Customer-obsessed science
Research areas
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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically.
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July 9, 202610 min read
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Featured news
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NAACL 20222022Seq2seq language generation models that are trained offline with multiple domains in a sequential fashion often suffer from catastrophic forgetting. Lifelong learning has been proposed to handle this problem. However, existing work such as experience replay or elastic weighted consolidation requires incremental memory space. In this work, we propose an innovative framework, RMR_DSE, that leverages a recall
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NAACL 20222022We present a method to control the emotional prosody of Text to Speech (TTS) systems by using phoneme-level intermediate features (pitch, energy, and duration) as levers. As a key idea, we propose Differential Scaling (DS) to disentangle features relating to affective prosody from those arising due to acoustics conditions and speaker identity. With thorough experimental studies, we show that the proposed
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HPDC 20222022In data-intensive workloads, data placement and memory management are inherently difficult: the programmer and the operating system have to choose between (combinations of) DRAM and storage, replacement policies, as well as paging sizes. Efficient memory management is based on fine-grained data access patterns driving placement decisions. Current solutions in this space cannot be applied to general workloads
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Annual Review of Chemical and Biomolecular Engineering2022There is an urgent need for new technologies to enable circularity for synthetic polymers, spurred by the accumulation of waste plastics in landfills and the environment and the contributions of plastics manufacturing to climate change. Chemical recycling is a promising means to convert waste plastics into molecular intermediates that can be remanufactured into new products. Given the growing interest in
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IJCNN 20222022Graph-based collaborative filtering for recommendation has attracted great attention recently, due to its effectiveness of capturing high-order proximity among users and items. To further improve its model robustness and alleviate label-sparsity issue, contrastive learning has been introduced to polish user and item representation by contrasting different views of user/item nodes, learning necessary and
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