Education
A multimodal developmental benchmark for language learning
How (dis)similar are the learning trajectories of vision-language models and children? Recent modeling work has attempted to understand the gap between models'
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
Transformers can efficiently learn in-context from example demonstrations.
Many-Shot In-Context Learning Rishabh Agarwal, A vi Singh
LLMs excel at few-shot in-context learning (ICL) - learning from a few input-output examples ("shots") provided in context at inference, without any weight updates.
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative of the target domain.
Set-based Neural Network Encoding Without Weight Tying Bruno Andreis
Furthermore, our S et-based N eural network E ncoder (SNE) takes into consideration the hierarchical computational structure of neural networks.
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching
Diffusion models have demonstrated great success in the field of text-to-image generation. However, alleviating the misalignment between the text prompts and images is still challenging. We break down the problem into two causes: concept ignorance and concept mismapping.
