66 research outputs found

    FROST -- Fast row-stochastic optimization with uncoordinated step-sizes

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    In this paper, we discuss distributed optimization over directed graphs, where doubly-stochastic weights cannot be constructed. Most of the existing algorithms overcome this issue by applying push-sum consensus, which utilizes column-stochastic weights. The formulation of column-stochastic weights requires each agent to know (at least) its out-degree, which may be impractical in e.g., broadcast-based communication protocols. In contrast, we describe FROST (Fast Row-stochastic-Optimization with uncoordinated STep-sizes), an optimization algorithm applicable to directed graphs that does not require the knowledge of out-degrees; the implementation of which is straightforward as each agent locally assigns weights to the incoming information and locally chooses a suitable step-size. We show that FROST converges linearly to the optimal solution for smooth and strongly-convex functions given that the largest step-size is positive and sufficiently small.Comment: Submitted for journal publication, currently under revie

    GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond

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    With the rapid advancement of large language models (LLMs), there is a pressing need for a comprehensive evaluation suite to assess their capabilities and limitations. Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may inadvertently encourage cherry-picking favored settings and prompts for better results. In this work, we introduce GPT-Fathom, an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals. We systematically evaluate 10+ leading LLMs as well as OpenAI's legacy models on 20+ curated benchmarks across 7 capability categories, all under aligned settings. Our retrospective study on OpenAI's earlier models offers valuable insights into the evolutionary path from GPT-3 to GPT-4. Currently, the community is eager to know how GPT-3 progressively improves to GPT-4, including technical details like whether adding code data improves LLM's reasoning capability, which aspects of LLM capability can be improved by SFT and RLHF, how much is the alignment tax, etc. Our analysis sheds light on many of these questions, aiming to improve the transparency of advanced LLMs.Comment: Accepted by NAACL 202

    EglN2 associates with the NRF1‐PGC1α complex and controls mitochondrial function in breast cancer

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    Abstract The EglN2/PHD1 prolyl hydroxylase is an important oxygen sensor contributing to breast tumorigenesis. Emerging studies suggest that there is functional cross talk between oxygen sensing and mitochondrial function, both of which play an essential role for sustained tumor growth. However, the potential link between EglN2 and mitochondrial function remains largely undefined. Here, we show that EglN2 depletion decreases mitochondrial respiration in breast cancer under normoxia and hypoxia, which correlates with decreased mitochondrial DNA in a HIF1/2α‐independent manner. Integrative analyses of gene expression profile and genomewide binding of EglN2 under hypoxic conditions reveal nuclear respiratory factor 1 (NRF1) motif enrichment in EglN2‐activated genes, suggesting NRF1 as an EglN2 binding partner. Mechanistically, by forming an activator complex with PGC1α and NRF1 on chromatin, EglN2 promotes the transcription of ferridoxin reductase (FDXR) and maintains mitochondrial function. In addition, FDXR, as one of effectors for EglN2, contributes to breast tumorigenesis in vitro and in vivo. Our findings suggest that EglN2 regulates mitochondrial function in ERα‐positive breast cancer

    Open X-Embodiment:Robotic learning datasets and RT-X models

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    Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io
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