The prototype selection problem aims to learn a sparse distribution of a source set such that it best matches a different target set. The applications of this problem include target subset selection, data summarization, and clustering, to name a few. In this talk, we present efficient algorithms for the prototype selection problem. Using the optimal transport theory, the proposed optimization formulation is an instance of submodular maximization, and therefore, we propose a greedy algorithm with simple updates. We further make use of the bandit setup to reduce the computations. The presentation is based on the papers [1, 2].
Design thinking is a problem-solving approach that is used to develop innovative and user-centered solutions. It is a process that helps individuals and teams to understand the needs and perspectives of users, and to develop solutions that meet those needs in a creative and effective way.
Dr. Arjun Jain will give an in-depth overview of the inner workings of autonomous agents, including their history of development, key modules that enable autonomy, and technologies that empower them, focusing on perception, localization, prediction, planning and control. He will also discuss current limitations of autonomous systems and propose solutions to overcome them, and also showcase research and advancements in true autonomy for GPS-denied environments at UAVIO labs.
In the context of global climate change that threatens human civilization in multiple ways, climate sciences have become a crucial topic of research. The parallel progress in the field of Machine Learning and the availability of huge volumes of climate-related data from multiple sources has sparked off interest in using data-driven approaches to answer crucial questions in climate science. Such questions involve short-term weather forecasts, long-term climate simulations and discovery of new laws and relations. Recent research has shown that it is possible to use models and algorithms related to Machine Learning and Deep Learning to answer more such questions successfully than earlier. In this talk, we will discuss several such applications of Machine Learning and Data Science in the domain of Climate Sciences.
Introduction to GPU ,Docker and Containers ,Data Preprocessing using RAPIDS ,Model Training and Optimizations ,Introduction to TensorRT ,Introduction to Triton Inference Server
Speech and language technologies have gained widespread acceptance worldwide in recent years. With new technology users rapidly growing in India, it is an important challenge to build inclusive technologies that cater to these new users. There are notable challenges towards achieving this goal largely owing to India's rich linguistic diversity. Multilinguality among Indian users contributes to large variations in speech accents that adversely affect speech recognition performance, even on resource-rich languages like English. User in multilingual societies like India also make frequent user of code-switching, i.e. mixing multiple languages while communicating, that further complicates user-machine interactions. In this talk, we will look at some of our recent work that aims to address these important challenges and outline a vision for widespread adoption of speech and language technologies in India.
Over the last decade, there has been an explosive increase in AI/ML applications—all thanks to powerful computational resources like GPUs and TPUs. Recent trends suggest that computational requirements of AI, ML, and Graphics applications are briskly outpacing the supply. Innovations are necessary for the semiconductor industry to keep up the pace. All such applications are data intensive; hence, conventional technology of moving the data from the memory to the core limits performance. However, researchers have proven we can fill this gap by introducing near-memory and in-memory computation. This talk will cover the basics of such computing resources and opportunities to revive, research, and develop such systems.