How OpenAI is approaching 2024 worldwide elections
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Pinterest Researchers Present an Effective Scalable Algorithm to Improve Diffusion Models Using Reinforcement Learning (RL)
Diffusion models are a set of generative models that work by adding noise to the training data and then learn to recover the same by reversing the noising process. This process allows these models to achieve state-of-the-art image quality, making them one of the most significant developments in Machine Learning (ML) in the past few…
Enhancing the Accuracy of Large Language Models with Corrective Retrieval Augmented Generation (CRAG)
In natural language processing, the quest for precision in language models has led to innovative approaches that mitigate the inherent inaccuracies these models may present. A significant challenge is the models’ tendency to produce “hallucinations” or factual errors due to their reliance on internal knowledge bases. This issue has been particularly pronounced in large language…
Google DeepMind Researchers Propose a Novel AI Method Called Sparse Fine-grained Contrastive Alignment (SPARC) for Fine-Grained Vision-Language Pretraining
Contrastive pre-training using large, noisy image-text datasets has become popular for building general vision representations. These models align global image and text features in a shared space through similar and dissimilar pairs, excelling in tasks like image classification and retrieval. However, they need help with fine-grained tasks such as localization and spatial relationships. Recent efforts…
Apple Researchers Introduce LiDAR: A Metric for Assessing Quality of Representations in Joint Embedding JE Architectures
Self-supervised learning (SSL) has proven to be an indispensable technique in AI, particularly in pretraining representations on vast, unlabeled datasets. This significantly reduces the dependency on labeled data, often a major bottleneck in machine learning. Despite the merits, a major challenge in SSL, particularly in Joint Embedding (JE) architectures, is evaluating the quality of learned…
