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Exploring Optimizations in Multi-Task Recommendation via Manipulating Auxiliary Gradients
Auxiliary Task Learning has proven to be an effective way to transfer knowledge between tasks. This is the case in many personalized recommendation scenarios, where many auxiliary tasks can boost the performance of the ...
Synthesizing Novel Views with Diffusion Models
Diffusion models have become the state-of-the-art generative model in a multitude of generative tasks such as audio and image synthesis. Until recently, there has not been much success with the specific image-to-image task ...
Applications of Neural Architecture Search to Deep Reinforcement Learning Methods
This research aims to investigate the impact of various Neural Architectures (NAs) on the performance of machine learning models in the context of the deterministic game of Othello, with the goal of providing insights into ...
Study of Tissue Heterogeneity and Classification using AI Techniques
(2021-05-03)
The idea behind our project is to design an algorithm that utilizes artificial intelligence to detect tissue heterogeneity in patients without the need to carry out an invasive biopsy. We aim to make the cancer prognosis ...
Machine Learning for Raga Classification in Indian Classical Music
(2019-02-11)
Indian Classical music consists of “Ragas”. Ragas are combinations of notes in a particular order. There are 72 combinations of these notes that form the 72 parent Ragas. The names of the Ragas are written around the circle ...
Streamlining TNS Data Collection for ML-Based RTL QoR Prediction
Chip designs must meet several requirements before they are ready for fabrication. One of these requirements is achieving convergence on timing (frequency). Meeting this requirement is a time-consuming task for chip designers ...