Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular strategy for leveraging additional data to improve the downstream performance, but finding the most relevant data to transfer from can be challenging. Neural Data Server (NDS), a search engine that recommends relevant data for a given downstream task, has been previously proposed to address this problem (Yan et al., 2020). NDS uses a mixture of experts trained on data sources to estimate similarity between each source and the downstream task. Thus, the computational cost to each user grows with the number of sources and requires an expensive training step for each data provider.To address these issues, we propose Scalable Neural Data Server (SNDS), a large-scale search engine that can theoretically index thousands of datasets to serve relevant ML data to end users. SNDS trains the mixture of experts on intermediary datasets during initialization, and represents both data sources and downstream tasks by their proximity to the intermediary datasets. As such, computational cost incurred by users of SNDS remains fixed as new datasets are added to the server, without pre-training for the data providers.We validate SNDS on a plethora of real world tasks and find that data recommended by SNDS improves downstream task performance over baselines. We also demonstrate the scalability of our system by demonstrating its ability to select relevant data for transfer outside of the natural image setting.

Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular strategy for leveraging additional data to improve the downstream performance, but finding the most relevant data to transfer from can be challenging. Neural Data Server (NDS), a search engine that recommends relevant data for a given downstream task, has been previously proposed to address this problem (Yan et al., 2020). NDS uses a mixture of experts trained on data sources to estimate similarity between each source and the downstream task. Thus, the computational cost to each user grows with the number of sources and requires an expensive training step for each data provider.To address these issues, we propose Scalable Neural Data Server (SNDS), a large-scale search engine that can theoretically index thousands of datasets to serve relevant ML data to end users.

Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.g. FastText) is widely used for efficient online inference. However, the representation ability of the FastText model is insufficient, resulting in poor classification performance, especially on some low-frequency queries and tailed categories. Using a deeper and more complex model (e.g. BERT) is an effective solution, but it will cause a higher online inference latency and more expensive computing costs. Thus, how to juggle both inference efficiency and classification performance is obviously of great practical importance. To overcome this challenge, in this paper, we propose knowledge condensation (KC), a simple yet effective knowledge distillation framework to boost the classification performance of the online FastText model under strict low latency constraints. Specifically, we propose to train an offline BERT model to retrieve more potentially relevant data. Benefiting from its powerful semantic representation, more relevant labels not exposed in the historical data will be added into the training set for better FastText model training. Moreover, a novel distribution-diverse multi-expert learning strategy is proposed to further improve the mining ability of relevant data. By training multiple BERT models from different data distributions, it can respectively perform better at high, middle, and low-frequency search queries. The model ensemble from multi-distribution makes its retrieval ability more powerful. We have deployed two versions of this framework in JD search, and both offline experiments and online A/B testing from multiple datasets have validated the effectiveness of the proposed approach.

This dissertation presents a methodology for recording speed climbing training sessions with multiple cameras and annotating the videos with relevant data, including body position, hand and foot placement, and timing. The annotated data is then analyzed using deep learning techniques to create a standard dataset of speed climbing training videos. The results demonstrate the potential of the new dataset for improving speed climbing training and research, including identifying areas for improvement, creating personalized training plans, and analyzing the effects of different training methods.The findings will also be applied to the training process of the Jiangxi climbing team through further empirical research to test the findings and further explore the feasibility of this study.

Maybe it stems from my childhood fascination with crystal balls and the Magic 8 Ball, but I have always been interested in predictions of the future. Machine learning has done a great job with predictions based on past data about events and behaviors, but it hasn't generally been applied to making sense of the broader world. But that is just what Signal AI is doing with machine learning. They produce "external intelligence" intended as an aid to decision-making. It could also be called "environmental sensing."

Classical sequential models employed in time-series prediction rely on learning the mappings from the past to the future instances by way of a hidden state. The Hidden states characterise the historical information and encode the required temporal dependencies. However, most existing sequential models operate within finite-dimensional Euclidean spaces which offer limited functionality when employed in modelling physics relevant data. Alternatively recent work with neural operator learning within the Fourier space has shown efficient strategies for parameterising Partial Differential Equations (PDE). In this work, we propose a novel sequential model, built to handle Physics relevant data by way of amalgamating the conventional RNN architecture with that of the Fourier Neural Operators (FNO). The Fourier-RNN allows for learning the mappings from the input to the output as well as to the hidden state within the Fourier space associated with the temporal data. While the Fourier-RNN performs identical to the FNO when handling PDE data, it outperforms the FNO and the conventional RNN when deployed in modelling noisy, non-Markovian data.

You want success over the coming months and years? The number-one way to get there is through people -- building businesses through their creativity, passion, and full participation in decision-making. But right behind empowered people is the number-two vital ingredient for success: data. Data that can reveal to you what your customers want, how your business is running, and what's around the corner. Now, we have the key that unlocks the patterns that have long been hidden away in databases and applications.

Digital transformation accelerated during the pandemic, as companies of all sizes and in a range of industries invested in advanced, collaborative technologies in a bid to adapt to the new normal. As a result, many more organizations have now established a solid foundation on which to build a digital future. At the heart of this digital transformation is data – every digital interaction generates vast quantities of it. And it can help businesses identify new opportunities, make better decisions, and improve operational efficiency. However, data can only deliver business benefits if it can be discovered, analyzed, and used to generate actionable insights.

The variety of tasks that it is solving now and will continue to solve in the next 5 or 10 years will also increase exponentially. As such systems evolve, they naturally become more intuitive, normal and easy to use for the public. Once considered highly mechanical machines with open roof tops and bicycle wheels, resembling more to a horse-cart (than a car as we think in modern terms), which only a particular section of people could afford, not just due to the cost but also the fuel availabilities, now has become highly cheap, comfortable, fuel efficient and safer. Electric cars are even replacing the brilliantly engineered mechanical components into more simpler ones that require minimum maintenance and are much safer for the environment. And let's not get started on autonomous cars.

The volume of data that IT systems generate nowadays is overwhelming, and without intelligent monitoring and analysis tools, it can result in missed opportunities, alerts, and expensive downtime. However, with the advent of Machine Learning and Big Data, a new category of IT operations tool has emerged called AIOps. AIOps can be defined as the practical application of Artificial Intelligence to augment, support, and automate IT processes. It leverages Machine Learning, Natural Language Processing, and Analytics to monitor and analyze complex real-time data, helping teams quickly detect and resolve issues. With AIOps, Ops teams can tame the vast complexity and volume of data generated by their modern IT environments to prevent outages, maintain uptime and achieve continuous service assurance. AIOps enables organizations to operate at speed demanded by modern businesses and deliver a great user experience.