First, obviously, the maximum over a set containing a single random variable has the distribution of that single element. Hence, there is no overestimation bias in the single-element case; i.e., Next, we consider the two (deterministic) following possible cases. In the rest of the proof, we shall apply Cantelli's inequality to upper bound Theorem 3.5 now follows from Theorem A.1 after plugging the approximation The scores are obtained via BCTS with a Batch-BFS implementation, as reported in Section 5.1. TS of depths 2,3, and 4. Note that for depth 1, the correction is vacuous since it coincides with the Episodic training cumulative reward of DQN with TS based on 5 seeds. Lastly, we summarize the results for all tested games in Table 2. Ablation study: Propagated value (PV) from the tree nodes: Ablation study for scores of all tested games.

To evaluate the design and skills of a robot or an algorithm for robotics, human-robot interaction user studies need to be performed. Classically, these studies are conducted by human experimenters, requiring considerable effort, and introducing variability and potential human error. In this paper, we investigate the use of robots in support of HRI experiments. Robots can perform repeated tasks accurately, thereby reducing human effort and improving validity through reduction of error and variability between participants. To assess the potential for robot led HRI experiments, we ran an HRI experiment with two participant groups, one led by a human experimenter and another led mostly by a robot experimenter.We show that the replacement of several repetitive experiment tasks through robots is not only possible but beneficial: Trials performed by the robot experimenter had fewer errors and were more fluent. There was no statistically significant difference in participants' perception w.r.t. cognitive load, comfortability, enjoyment, safety, trust and understandability between both groups. To the best of our knowledge, this is the first comparison between robot-led and human-led HRI experiments. It suggests that using robot experimenters can be beneficial and should be considered.

Detecting and discovering new gene interactions based on known gene expressions and gene interaction data presents a significant challenge. Various statistical and deep learning methods have attempted to tackle this challenge by leveraging the topological structure of gene interactions and gene expression patterns to predict novel gene interactions. In contrast, some approaches have focused exclusively on utilizing gene expression profiles. In this context, we introduce GENER, a parallel-layer deep learning network designed exclusively for the identification of gene-gene relationships using gene expression data. We conducted two training experiments and compared the performance of our network with that of existing statistical and deep learning approaches. Notably, our model achieved an average AUROC score of 0.834 on the combined BioGRID&DREAM5 dataset, outperforming competing methods in predicting gene-gene interactions.

Ultra-Wideband (UWB) systems are becoming increasingly popular for indoor localization, where range measurements are obtained by measuring the time-of-flight of radio signals. However, the range measurements typically suffer from a systematic error or bias that must be corrected for high-accuracy localization. In this paper, a ranging protocol is proposed alongside a robust and scalable antenna-delay calibration procedure to accurately and efficiently calibrate antenna delays for many UWB tags. Additionally, the bias and uncertainty of the measurements are modelled as a function of the received-signal power. The full calibration procedure is presented using experimental training data of 3 aerial robots fitted with 2 UWB tags each, and then evaluated on 2 test experiments. A localization problem is then formulated on the experimental test data, and the calibrated measurements and their modelled uncertainty are fed into an extended Kalman filter (EKF). The proposed calibration is shown to yield an average of 46% improvement in localization accuracy. Lastly, the paper is accompanied by an open-source UWB-calibration Python library, which can be found at https://github.com/decargroup/uwb_calibration.

In this blog post, we will be fine tuning the YOLOv7 object detection model on a real-world pothole detection dataset. Since its inception, the YOLO family of object detection models have come a long way. YOLOv7 is the most recent addition to this famous anchor-based single-shot family of object detectors. It comes with a bunch of improvements which includes state-of-the-art accuracy and speed. Benchmarked on the COCO dataset, the YOLOv7 tiny model achieves more than 35% mAP and the YOLOv7 (normal) model achieves more than 51% mAP. It is also equally important that we get good results when fine tuning such a state-of-the-art model. For that reason, we will be fine tuning YOLOv7 on a real-world pothole detection dataset in this blog post.

Almost everything we see around us today comes from factories. However, manufacturing as we see it today is mostly outdated. Manufacturers spend up to 15–20% of their sales revenue due to the cost of poor quality (COPQ) [link]. This includes the cost of detecting and preventing product failures. The later a defect is detected, the more resources have been wasted on the defective part.

Whether you are an algorithm developer in a growing startup company, a data scientist in a university research lab, or a kaggle hobbyist, there may come a point in time when the training resources that you have onsite no longer meet your training demands. In this post we target development teams that are (finally) ready to move their machine learning (ML) workloads to the cloud. We will discuss some of the important decisions that need to made during this big transition. Naturally, any attempt to encompass all of the steps of such an endeavor is doomed to fail. Machine learning projects come in many shapes and forms and as their complexity increases so does the undertaking of making such a significant change as migrating to the cloud. In this post we will highlight what we believe to be some of the most important considerations that are common to most typical deep learning projects.

Whether you are an algorithm developer in a growing startup company, a data scientist in a university research lab, or a kaggle hobbyist, there may come a point in time when the training resources that you have onsite no longer meet your training demands. In this post we target development teams that are (finally) ready to move their machine learning (ML) workloads to the cloud. We will discuss some of the important decisions that need to made during this big transition. Naturally, any attempt to encompass all of the steps of such an endeavor is doomed to fail. Machine learning projects come in many shapes and forms and as their complexity increases so does the undertaking of making such a significant change as migrating to the cloud. In this post we will highlight what we believe to be some of the most important considerations that are common to most typical deep learning projects.

Driving etiquette takes a different flavor for each locality as drivers not only comply with rules/laws but also abide by local unspoken convention. When to have the turn signal (indicator) on/off is one such etiquette which does not have a definitive right or wrong answer. Learning this behavior from the abundance of data generated from various sensor modalities integrated in the vehicle is a suitable candidate for deep learning. But what makes it a prime candidate for Federated Learning are privacy concerns and bandwidth limitations for any data aggregation. This paper presents a long short-term memory (LSTM) based Turn Signal Prediction (on or off) model using vehicle control area network (CAN) signal data. The model is trained using two approaches, one by centrally aggregating the data and the other in a federated manner. Centrally trained models and federated models are compared under similar hyperparameter settings. This research demonstrates the efficacy of federated learning, paving the way for in-vehicle learning of driving etiquette.

AI-enabled Dynamics 365 Customer Insights helps unifying data from multiple sources within an organization and generates a single, end-to-end view of the customer. This 360-degree customer view can be used to discover insights to optimize customer engagement and drive personalized customer experiences. This unified data is an ideal source to build machine learning (ML) models that can generate additional business insights. Customer Insights provides seamless integration with Azure ML (AML) to bring your own custom models to work on this integrated data. In this blog, we will share a step-by-step guide on how to do that.