2.2k citations · 7.1k across the 72 of their papers we have counts for
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Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases
Shrimai Prabhumoye, Rafal Kocielnik, Mohammad Shoeybi +2
Detecting social bias in text is challenging due to nuance, subjectivity, and difficulty in obtaining good quality labeled datasets at scale, especially given the evolving nature o…
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
John Guibas, Morteza Mardani, Zongyi Li +3
Vision transformers have delivered tremendous success in representation learning. This is primarily due to effective token mixing through self attention. However, this scales quadr…
Guiding Global Placement With Reinforcement Learning
Robert Kirby, Kolby Nottingham, Rajarshi Roy +2
Recent advances in GPU accelerated global and detail placement have reduced the time to solution by an order of magnitude. This advancement allows us to leverage data driven optimi…
One TTS Alignment To Rule Them All
Rohan Badlani, Adrian Łancucki, Kevin J. Shih +3
Speech-to-text alignment is a critical component of neural textto-speech (TTS) models. Autoregressive TTS models typically use an attention mechanism to learn these alignments on-l…
Long-Short Transformer: Efficient Transformers for Language and Vision
Chen Zhu, Wei Ping, Chaowei Xiao +4
Transformers have achieved success in both language and vision domains. However, it is prohibitively expensive to scale them to long sequences such as long documents or high-resolu…
View Generalization for Single Image Textured 3D Models
Anand Bhattad, Aysegul Dundar, Guilin Liu +2
Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view gene…