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Training a high-quality machine learning model requires careful data and feature preparation. To fully utilize raw data stored as tables in Databricks, running ETL pipelines and feature engineering may be required to transform the raw data into helpful feature tables. If your table is large, this step could be very time-consuming. We are excited to announce that the Photon Engine can now be enabled in Databricks Machine Learning Runtime, capable of speeding up spark jobs and feature engineering workloads by 2x or more. “By enabling Photon and using a new PIT join, the time required to generate the training dataset…

arXiv:2408.00315v1 Announce Type: new Abstract: Recently Diffusion-based Purification (DiffPure) has been recognized as an effective defense method against adversarial examples. However, we find DiffPure which directly employs the original pre-trained diffusion models for adversarial purification, to be suboptimal. This is due to an inherent trade-off between noise purification performance and data recovery quality. Additionally, the reliability of existing evaluations for DiffPure is questionable, as they rely on weak adaptive attacks. In this work, we propose a novel Adversarial Diffusion Bridge Model, termed ADBM. ADBM directly constructs a reverse bridge from the diffused adversarial data back to its original clean examples,…

arXiv:2408.00286v1 Announce Type: new Abstract: 3D object detection is essential for understanding 3D scenes. Contemporary techniques often require extensive annotated training data, yet obtaining point-wise annotations for point clouds is time-consuming and laborious. Recent developments in semi-supervised methods seek to mitigate this problem by employing a teacher-student framework to generate pseudo-labels for unlabeled point clouds. However, these pseudo-labels frequently suffer from insufficient diversity and inferior quality. To overcome these hurdles, we introduce an Agent-based Diffusion Model for Semi-supervised 3D Object Detection (Diff3DETR). Specifically, an agent-based object query generator is designed to produce object queries that effectively adapt to dynamic scenes…

arXiv:2408.00584v1 Announce Type: new Abstract: Rebuses are puzzles requiring constrained multi-step reasoning to identify a hidden phrase from a set of images and letters. In this work, we introduce a large collection of verbalized rebuses for the Italian language and use it to assess the rebus-solving capabilities of state-of-the-art large language models. While general-purpose systems such as LLaMA-3 and GPT-4o perform poorly on this task, ad-hoc fine-tuning seems to improve models' performance. However, we find that performance gains from training are largely motivated by memorization. Our results suggest that rebus solving remains a challenging test bed to evaluate large language…

(Funtap/Shutterstock) Anaconda Inc., an open ecosystem for sourcing, building, and deploying data science and AI initiatives, announced the release of Anaconda Code within its Anaconda Toolbox for Excel. This new feature enables users to write and execute Python code directly within Microsoft Excel. The new add-in aims to provide users with greater control, security, and flexibility over their Python environments. It helps with enhanced functionality and streamlined workflows. Running the code locally also reduces dependence on network communication and allows users to keep all code and data securely within the Excel workbook.  Microsoft introduced Python in Excel in August 2023…