4 accepted papers
Despite recent efforts in understanding the compression impact on Large Language Models (LLMs) in terms of their downstream task performance and trustworthiness on relatively simpler uni-modal benchmarks (e.g. question answering, common sense reasoning), their detailed study on multi-modal Large Vis…
Large pre-trained language models have recently gained significant traction due to their improved performance on various down-stream tasks like text classification and question answering, requiring only few epochs of fine-tuning. However, their large model sizes often prohibit their applications on…
Existing deep neural networks (DNNs) that achieve state-of-the-art (SOTA) performance on both clean and adversarially-perturbed images rely on either activation or weight conditioned convolution operations. However, such conditional learning costs additional multiply-accumulate (MAC) or addition ope…
Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these dee…