Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more merger neural network blocks that each generate block output sequence that has fewer elements than the block input sequence that is processed by the merger neural network block.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating predictions about images. One of the systems includes a neural network comprising a sequence of one or more network blocks that are each configured to perform operations comprising: obtaining a block input that represents an intermediate representation of an input image; determining a plurality of patches of the block input or of an updated representation of the block input, wherein each patch comprises a different subset of elements of the block input or of the updated representation of the block input; assigning each patch to one or more respective expert modules of a plurality of expert modules of the network block; for each patch of the plurality of patches, processing the patch using the corresponding expert modules to generate respective module outputs; and generating a block output by combining the module outputs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for fine-tuning a computer vision neural network. In particular, the neural network is fine-tuned using task rewards and through reinforcement learning.

Abstract: Generally, the present disclosure is directed to systems and methods that provide a simple, scalable, yet effective strategy to perform transfer learning with a mixture of experts (MoE). In particular, the transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. In contrast, example systems and methods of the present disclosure use expert representations for transfer with a simple, yet effective, strategy.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks using transfer learning.

Abstract: Provided are methods for switching active speakers during a video conferencing session. An image of an active speaker in a video conference is provided for presentation in a main display area of a display screen, where the active speaker is one of a plurality of users participating in the video conference over a network. When, a new active speaker out of the users participating in the video conference is detected, resolutions of available video streams received for each of the users are determined. In response to determining that the resolution of the video stream received for the new active speaker is below a threshold resolution, the image of the active speaker continues to be provided for presentation in the main display area until a predetermined period of time has elapsed.

Abstract: Provided are methods for switching active speakers during a video conferencing session. An image of an active speaker in a video conference is provided for presentation in a main display area of a display screen, where the active speaker is one of a plurality of users participating in the video conference over a network. When, a new active speaker out of the users participating in the video conference is detected, resolutions of available video streams received for each of the users are determined. In response to determining that the resolution of the video stream received for the new active speaker is below a threshold resolution, the image of the active speaker continues to be provided for presentation in the main display area until a predetermined period of time has elapsed.