Abstract As visual recognition models are developed across diverse applications, we need the ability to reliably deploy our systems in a variety of environments. At the same time, visual models tend to be trained and[…]
Abstract Deep learning has been successfully applied to solve various complex problems. However, deep learning algorithms have also been employed to create deepfakes (e.g. fake audios, images and videos) for misleading information. Deepfakes are increasingly[…]
Abstract For the polar (yes/no) question “Want to get dinner?”, there are many perfectly natural responses in addition to ‘yes’ and ‘no’. Humans can spontaneously interpret responses such as “I’m starving.”, “You are up for[…]
Abstract Can a system learn to recognise (and understand) speech with little or no supervision? In contrast to modern speech recognition systems which are trained on thousands of hours of transcribed speech audio, human infants[…]
Abstract Recent advances in data-driven approaches have demonstrated appealing results in generating natural languages in applications like machine translation and summarization. However, when the generation tasks are open-ended and the content is under-specified, existing techniques[…]
Abstract Mapping textual mentions to entities in a knowledge graph is a key step in using knowledge graphs, called Named Entity Disambiguation (NED). A key challenge in NED is generalizing to rarely seen (tail) entities.[…]
Abstract Although training end-to-end neural speech recognition models requires large volume of transcribed data for new domains, sharing of trained neural modules is not common. In this talk, I’ll present LegoNN, a procedure for building[…]
