119 citations · 200 across the 10 of their papers we have counts for
10 papers
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
Tianhong Li, Sangnie Bhardwaj, Yonglong Tian +6
Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalization capabilities. However, automatically…
StyleDrop: Text-to-Image Generation in Any Style
Kihyuk Sohn, Nataniel Ruiz, Kimin Lee +11
Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distributi…
SPADE: Self-supervised Pretraining for Acoustic DisEntanglement
John Harvill, Jarred Barber, Arun Nair +1
Self-supervised representation learning approaches have grown in popularity due to the ability to train models on large amounts of unlabeled data and have demonstrated success in d…
Muse: Text-To-Image Generation via Masked Generative Transformers
Huiwen Chang, Han Zhang, Jarred Barber +9
We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregres…
Challenges and Opportunities in Multi-device Speech Processing
Gregory Ciccarelli, Jarred Barber, Arun Nair +2
We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidev…
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
Peyman Bateni, Jarred Barber, Raghav Goyal +4
Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learning aims to alleviate this issue by learning effectively from few labelled examp…
End-to-end Alexa Device Arbitration
Jarred Barber, Yifeng Fan, Tao Zhang
We introduce a variant of the speaker localization problem, which we call device arbitration. In the device arbitration problem, a user utters a keyword that is detected by multipl…
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels
Jarred Barber
Gaussian processes are powerful models for probabilistic machine learning, but are limited in application by their inference complexity. We propose a method for deriving p…
Enhancing Few-Shot Image Classification with Unlabelled Examples
Peyman Bateni, Jarred Barber, Jan-Willem van de Meent +1
We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-…