We introduce AudioPaLM, a large language model for speech understanding ...
Conformer models maintain a large number of internal states, the vast
ma...
Assessing the aesthetics of an image is challenging, as it is influenced...
The field of vision and language has witnessed a proliferation of pre-tr...
We introduce Noise2Music, where a series of diffusion models is trained ...
Monitoring of colonial waterbird nesting islands is essential to trackin...
We study the effect of normalization on the layers of deep neural networ...
We present the Pathways Autoregressive Text-to-Image (Parti) model, whic...
Exploring large-scale pretrained foundation models is of significant int...
We present a simple and effective self-supervised learning approach for
...
In learning action recognition, models are typically pre-trained on obje...
Pretraining language models with next-token prediction on massive text
c...
We summarize the results of a host of efforts using giant automatic spee...
With recent progress in joint modeling of visual and textual representat...
End-to-end (E2E) models have shown to outperform state-of-the-art
conven...
We consider shallow (single hidden layer) neural networks and characteri...
End-to-end (E2E) automatic speech recognition (ASR) models, by now, have...
Streaming automatic speech recognition (ASR) aims to emit each hypothesi...
Streaming automatic speech recognition (ASR) aims to emit each hypothesi...
The generative adversarial network (GAN) framework has emerged as a powe...
After learning a new object category from image-level annotations (with ...
After learning a new object category from image-level annotations (with ...
Inspired by the robustness and efficiency of sparse representation in sp...
In automatic speech recognition (ASR), model pruning is a widely adopted...
Recently Transformer and Convolution neural network (CNN) based models h...
Convolutional neural networks (CNN) have shown promising results for
end...
Self-similarity refers to the image prior widely used in image restorati...
Neural architecture search (NAS) has shown promising results discovering...
While scale-invariant modeling has substantially boosted the performance...
Generic Image recognition is a fundamental and fairly important visual
p...
We study how to set channel numbers in a neural network to achieve bette...
Slimmable networks are a family of neural networks that can instantly ad...
Density estimation plays a fundamental role in many areas of statistics ...
We present a simple and general method to train a single neural network
...
In this report we demonstrate that with same parameters and computationa...
With the pervasive network based services in smart homes, traditional ne...
We present a novel deep learning based image inpainting system to comple...
Recent deep learning based approaches have shown promising results on im...
In present object detection systems, the deep convolutional neural netwo...