In this paper, we propose an algorithm that can identify influential instances without using any domain knowledge.

Reviewer 2 raises some important points and I agree with some of the concerns about the clarity of the paper and the contrast to existing other work. The authors' reply contains some additional experiments, which is great. And I appreciate the clarification of my misunderstanding of the storage of parameters, not gradients. Data cleansing is an important step in machine learning systems and principled approaches for automating this step is a valuable contribution. The setting considered by the authors covers a substantial fraction of ML algorithms used today.

Data cleansing is a typical approach used to improve the accuracy of machine learning models, which, however, requires extensive domain knowledge to identify the influential instances that affect the models. In this paper, we propose an algorithm that can identify influential instances without using any domain knowledge. The proposed algorithm automatically cleans the data, which does not require any of the users' knowledge. Hence, even non-experts can improve the models. The existing methods require the loss function to be convex and an optimal model to be obtained, which is not always the case in modern machine learning.

Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.

Wouldn't it be nice to have a sneak-peek into the future of your business to make informed decisions and eliminate guesswork? With the help of predictive modeling, this is possible. Predictive modeling enables businesses to reliably forecast trends and behaviors using past and current data. But to ensure the effectiveness of a predictive model, the data must meet exceptionally high standards. It is for this reason; the data scientists spend 80% of their time preparing and organizing data.

Much like we have Chemical Engineering and Electrical Engineering and Mechanical Engineering, it is time to formalize of field of Data Engineering. This is a special two-part series on trends and requirements leading to the formalization of the Field of Data Engineering. "Data is the new oil…in much the same way that oil fueled economic growth in the 20th century, data will fuel economic growth in the 21st century." To further raise the credibility of data as the economic fuel for the next century, "The Economist" Special Report on the Data Economy asks "Are data more like oil or sunlight?" Still, it is hard to put a definitive value on data. If data is to be the fuel for economic growth in the 21st century, don't we need to find a way to accurately determine what data is worth?

Data cleansing is a typical approach used to improve the accuracy of machine learning models, which, however, requires extensive domain knowledge to identify the influential instances that affect the models. In this paper, we propose an algorithm that can identify influential instances without using any domain knowledge. The proposed algorithm automatically cleans the data, which does not require any of the users' knowledge. Hence, even non-experts can improve the models. The existing methods require the loss function to be convex and an optimal model to be obtained, which is not always the case in modern machine learning.

The single most important thing that a medical team does before a surgery is cleaning the tools. Fully functional and clean instruments are critical to successful outcomes. Without adequate cleaning, patient safety is at risk. The same is true for your digital operations. As organizations prepare to forge into a future with artificial intelligence (AI), they must also "clean" their tools--that is, their processes and data.

So, does AI really require a year of data preparation? The answer is profound: No. And clients typically see results for their first use case within 3-4 months. All business applications, including AI, used to be built based on the data model that governed a specific problem. Such a data model typically resides in a data warehouse that can be located either on-premise or in the cloud.