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Found 9 papers, 2 papers with code
Text-Aware Diffusion for Policy Learning
Training an agent to achieve particular goals or perform desired behaviors is often accomplished through reinforcement learning, especially in the absence of expert demonstrations.
Self-Correcting Self-Consuming Loops for Generative Model Training
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.
Towards A Unified Neural Architecture for Visual Recognition and Reasoning
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.
Does Visual Pretraining Help End-to-End Reasoning?
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.
Understanding Diffusion Models: A Unified Perspective
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.
Intriguing Properties of Contrastive Losses
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.
Data Augmentation via Structured Adversarial Perturbations
The advantage of adversarial augmentation is that it replaces sampling with the use of a single, calculated perturbation that maximally increases the loss.
Scalable Recommender Systemsthrough Recursive Evidence Chains
A popular matrix completion algorithm is matrix factorization, where ratings are predicted from combining learned user and item parameter vectors.
Scalable Recommender Systems through Recursive Evidence Chains
Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors.
