Anniversary AI reflections

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Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

Large language models (LLMs) are transforming research in psychology and the behavioral sciences by enabling advanced text analysis at scale. Their applications range from the analysis of social media posts to infer psychological traits to the automated sc ...

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

We introduce BLAP, a model capable of generating high-quality captions for music. BLAP leverages a fine-tuned CLAP audio encoder and a pre-trained Flan-T5 large language model. To achieve effective cross-modal alignment between music and language, BLAP uti ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

Foundation models have become a powerful tool in single-cell transcriptomics, enabling broad generalization across tasks such as cell type annotation, data integration, and drug response prediction. Yet, most current models are trained predominantly on hea ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

Advanced artificial intelligence (AI) models typically achieve high accuracy in wind power forecasting, but their internal mechanisms lack interpretability, which undermines user confidence in forecast value and strategy execution. To this end, this paper ...

Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. These advances are largely due to diffusion-based generative models, which are very stable and simple to train. These diffusion model ...

Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemic ...

Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face image manipulation ...

Generative AI tools (often abbreviated genAI or GAI), such as chatGPT, have arguably great potential to induce a paradigm shift in knowledge work.Herein, we present the results of a survey conducted within the nuclear engineering professional community to ...

Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long- horizon tas ...

Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have proven their effectiveness but typically ...

Marmoset monkeys encode vital information in their calls and serve as a surrogate model for neuro-biologists to understand the evolutionary origins of human vocal communication. Traditionally analyzed with signal processing-based features, recent approache ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Crafting an appealing heading is crucial for attracting readers and marketing work or products. A popular way is to summarize the main idea with a refined description and a memorable acronym. However, there lacks a systematic study and a formal benchmark i ...

In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is ...

Anniversary AI reflections

Chat with Graph Search

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Sequential Reasoning with Socially Caused Beliefs

From Embeddings to Explainability: A Tutorial on Large-Language-Model-Based Text Analysis for Behavioral Scientists

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Bootstrapping Language-Audio Pre-training for Music Captioning

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Abstract IA02: Cancer-specific foundation models: Friend or foe in healthcare AI?

Computational design of complex protein folds and soluble analogs of membrane proteins

Can we trust explainable artificial intelligence in wind power forecasting?

MCMC: Bridging Rendering, Optimization and Generative AI

Leveraging large language models for predictive chemistry

Towards the Detection of AI-Synthesized Human Face Images

Generative AI tools in the nuclear engineering community: A survey-based evaluation of the current adoption and impacts

SIMPLE HIERARCHICAL PLANNING WITH DIFFUSION

From classification to segmentation with explainable AI: A study on crack detection and growth monitoring

On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis

Sparse autoregressive neural networks for classical spin systems

Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm

When Your AI Becomes a Target: AI Security Incidents and Best Practices