137 citations · 248 across the 8 of their papers we have counts for
8 papers
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?
David Mayo, Christopher Wang, Asa Harbin +4
When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powe…
Learning Disentangled Prompts for Compositional Image Synthesis
Kihyuk Sohn, Albert Shaw, Yuan Hao +5
We study domain-adaptive image synthesis, the problem of teaching pretrained image generative models a new style or concept from as few as one image to synthesize novel images, to…
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
Sehoon Kim, Amir Gholami, Albert Shaw +5
The recently proposed Conformer model has become the de facto backbone model for various downstream speech tasks based on its hybrid attention-convolution architecture that capture…
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
Forrest N. Iandola, Albert E. Shaw, Ravi Krishna +1
Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, nat…
SqueezeNAS: Fast neural architecture search for faster semantic segmentation
Albert Shaw, Daniel Hunter, Forrest Iandola +1
For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target co…
Meta Architecture Search
Albert Shaw, Wei Wei, Weiyang Liu +2
Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks. Unfortunately, the computational cost can make it difficul…
Boosting the Actor with Dual Critic
Bo Dai, Albert Shaw, Niao He +2
This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian dual form of the Bellman optimalit…
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li +5
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades. The…