AlphaProteo generates novel proteins for biology and health research Google DeepMind Blog
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[[{“value”:” When training a machine learning model, you may sometimes work with datasets with a large number of features. However, only a small subset of these features will actually be important for the model to make predictions. Which is why you need feature selection to… Read More »Tips for Effective Feature Selection in Machine Learning Bala Priya C MachineLearningMastery.com
[[{“value”:” Large Language Models (LLMs) have demonstrated great performance in Natural Language Processing (NLP) applications. However, they have high computational costs when fine-tuning them, which can lead to incorrect information being generated, i.e., hallucinations. Two viable strategies have been established to solve these problems: parameter-efficient… Read More »Enhancing Fact-Checking with LoraMap: A Neuroscience-Inspired Approach to Efficient LoRA Integration Tanya Malhotra Artificial Intelligence Category – MarkTechPost
[[{“value”:” Machine learning has revolutionized various fields, offering powerful tools for data analysis and predictive modeling. Central to these models’ success is hyperparameter optimization (HPO), where the parameters that govern the learning process are tuned to achieve the best possible performance. HPO involves selecting hyperparameter… Read More »This AI Paper from Cornell and Brown University Introduces Epistemic Hyperparameter Optimization: A Defended Random Search Approach to Combat Hyperparameter Deception Nikhil Artificial Intelligence Category – MarkTechPost
[[{“value”:” Hypergraphs, which extend traditional graphs by allowing hyperedges to connect multiple nodes, offer a richer representation of complex relationships in fields like social networks, bioinformatics, and recommender systems. Despite their versatility, generating realistic hypergraphs is challenging due to their complexity and the need for… Read More »HYGENE: A Diffusion-Based Deep Learning Approach for Hypergraph Generation and Modeling Sana Hassan Artificial Intelligence Category – MarkTechPost
[[{“value”:” Spiking Neural Networks (SNNs) hold significant promise in developing energy-efficient and biologically plausible artificial neural networks. However, a critical challenge is their limited ability to handle sequential tasks such as text classification and time-series forecasting. This limitation primarily stems from the lack of an… Read More »Could Brain-Inspired Patterns Be the Future of AI? Microsoft Investigates Central Pattern Generators in Neural Networks Aswin Ak Artificial Intelligence Category – MarkTechPost
[[{“value”:” Information management and retrieval systems are essential for businesses and organizations, whether for customer support, internal knowledge bases, academic research, or instructional purposes. It can be challenging to manage enormous data volumes while ensuring users can quickly locate what they need. Regarding privacy issues,… Read More »MaxKB: Knowledge-based Question-Answering System based on Large Language Model and RAG Niharika Singh Artificial Intelligence Category – MarkTechPost
[[{“value”:” Accurately transcribing spoken language into written text is becoming increasingly essential in speech recognition. This technology is crucial for accessibility services, language processing, and clinical assessments. However, the challenge lies in capturing the words and the intricate details of human speech, including pauses, filler… Read More »CrisperWhisper: A Breakthrough in Speech Recognition Technology with Enhanced Timestamp Precision, Noise Robustness, and Accurate Disfluency Detection for Clinical Applications Sana Hassan Artificial Intelligence Category – MarkTechPost
[[{“value”:” Artificial intelligence is rapidly advancing, with a significant focus on improving models that process and interpret complex datasets, particularly time series data. This type of data involves sequences of data points collected over time and is critical in various fields, including finance, healthcare, and… Read More »A Novel Hybrid Approach Combining Hyperdimensional Vector Computing and Tsetlin Machines for Efficient Sequence Learning, Classification, and Forecasting in High-Dimensional Time Series Data Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Label-efficient segmentation has emerged as a crucial area of research, particularly in point cloud semantic segmentation. While deep learning techniques have advanced this field, the reliance on large-scale datasets with point-wise annotations remains a significant challenge. Recent methods have explored weak supervision, human annotations,… Read More »Enhancing Segmentation Efficiency: A Unified Approach for Label-Limited Learning Across 2D and 3D Data Modalities Shoaib Nazir Artificial Intelligence Category – MarkTechPost