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20 Nov

Cloudera Enhances Data Catalog and Metadata Management with Octopai Acquisition

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Cloudera, an enterprise data cloud solutions provider, has agreed to acquire Octopai’s data lineage and catalog platform. The acquisition is expected to be completed by the end of this month. This move significantly enhances Cloudera’s data catalog and metadata management capabilities. The struggle to achieve comprehensive data intelligence is a fundamental challenge faced by enterprises in a data-driven world. This issue is especially pronounced in highly regulated industries, such as finance, healthcare, and telecommunications. Organizations often struggle to gain data visibility over multiple data solutions across hybrid environments. Cloudera aims to address the current fragmented state of enterprise data by…

20 Nov

Hypergraph $p$-Laplacian equations for data interpolation and semi-supervised learning

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20 Nov

ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback

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[Submitted on 11 Apr 2024 (v1), last revised 19 Nov 2024 (this version, v4)] View a PDF of the paper titled ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback, by Ming Li and 6 other authors View PDF HTML (experimental) Abstract:To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that existing methods still face significant challenges in generating images that align with the image conditional controls. To this end, we propose ControlNet++, a novel approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency between generated images…

20 Nov

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

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[Submitted on 9 Nov 2023 (v1), last revised 19 Nov 2024 (this version, v2)] Authors:Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting Liu View a PDF of the paper titled A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions, by Lei Huang and 10 other authors View PDF HTML (experimental) Abstract:The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Nevertheless, LLMs are prone to hallucination, generating plausible…

20 Nov

Pentaho President Maggie Laird Discusses Company’s Return to Data Management Roots

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The AI boom is forcing companies to come to grips with an uncomfortable reality: Their data is not well managed. That’s good news for data intelligence companies like Pentaho, which is refocusing its efforts on data management and governance under Maggie Laird, who was promoted to president of the Hitachi-Vantara subsidiary in April. Laird recently joined the Big Data Debrief to chat about the impact of AI on data management, the massive opportunity it poses for data intelligence, and the new direction she is leading Pentaho to monetize that opportunity and help customers come to grips with their big data…

20 Nov

Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation

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[Submitted on 18 Nov 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation, by R"uveyda Yilmaz and 2 other authors View PDF HTML (experimental) Abstract:Automated cell segmentation in microscopy images is essential for biomedical research, yet conventional methods are labor-intensive and prone to error. While deep learning-based approaches have proven effective, they often require large annotated datasets, which are scarce due to the challenges of manual annotation. To overcome this, we propose a novel framework for synthesizing densely…

20 Nov

Masked Pre-training Enables Universal Zero-shot Denoiser

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[Submitted on 26 Jan 2024 (v1), last revised 17 Nov 2024 (this version, v2)] View a PDF of the paper titled Masked Pre-training Enables Universal Zero-shot Denoiser, by Xiaoxiao Ma and 7 other authors View PDF HTML (experimental) Abstract:In this work, we observe that model trained on vast general images via masking strategy, has been naturally embedded with their distribution knowledge, thus spontaneously attains the underlying potential for strong image denoising. Based on this observation, we propose a novel zero-shot denoising paradigm, i.e., Masked Pre-train then Iterative fill (MPI). MPI first trains model via masking and then employs pre-trained weight…

20 Nov

Key-Element-Informed sLLM Tuning for Document Summarization

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[Submitted on 7 Jun 2024 (v1), last revised 19 Nov 2024 (this version, v3)] View a PDF of the paper titled Key-Element-Informed sLLM Tuning for Document Summarization, by Sangwon Ryu and 4 other authors View PDF HTML (experimental) Abstract:Remarkable advances in large language models (LLMs) have enabled high-quality text summarization. However, this capability is currently accessible only through LLMs of substantial size or proprietary LLMs with usage fees. In response, smaller-scale LLMs (sLLMs) of easy accessibility and low costs have been extensively studied, yet they often suffer from missing key information and entities, i.e., low relevance, in particular, when input…

20 Nov

Tips on Building a Winning Data and AI Strategy from JPMC

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(Lewis-Tse/Shutterstock) With $274 billion in revenue last year and $3.3 trillion in assets under management, JPMorgan Chase has more resources than most to devote to building a winning data and AI strategy. But as James Massa, JPMorgan Chase’s senior executive director of software engineering and architecture, explained during his SolixEmpower keynote last week, even the biggest companies in the world must pay close attention to the data and AI details in order to succeed. In his Solix Empower 2024 keynote address, titled “Data Quality and Data Strategy for AI, Measuring AI Value, Testing LLMs, and AI Use Cases,” Massa provided…

20 Nov

4+3 Phases of Compute-Optimal Neural Scaling Laws

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[Submitted on 23 May 2024 (v1), last revised 17 Nov 2024 (this version, v2)] View a PDF of the paper titled 4+3 Phases of Compute-Optimal Neural Scaling Laws, by Elliot Paquette and 3 other authors View PDF Abstract:We consider the solvable neural scaling model with three parameters: data complexity, target complexity, and model-parameter-count. We use this neural scaling model to derive new predictions about the compute-limited, infinite-data scaling law regime. To train the neural scaling model, we run one-pass stochastic gradient descent on a mean-squared loss. We derive a representation of the loss curves which holds over all iteration counts…