CMU researchers propose XEUS: a multilingual universal speech encoder trained on over 4000 languages
Self-supervised learning (SSL) has expanded the reach of speech technologies to many languages by minimizing the need for labeled data. ...
Tag: Researchers
This AI article by researchers at Tencent AI Lab introduces Persona-Hub: A collection of 1 billion diverse personas to scale synthetic data
Synthetic data generation has become crucial in training large language models (LLMs). This field focuses on creating artificial datasets that ...
University of Maryland Researchers Introduce GenQA Instructional Dataset: Automating Large-Scale Instructional Dataset Generation for AI Model Tuning and Diversity Improvement
Natural language processing has greatly improved language model tuning. This process involves honing ai models to perform specific tasks more ...
Pantera Capital Researchers Say Solana Will Steal Market Share From Ethereum, Here's Why
Analysts at the cryptocurrency-focused asset manager Pantera Capital have presented bullish arguments in favor of solana ecosystem compared to ethereum. ...
New York University researchers propose intermodal and intramodal (I2M2) models for multimodal learning, capturing both intermodal and intramodal dependencies
In supervised multimodal learning, data from multiple modalities is mapped to a target label using information about the boundaries between ...
UC Berkeley researchers propose a neural diffusion model that operates on syntax trees for program synthesis
Large language models (LLMs) have revolutionized code generation, but their autoregressive nature poses a significant challenge. These models generate code ...
Stanford researchers propose SleepFM: a new multimodal basic model for sleep analysis
Sleep medicine is a fundamental field that involves monitoring and evaluating physiological signals to diagnose sleep disorders and understand sleep ...
Arizona State University Researchers Evaluate ReAct Prompts: The Role of Example Similarity in Improving Reasoning of Large Language Models
Large language models (LLM) have advanced rapidly, especially in natural language processing (NLP) and natural language understanding (NLU). These models ...
Researchers at UC Berkeley, UIUC, and NYU developed an algorithmic framework that uses reinforcement learning (RL) to optimize vision and language models (VLM)
Using linguistic thinking, broad vision-language models (VLMs) have demonstrated remarkable capabilities as adaptive agents that can solve a wide range ...
Microsoft researchers present MatterSim: a deep learning model for materials in real-world conditions
Methods such as molecular dynamics simulations, quantitative structure-property relationships (QSPR), and first-principles calculations are based on scientific principles and complex ...
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