Data-driven techniques, such as imitation and offline reinforcement learning (RL), that convert offline datasets into policies are seen as solutions to control problems across many fields. However, recent research has suggested that merely increasing expert data and finetuning imitation learning can often surpass offline RL, even if RL has access to abundant data. This finding…
Researchers from New York University, Genentech, and CIFAR are pioneering a new approach to multi-modal learning in an attempt to improve its efficacy. Multi-modal learning involves using data from various sources to inform a target label, placing boundaries between the sources to allow for differentiation. This type of learning is commonly used in fields like…
Researchers from New York University, Genentech, and CIFAR have proposed a new paradigm to address inconsistencies in supervised multi-modal learning referred to as Inter & Intra-Modality Modeling (I2M2). Multi-modal learning is a critical facet of machine learning, used in autonomous vehicles, healthcare, and robotics, among other fields, where data from different modalities is mapped to…
Computer vision, a field focusing on enabling devices to interpret and understand visual information from the world, faces a significant challenge: aligning vision models with human aesthetic preferences. Even modern vision models trained on large datasets sometimes fail to produce visually appealing results that align with user expectations for aesthetics, style, and cultural context. In…
The field of radiology has seen a transformative impact with the advent of generative vision-language models (VLMs), automating medical image interpretation and report generation. This innovative tech has shown potential in reducing radiologists’ workload and improving diagnostic accuracy. However, a challenge to this technology is its propensity to produce hallucinated content — text that is…
Machine learning (ML) is transforming the healthcare industry by enhancing the evaluation of treatments through the prediction of treatment impacts on patient outcomes. This methodology, known as causal ML, uses data from various sources including randomized controlled trials, clinical registries, and electronic health records to measure treatment effects. By providing personalized outcome predictions under different…