Customer-obsessed science
Research areas
-
July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically.
-
-
July 9, 202610 min read
-
Featured news
-
ICASSP 20212021We present a new method and a large-scale database to detect audio-video synchronization (A/V sync) errors in tennis videos. A deep network is trained to detect the visual signature of the tennis ball being hit by the racquet in the video stream. Another deep network is trained to detect the auditory signature of the same event in the audio stream. During evaluation, the audio stream is searched by the
-
NAACL 20212021In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities. Previous works that incorporate context for SLU have mostly focused on domains where context is limited to a few minutes. However, there are domains that have related context that could span up to hours and days. In this paper, we propose temporal
-
CVPR 20212021Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people’s lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models. In this work, we revisit existing approaches for cross-modal recipe retrieval and propose a simplified end-to-end model based on well established and high performing
-
The Web Conference 20212021Knowledge Graphs (KGs) are ubiquitous structures for information storage in several real-world applications such as web search, ecommerce, social networks, and biology. Querying KGs remains a foundational and challenging problem due to their size and complexity. Promising approaches to tackle this problem include embedding the KG units (e.g., entities and relations) in a Euclidean space such that the query
-
ICASSP 20212021As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains. We present a domain-aware rescoring framework suitable for achieving domain adaptation during second-pass rescoring in production settings. In our framework, we fine-tune a domain-general neural language model on several domains, and use an LSTM-based
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all