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Tag: generation

  • Optimizing Token Generation in PyTorch Decoder Models

    Optimizing Token Generation in PyTorch Decoder Models Hiding host-device synchronization via CUDA stream interleaving The post Optimizing Token Generation in PyTorch Decoder Models appeared first on Towards Data Science. Chaim Rand Go to original source

  • On Generation in Metric Spaces

    On Generation in Metric Spaces arXiv:2602.07710v1 Announce Type: new Abstract: We study generation in separable metric instance spaces. We extend the language generation framework from Kleinberg and Mullainathan [2024] beyond countable domains by defining novelty through metric separation and allowing asymmetric novelty parameters for the adversary and the generator. We introduce the $(varepsilon,varepsilon’)$-closure dimension, a…

  • Worst-case generation via minimax optimization in Wasserstein space

    Worst-case generation via minimax optimization in Wasserstein space arXiv:2512.08176v1 Announce Type: new Abstract: Worst-case generation plays a critical role in evaluating robustness and stress-testing systems under distribution shifts, in applications ranging from machine learning models to power grids and medical prediction systems. We develop a generative modeling framework for worst-case generation for a pre-specified risk,…

  • Beyond Code Generation: Continuously Evolve Text with LLMs

    Beyond Code Generation: Continuously Evolve Text with LLMs Long-running content evolution and an introduction to result analysis The post Beyond Code Generation: Continuously Evolve Text with LLMs appeared first on Towards Data Science. Julian Mendel Go to original source

  • Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization

    Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization arXiv:2505.11089v1 Announce Type: new Abstract: In this paper, we consider a score-based Integer Programming (IP) approach for solving the Bayesian Network Structure Learning (BNSL) problem. State-of-the-art BNSL IP formulations suffer from the exponentially large number of variables and constraints. A standard approach in IP…

  • Structure-Preference Enabled Graph Embedding Generation under Differential Privacy

    Structure-Preference Enabled Graph Embedding Generation under Differential Privacy arXiv:2501.03451v1 Announce Type: new Abstract: Graph embedding generation techniques aim to learn low-dimensional vectors for each node in a graph and have recently gained increasing research attention. Publishing low-dimensional node vectors enables various graph analysis tasks, such as structural equivalence and link prediction. Yet, improper publication opens…

  • Unlocking the Untapped Potential of Retrieval-Augmented Generation (RAG) Pipelines

    Unlocking the Untapped Potential of Retrieval-Augmented Generation (RAG) Pipelines Essential Metrics and Methods to Enhance Performance Across Retrieval, Generation, and End-to-End Pipelines Continue reading on Towards Data Science » Saleh Alkhalifa Go to original source