Generative models, which can include GANs, often exhibit the ability to encode significant visual concepts linearly within their latent space. This feature allows these models to perform controlled image edits, making alterations to facial attributes such as age and gender. However, in the case of multi-step generative models, like diffusion models, identifying this linear latent…
MIT and the University of Washington researchers have developed a model to understand and predict human behavior by considering computational constraints that limit decision-making abilities for both humans and machines. One of the defining points about the model is its ability to derive an agent's computational constraints or "inference budget" based on a few previous…
Researchers from MIT and the University of Washington have developed a model to predict human behavior that accounts for computational constraints. These constraints can impact the problem-solving abilities of both human and artificial intelligences (AI). The model can infer an “inference budget”, a computation of the possible constraints on an agent’s problem-solving methods, by observing…
Researchers from MIT and the MIT-IBM Watson AI Lab have developed a machine-learning accelerator that combats cyber threats, thereby protecting sensitive user data. While certain health or fitness apps employ these vast machine-learning models to provide insights, they can sometimes prove to be sluggish and consume a large amount of energy due to the shifting…
With the arrival of spring in the Northern Hemisphere, tornado season begins. Despite their appearance being easily recognizable, detecting tornadoes with radar presents a challenge, making it difficult to pinpoint when and why these destructive phenomena occur. A breakthrough may be on the horizon with the TorNet dataset, recently released as open source by researchers…
Apple's researchers have risen to the challenge of developing AI language models that prioritize efficiency, accuracy, ethical considerations, and user privacy. Two such models have been developed: one with three billion parameters that is optimized for on-device use, and a larger server-based model made for Apple's Private Cloud Compute. These models take us closer to…
This article presents research by scientists from the University of Helsinki, who have developed advanced algorithms for detecting dense subgraphs in temporal networks. Their work addresses two key challenges in temporal network analysis: identifying Jaccard Constrained Dense Subgraphs (JCDS) and discovering Jaccard Weighted Dense Subgraphs (JWDS).
The goal of their research was to maximize total…
Direct Preference Optimization (DPO) is a sophisticated training technique used for refining large language models (LLMs). It does not depend on a single gold reference like traditional supervised fine-tuning, instead, it trains models to identify quality differences among multiple outputs. Adding reinforcement learning approaches, DPO can learn from feedback, making it a useful technique for…