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In classic supervised learning, once a model is deployed in an application, it is fixed. No updates will be made to it during the application. This is inappropriate for many dynamic and open environments, where unexpected samples from unseen classes may appear. In such an environment, the model should be able to detect these novel samples from unseen classes and learn them after they are labeled. We call this paradigm Autonomous Learning after Model Deployment (ALMD). The learning here is continuous and involves no human engineers. Labeling in this scenario is performed by human co-workers or other knowledgeable agents, which is similar to what humans do when they encounter an unfamiliar object and ask another person for its name. In ALMD, the detection of novel samples is dynamic and differs from traditional out-of-distribution (OOD) detection in that the set of in-distribution (ID) classes expands as new classes are learned during application, whereas ID classes is fixed in traditional OOD detection. Learning is also different from classic supervised learning because in ALMD, we learn the encountered new classes immediately and incrementally. It is difficult to retrain the model from scratch using all the past data from the ID classes and the novel samples from newly discovered classes, as this would be resource- and time-consuming. Apart from these two challenges, ALMD faces the data scarcity issue because instances of new classes often appear sporadically in real-life applications. To address these issues, we propose a novel method, PLDA, which performs dynamic OOD detection and incremental learning of new classes on the fly. Empirical evaluations will demonstrate the effectiveness of PLDA. 

In this poster, we will show how to leverage NVidia’s Bluef ield Data Processing Unit (DPU) in geospatial systems. Existing work in literature has explored DPUs in the context of machine learning, compression and MPI acceleration. We show our designs on how to integrate DPUs into existing high performance geospatial systems like MPI-GIS. The workflow of a typical spatial computing workload consists of two phases- filter and refine. First we used DPU as a target to offload spatial computations from the host CPU. We show the performance improvements due to offload. Next we used DPU for network I/O processing. In network I/O case, the query data first comes to DPU for filtering and then the query goes to CPU for refinement. DPU-based filter and refine system can be useful in other domains like Physics where an FPGA is used to perform the filter to handle Big Data. We used Bluefield-2 and Bluefield-3 in our experiments. For scalability study, we have used up to 16 DPUs.