Articles tagged with Deep Learning
Autograph, a new framework, uses graph neural networks and deep reinforcement learning to achieve higher accuracy and faster execution of compute-intensive programs. It outperformed other approaches across various datasets, with notable improvements on Polybench, NPB, and SPEC 2006 benchmarks.
A new security framework based on blockchain technology and distributed reinforcement learning ensures secure data storage and transmission while adapting to evolving threats. The framework demonstrated improved memory consumption and transaction latency compared to existing approaches.
Researchers warn that advances in AI and neurotechnology are outpacing our understanding of consciousness, with potential serious ethical consequences. A better understanding of consciousness could have major implications for AI, prenatal policy, animal welfare, medicine, mental health, law, and emerging neurotechnologies.
Researchers Prof Axel Cleeremans, Prof Anil Seth, and Prof Liad Mudrik warn that advances in AI and neurotechnology are outpacing our understanding of consciousness. They emphasize the need for theory-driven research and innovative methods to advance consciousness science.
Researchers develop AI-powered methods for modeling the Gulf of Mexico's dynamics, achieving higher accuracy for short-term predictions and emulating 10-year dynamics without hallucinations. This breakthrough drives forward critical management of natural resources in the U.S. and Mexico, advancing AI technology in earth sciences.
The Stowers Institute has appointed its first AI Fellow, Sumner Magruder, to harness the potential of artificial intelligence in biological research. He will collaborate with researchers to design new algorithms and unlock insights from large datasets.
A novel machine learning framework combines interpretable deep learning with multiscale computational techniques to predict lattice thermal conductivity. The approach identifies high-performance materials for thermal management and energy conversion, providing deeper insights into heat transfer at the atomic scale.
Researchers at HUN-REN Szegedi Biológiai Kutatóközpont have developed an AI-powered platform for automated 3D cell culture analysis, enabling high-precision screening of cellular models. The technology removes the limitation of throughput in personalized medicine, allowing for fast and accurate analysis of clinical samples.
Recent research found that large language models are not yet able to consistently fool humans in conversations. They struggle with using discourse markers, opening and closing features, and subtle imitations. Despite rapid development, key differences between human and artificial conversations will likely remain.
Researchers at Edith Cowan University have developed a new way to measure biological age using AI, combining IgG N-glycome and transcriptome data. The method, called gtAge, predicts age with high accuracy and links to health markers, offering potential for early detection of age-related diseases.
VFF-Net applies label-wise noise labelling, cosine similarity-based contrastive loss, and layer grouping to improve image classification performance compared to conventional forward-forward networks. The algorithm reduces test errors on various datasets, enabling lighter and more brain-like training methods that make AI more sustainable.
MetaSeg achieves the same segmentation performance as U-Nets but requires 90% fewer parameters, making medical image segmentation more cost-effective. The new approach leverages implicit neural representations to quickly adjust to new images and decode accurate labels.
A new deep learning framework, Themeda, achieves high accuracy in predicting annual land cover categories across Australia's vast savanna biome. By integrating satellite data with environmental predictors, the model delivers probabilistic outputs that reflect uncertainty and captures ecological shifts at multiple spatial scales.
Researchers developed an AI-based generative approach to discovering technology opportunities from patent maps using machine learning. The system translates patent vacancies into human-readable text, enabling the identification of untapped technologies and facilitating innovation forecasting.
A novel framework integrates Kolmogorov–Arnold networks with dynamic predictor pruning optimization to improve TC intensity prediction. TCI–KAN achieves superior accuracy in 6-h intensity forecasts, outperforming referenced best records by 31%, 13%, and 6%. The model's accuracy varies by region and TC category.
A team of researchers developed a computational method that can design intrinsically disordered proteins with desired properties. The work uses automatic differentiation to optimize protein sequences and leverages molecular dynamics simulations for precision. This breakthrough has the potential to reveal new insights into diseases like...
A new 'future-guided' AI method developed at the University of California, Santa Cruz, has shown significant improvements in predicting seizures using brain wave data. The technique operates with two deep learning models working together, improving predictions further into the future by transferring knowledge.
A new deep learning approach, Electrode Net, accelerates the design of porous electrodes in electrochemical devices, achieving high accuracy and speed. The method outperforms traditional models on benchmarks, enabling rapid screening of large design spaces.
Biomedical engineers at Duke University developed a platform combining automated wet lab techniques and AI to design nanoparticles for drug delivery. The TuNa-AI platform resulted in a 42.9% increase in successful nanoparticle formation compared to standard approaches.
Researchers developed MoBluRF, a two-stage motion deblurring method for NeRFs, achieving high-quality 3D reconstructions from ordinary blurry videos. The framework outperforms state-of-the-art methods and is robust against varying degrees of blur, enabling smartphones to produce sharper and more immersive content.
A deep learning model achieved up to 98% accuracy in distinguishing autistic from neurotypical participants, providing clear insights into brain regions most influential to its decisions. The model could benefit autistic people and clinicians by offering accurate and explainable results to inform assessment and support.
Researchers developed an AI-driven algorithm that can predict nearly 70% of hot flashes before they're felt. The Embr Wave wearable device will incorporate this technology to mitigate symptoms and provide meaningful relief.
The book provides an overview of agent-based modeling and multi-agent systems, highlighting their application in understanding economic crises. It integrates machine learning to enhance adaptation and behavior of agents in dynamic environments.
A new machine learning model predicts heart disease risk in women by analyzing mammograms, offering a 'two-for-one' screening approach that combines breast and cardiovascular screenings. The model performs comparable accuracy to traditional risk calculators without requiring extensive clinical data.
A deep learning approach called Electrode Net optimizes porous-electrode design without sacrificing accuracy. The method achieves high predictive accuracy and speeds up computation time by 96%, enabling rapid screening of large design spaces.
Biochar, a carbon-rich material, is gaining attention for its ability to improve soils, clean water, and capture carbon. Machine learning models can predict biochar yield and pollutant removal efficiency with over 90% accuracy, accelerating its development.
A new tool called Flexynesis uses deep neural networks to evaluate multi-modal data, enabling doctors to make better diagnoses and develop more precise treatment strategies for patients. The tool is designed to be flexible and accessible to non-experts, bridging the gap in precision cancer therapy.
Researchers used AI models with integrated clinical and claims data to predict chronic kidney disease (CKD) progression to end-stage renal disease (ESRD). The study found that the models outperformed single data source models and reduced racial bias. The findings can inform likelihood and management of CKD, supporting targeted interven...
Researchers developed CerviPro, a multimodal deep learning model that accurately identifies high-risk patients with locally advanced disease. The model achieved superior predictive performance compared to conventional methods and provided critical prognostic insights.
Researchers at the University of Vaasa developed smart packaging that can detect subtle color changes in printed packages, enabling cost-effective solutions for industries like food and beverage, healthcare, and logistics. This technology provides a human-eye accurate and environmentally friendly alternative to electronic sensors, pavi...
Artificial neural networks offer superior predictive accuracy in predicting biodiesel properties and enable rapid assessment of diverse feedstock options. Hybrid models combining generative and discriminative approaches achieve significant yield improvements and optimize biodiesel production from waste cooking oil.
A new AI-powered solution enables robots to navigate without a map by training them to evaluate visual richness and avoid collisions. The approach combines deep reinforcement learning with real-time feedback from RGB-D camera input and ORB-SLAM2, achieving improved navigation success rates in simulated tests.
A new deeplearning framework uses federated transfer learning to predict battery state of health during fast charging, preserving user privacy. The framework outperforms traditional methods and has been integrated into intelligent battery management systems.
A team of researchers developed a method to annotate biopsy image data with eye-tracking devices, reducing the burden on pathologists. The resulting AI model achieved an accuracy of 96.3% and surpassed human pathologists' performance in diagnosing skin lesions.
A new AI-assisted model combines MRI, biochemical, and clinical information to predict worsening knee osteoarthritis with high accuracy. The model showed improved accuracy in predicting worsening pain and joint space narrowing, suggesting its potential to enhance care.
Researchers at UC Berkeley developed an AI-powered training method called Human-in-the-Loop Sample Efficient Robotic Reinforcement Learning (HiL-SERL) that enables robots to perform complicated tasks with precision and speed. With human feedback, robots learn from demonstrations and real-world attempts, achieving a 100% success rate in...
Researchers are combining machine learning algorithms with neuromorphic hardware to build brain-like devices that can learn from data and adapt in real-time. These devices have the potential to revolutionize industries such as manufacturing by enabling machines to sense their environment, adapt to new tasks, and make decisions without ...
A research team developed an optimization method for path planning in uncertain environments using deep reinforcement learning and action curiosity module. The algorithm showed remarkable improvements in convergence speed, training duration, and path planning success rate compared to baseline algorithms.
Keren Zhou receives $274,265 from NSF for a project on DLToolkit, a novel performance profiling and analysis infrastructure for scientific deep learning workloads. The toolkit aims to foster innovation in scientific applications using DL.
A new deep learning model, MSI-SEER, achieves high accuracy in predicting microsatellite instability-high tumors and their responsiveness to immunotherapy. The model combines tumor MSI status with stroma-to-tumor ratio for highly accurate ICI response prediction.
Researchers developed an innovative AI approach called GraSSCoL to predict complex astrochemical reactions. The model achieved outstanding Top-k accuracy scores, outperforming earlier state-of-the-art models by a significant margin.
A new AI tool can learn to read medical images with far less data, cutting down the amount of required data by up to 20 times. The tool improves upon medical image segmentation, a labor-intensive task often performed by experts, and boosts model performance in settings with limited annotated data.
A deep learning-based model enables fast and accurate stroke risk prediction by segmenting carotid arterial vessel lumens, vessel walls, and plaques in MRI images. The model achieves high accuracy in plaque segmentation, outperforming manual methods, and completes assessment in under 3 seconds.
A team of computer scientists created 2,300 original sudoku puzzles and asked AI tools like OpenAI's ChatGPT to solve them. The results showed that while some AI models could solve easy sudokus, most struggled to provide accurate explanations, raising questions about the trustworthiness of AI-generated information.
Researchers found that ChatGPT-4 performed better across demographic groups, while LLaVA showed significant sex-related biases in diagnosing skin diseases from medical images. The study emphasizes the need to address these biases to ensure AI models are safe and effective for all patients.
The team aims to deliver AI power directly to devices, improving resilience and speed in constrained environments. By processing data step-by-step across a network of devices, they can create a safe and adaptable system that can withstand attacks and extreme conditions.
Researchers use AI to solve differential equations, such as Schrodinger's equation, for large-scale systems, improving efficiency and accuracy in fields like drug discovery and material design.
Researchers developed an AI-based screening tool using pangrams to detect Parkinson’s disease with nearly 86 percent accuracy. The web-based test analyzes voice recordings for subtle patterns linked to the neurodegenerative disease, identifying potential warning signs.
Researchers developed a smart neural network model that combines CNNs and RNNs to predict multicolor soliton evolution, surpassing limitations of standard frameworks. The dual-channel system accurately tracks changes in energy, wavelength, and phase with remarkable accuracy.
Researchers developed an AI model that accurately differentiates patients with various neurodegenerative disorders using 3D brain imaging data. The model achieved high accuracy, particularly for vascular dementia and Alzheimer's disease, by focusing on subcortical brain structures.
Researchers found that deep neural networks exhibit absorbing phase transitions, a phenomenon observed in physical systems like forest fires. This discovery provides a unified framework describing how the signal propagates between layers of neurons, enabling prediction of trainability and generalizability.
Researchers developed OmicsTweezer, a tool that uses machine learning and single-cell data integration to analyze human tissue. The tool can estimate cell type composition in tumors and surrounding tissues, which could help pinpoint potential therapeutic targets.
A new deep learning model enhances handheld 3D medical imaging by automatically tracking transducer motion without external sensors. The model produces more realistic 3D US images and can reconstruct blood vessel structures using ultrasound and photoacoustic data.
Researchers developed a simple model that reproduces deep neural network features, allowing for optimized parameter tuning. The 'folding ruler' model demonstrates how nonlinearity and noise improve network performance, enabling more efficient training without trial-and-error.
Researchers developed an AI system that enables a four-legged robot to adapt its gait to different terrain, just like animals. The robot learned to switch gaits on the fly and navigate uneven surfaces without any alterations to the system itself, overcoming previous limitations around adaptability.
Researchers have developed an AI-assisted diagnostic system that can estimate bone mineral density in the lumbar spine and femur with high sensitivity and specificity. The system has the potential to transform routine clinical X-rays into a powerful tool for opportunistic screening, enabling earlier detection of osteoporosis.
Researchers developed an AI-powered diagnostic approach using quantitative biomarkers and biometrics to rapidly assess neurodivergent disorders. The method has the potential to diagnose autism or ADHD in as little as 15 minutes, providing healthcare providers with additional tools to tailor treatments.
A new study reveals that AI systems transition from relying on word positions to meaning-based understanding as they receive enough data for training. The transition occurs abruptly, similar to a phase transition in physical systems, and is driven by the amount of data available.
A new technique called WeGeFT improves large language model performance in tasks such as commonsense reasoning and code generation. By fine-tuning key parameters, researchers reduce the need for significant computational power, advancing the field of artificial intelligence.
A team of researchers at Tohoku University's AIMR used machine learning potential to characterize Sn catalyst activity, identifying the most effective catalysts for CO2 reduction. The study provides novel insights into the behavior of Sn-based catalysts and could lead to more efficient fuel production.