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Artificial Intelligence

Weights2Weights: A Subspace within Diffusion Weights acting as a Comprehensible Hidden Space for Tailored Diffusion Models

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…

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In order to improve the efficiency of an AI assistant, begin by simulating the unpredictable actions of individuals.

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…

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For the improvement of AI assistance, initially emulate the unpredictable actions of humans.

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…

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This small microchip can protect user information whilst facilitating effective processing on a mobile phone.

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…

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A data set for artificial intelligence paves fresh avenues for identifying tornadoes.

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…

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This AI document by Apple presents the base language models that fuel Apple’s intelligence features: On-Device AFM and Server AFM.

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…

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Presenting JCDS and JWDS: Innovative Methods for Identifying Dense Subgraph in Time-Based Graphs.

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…

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What is the Significance of the Reference Model in Direct Preference Optimization (DPO)? A Practical Evaluation of Ideal KL-Divergence Constraints and Importance

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…

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