Search Results for author:
Found 6 papers, 3 papers with code
Learning Disentangled Prompts for Compositional Image Synthesis
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 better understand the compositional image synthesis.
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
After re-examining the design choices for both the macro and micro-architecture of Conformer, we propose Squeezeformer which consistently outperforms the state-of-the-art ASR models under the same training schemes.
Ranked #30 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
SqueezeNAS: Fast neural architecture search for faster semantic segmentation
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 computing platform.
Ranked #61 on
Semantic Segmentation
on Cityscapes val
(using extra training data)
Meta Architecture Search
Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks.
Boosting the Actor with Dual Critic
This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC.
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades.
