1 code implementation • 11 Feb 2024 • Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun
As synthetic data becomes higher quality and proliferates on the internet, machine learning models are increasingly trained on a mix of human- and machine-generated data.
no code implementations • 10 Nov 2023 • Calvin Luo, Boqing Gong, Ting Chen, Chen Sun
Motivated by the recent success of multi-task transformers for visual recognition and language understanding, we propose a unified neural architecture for visual recognition and reasoning with a generic interface (e. g., tokens) for both.
no code implementations • NeurIPS 2023 • Chen Sun, Calvin Luo, Xingyi Zhou, Anurag Arnab, Cordelia Schmid
A positive result would refute the common belief that explicit visual abstraction (e. g. object detection) is essential for compositional generalization on visual reasoning, and confirm the feasibility of a neural network "generalist" to solve visual recognition and reasoning tasks.
no code implementations • 25 Aug 2022 • Calvin Luo
Diffusion models have shown incredible capabilities as generative models; indeed, they power the current state-of-the-art models on text-conditioned image generation such as Imagen and DALL-E 2.
3 code implementations • NeurIPS 2021 • Ting Chen, Calvin Luo, Lala Li
We construct datasets with explicit and controllable competing features, and show that, for contrastive learning, a few bits of easy-to-learn shared features can suppress, and even fully prevent, the learning of other sets of competing features.
no code implementations • 5 Nov 2020 • Calvin Luo, Hossein Mobahi, Samy Bengio
The advantage of adversarial augmentation is that it replaces sampling with the use of a single, calculated perturbation that maximally increases the loss.
no code implementations • 20 Oct 2018 • Elias Tragas, Calvin Luo, Maxime Yvez, Kevin Luk, David Duvenaud
A popular matrix completion algorithm is matrix factorization, where ratings are predicted from combining learned user and item parameter vectors.
no code implementations • 5 Jul 2018 • Elias Tragas, Calvin Luo, Maxime Gazeau, Kevin Luk, David Duvenaud
Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors.