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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The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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ICASSP 20212021Prosody is an integral part of communication, but remains an open problem in state-of-the-art speech synthesis. There are two major issues faced when modelling prosody: (1) prosody varies at a slower rate compared with other content in the acoustic signal (e.g. segmental information and background noise); (2) determining appropriate prosody without sufficient context is an ill-posed problem. In this paper
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ICASSP 20212021In this paper, we introduce Kathaka, a model trained with a novel two-stage training process for neural speech synthesis with contextually appropriate prosody. In Stage I, we learn a prosodic distribution at the sentence level from melspectrograms available during training. In Stage II, we propose a novel method to sample from this learnt prosodic distribution using the contextual information available
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ACL-IJCNLP 2021 Workshop on e-Commerce and NLP (ECNLP)2021We improve customer experience and gain their trust when their issues are resolved rapidly with less friction. Existing work has focused on reducing the overall case resolution time by binning a case into predefined categories and routing it to the desired support engineer. However, the actions taken by the engineer during case analysis and resolution are altogether ignored, even though it forms the bulk
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IEEE Low-Power Computer Vision (LPCV) Challenge2021The low-power computer vision (LPCV) challenge is an annual competition for the best technologies in image classification and object detection measured by both efficiency (execution time and energy consumption) and accuracy (precision/recall). Our Amazon team has won three awards from LPCV challenges: 1st prize for interactive object detection challenge in 2018 and 2019 and 2nd prize for interactive image
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*SEM 20212021Semantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets. To unify different datasets and train a single model for them, we investigate the use of Multi-Task Learning (MTL) architectures. We experiment with five datasets (GEOQUERY, NLMAPS, TOP, OVERNIGHT, AMR). We
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
