdoors intentionally embedded within an AI model that can cause a system to fail in unexpected ways, or allow a malicious actor to hijack the AI model at will. This multi-year initiative helped to map out the complex nature of the threat, […]

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arXiv:2603.05511v1 Announce Type: new Abstract: As artificial intelligence shifts from pure tool for delegation toward agentic collaboration, its use in the arts can shift beyond the exploration of machine autonomy toward synergistic co-creation. While our earlier robotic works utilized automation to distance the artist’s intent from the final mark, we present Companion: an artistic apparatus that integrates a drawing robot with Large Language Models (LLMs) to re-center human-machine presence. By leveraging in-context learning and real-time tool use, the […]

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arXiv:2602.23588v1 Announce Type: new Abstract: Large unimodal foundation models for vision and language encode rich semantic structures, yet aligning them typically requires computationally intensive multimodal fine-tuning. Such approaches depend on large-scale parameter updates, are resource intensive, and can perturb pretrained representations. Emerging evidence suggests, however, that independently trained foundation models may already exhibit latent semantic compatibility, reflecting shared structures in the data they model. This raises a fundamental question: can cross-modal alignment be achieved without modifying the models […]

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arXiv:2601.12249v1 Announce Type: new Abstract: Breast cancer is one of the most common cancers among women worldwide, and its accurate and timely diagnosis plays a critical role in improving treatment outcomes. This thesis presents an innovative framework for detecting malignant masses in mammographic images by integrating the Pyramid Adaptive Atrous Convolution (PAAC) and Transformer architectures. The proposed approach utilizes Multi-Scale Feature Fusion to enhance the extraction of features from benign and malignant tissues and combines Dice Loss and […]

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arXiv:2307.07785v2 Announce Type: replace Abstract: The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset. Classical information criteria typically consider the large-data limit, penalizing model size. However, these criteria are not appropriate in modern settings where overparameterized models tend to perform well. For any overparameterized model, we show that there exists a dual underparameterized model that possesses the same marginal likelihood, thus establishing […]

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When I first heard “cloud commitment insurance,” I nodded politely and had absolutely no idea what anyone was talking about. Insurance? For cloud commitments? What gets insured — the server? The discount? The contract? And who’s the insurance company here? I had several sessions with the Archera team trying to wrap my head around it. Here’s the thing that made it so confusing: their product is genuinely simple. So simple that the simplicity itself became the barrier. There […]

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It costs millions of dollars and months of computing time to train a large language model from the ground up. You most likely never need to do it. Fine-tuning lets you adapt pre-trained language models to your needs in hours or days, not months, with a fraction of the resources. This tutorial takes you from theory to practice: you’ll learn the four core fine-tuning techniques, code a complete training pipeline in Python, and learn the techniques that separate […]

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Reinforcement learning has been widely applied to diffusion and flow models for visual tasks such as text-to-image generation. However, these tasks remain challenging because diffusion models have intractable likelihoods, which creates a barrier for directly applying popular policy-gradient type methods. Existing approaches primarily focus on crafting new objectives built on already heavily engineered LLM objectives, using ad hoc estimators for likelihood, without a thorough investigation into how such estimation affects overall algorithmic performance. In this work, we provide […]

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