Bootstrapped pretraining, i.e., the reuse of a pretrained base model for further pretraining, such as continual pretraining or model growth, is promising at reducing the cost of training language models from scratch. However, its effectiveness remains unclear, especially when applied to overtrained base models. In this work, we empirically study the scaling behavior of bootstrapped pretraining and find that its scaling efficiency diminishes in a predictable manner: The scaling exponent with respect to second-stage pretraining tokens decreases logarithmically with the number of tokens used to pretrain the base model. The joint dependence on first- and second-stage tokens is accurately modeled by a simple scaling law. Such saturation effect reveals a fundamental trade-off in multi-stage pretraining strategies: the more extensively a model is pretrained, the less additional benefit bootstrapping provides. Our findings provide practical insights for efficient language model training and raise important considerations for the reuse of overtrained models.

The unprecedented growth of deep learning models has enabled remarkable advances but introduced substantial computational bottlenecks. A key factor contributing to training efficiency is batch-size and learning-rate scheduling in stochastic gradient methods. However, naive scheduling of these hyperparameters can degrade optimization efficiency and compromise generalization. Motivated by recent theoretical insights, we investigated how the batch size and learning rate should be increased during training to balance efficiency and convergence. We analyzed this problem on the basis of stochastic first-order oracle (SFO) complexity, defined as the expected number of gradient evaluations needed to reach an $ε$-approximate stationary point of the empirical loss. We theoretically derived optimal growth schedules for the batch size and learning rate that reduce SFO complexity and validated them through extensive experiments. Our results offer both theoretical insights and practical guidelines for scalable and efficient large-batch training in deep learning.

The swift progress of Multi-modal Large Models (MLLMs) has showcased their impressive ability to tackle tasks blending vision and language. Yet, most current models and benchmarks cater to scenarios with a narrow scope of visual and textual contexts. These models often fall short when faced with complex comprehension tasks, which involve navigating through a plethora of irrelevant and potentially misleading information in both text and image forms. To bridge this gap, we introduce a new, more demanding task known as Interleaved Image-Text Comprehension (IITC). This task challenges models to discern and disregard superfluous elements in both images and text to accurately answer questions and to follow intricate instructions to pinpoint the relevant image. In support of this task, we further craft a new VEGA dataset, tailored for the IITC task on scientific content, and devised a subtask, Image-Text Association (ITA), to refine image-text correlation skills. Our evaluation of four leading closed-source models, as well as various open-source models using VEGA, underscores the rigorous nature of IITC. Even the most advanced models, such as Gemini-1.5-pro and GPT4V, only achieved modest success. By employing a multi-task, multi-scale post-training strategy, we have set a robust baseline for MLLMs on the IITC task, attaining an $85.8\%$ accuracy rate in image association and a $0.508$ Rouge score. These results validate the effectiveness of our dataset in improving MLLMs capabilities for nuanced image-text comprehension.

ChatGPT is a large language model developed by OpenAI. Despite its impressive performance across various tasks, no prior work has investigated its capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of ChatGPT on various benchmark biomedical tasks, such as relation extraction, document classification, question answering, and summarization. To the best of our knowledge, this is the first work that conducts an extensive evaluation of ChatGPT in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot ChatGPT even outperforms the state-of-the-art fine-tuned generative transformer models, such as BioGPT and BioBART. This suggests that ChatGPT's pre-training on large text corpora makes it quite specialized even in the biomedical domain. Our findings demonstrate that ChatGPT has the potential to be a valuable tool for various tasks in the biomedical domain that lack large annotated data.

Abstract: The Gaussian Elimination with Partial Pivoting (GEPP) is a classical algorithm for solving systems of linear equations. Although in specific cases the loss of precision in GEPP due to roundoff errors can be very significant, empirical evidence strongly suggests that for a {\it typical} square coefficient matrix, GEPP is numerically stable. We obtain a (partial) theoretical justification of this phenomenon by showing that, given the random n n standard Gaussian coefficient matrix A, the {\it growth factor} of the Gaussian Elimination with Partial Pivoting is at most polynomially large in n with probability close to one. This implies that with probability close to one the number of bits of precision sufficient to solve Ax b to m bits of accuracy using GEPP is m O(logn), which improves an earlier estimate m O(log2n) of Sankar, and which we conjecture to be optimal by the order of magnitude. Abstract: Linear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing.

QMI Market Research Published Latest Machine Learning as a Service Market 2032 Study with an in-depth analysis of the current scenario, the Market size, demand, growth pattern, trends, and forecast. By following several steps of collecting and analyzing market data, this finest market research report is structured by expert team. This Machine Learning as a Service Market report highlights key market dynamics of the sector and encompasses historic data, present market trends, environment, technological innovation, upcoming technologies, and technical progress in the related industry. Moreover, the Machine Learning as a Service report also contains all the information including market definition, classifications, key developments, applications, and engagements while detailing the actions of key players with respect to product launches, joint ventures, developments, mergers, and acquisitions and effects of the same in terms of sales, import, export, revenue and CAGR values. An excellent market research report can be generated only with the leading attributes such as the highest level of spirit, practical solutions, committed research and analysis, innovation, talent solutions, integrated approaches, the most up-to-date technology, and dedication.

Major players in the Machine Learning Courses market are profiled with company overview, financial overview, product portfolios, recent developments, and strengths and weaknesses. Top Players...

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In this article, we give visualization and different machine learning technics for two weeks and 40 days ahead forecast based on public data. On July 15, 2020, Senegal reopened its airspace doors, while the number of confirmed cases is still increasing. The population no longer respects hygiene measures, social distancing as at the beginning of the contamination. Negligence or tiredness to always wear the masks? We make forecasting on the inflection point and possible ending time.

One possible solution to cellular agriculture's biggest problem -- how to develop a cheap, humane, growth material for cultured meat -- may have come from a conversation in line at a Tim Hortons in Alberta. The husband and wife duo of Matt and Jalene Anderson-Baron were waiting for Timbits and coffee and talking about the technology behind their startup, Future Fields, when Jalene suggested a possible new growth medium. Matt Anderson-Baron had hit a wall in his research, and the pair, which represented two-thirds of the founding triumvirate of Future Fields, were out for a snack. Along with co-founder Lejjy Gafour, the three friends had set out to launch a startup from Canada that could do something about the world's reliance on animals for protein. They recognized that the attendant problems associated with animal farming were unsustainable at a scale needed to meet global demand for meat.