ML applications

Researchers from Stanford and Amazon Developed STARK: A Large-Scale Semi-Structure Retrieval AI Benchmark on Textual and Relational Knowledge Bases

ML applications

4 Min Read

Researchers from Stanford and Amazon Developed STARK: A Large-Scale Semi-Structure Retrieval AI Benchmark on Textual and Relational Knowledge Bases

By Editorial Team

May 3, 2024

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Kolmogorov-Arnold Networks (KANs): A New Era of Interpretability and Accuracy in Deep Learning

ML applications

4 Min Read

Kolmogorov-Arnold Networks (KANs): A New Era of Interpretability and Accuracy in Deep Learning

By Editorial Team

May 2, 2024

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Bridging the Binary Gap: Challenges in Training Neural Networks to Decode and Summarize Code

ML applications

4 Min Read

Bridging the Binary Gap: Challenges in Training Neural Networks to Decode and Summarize Code

By Editorial Team

May 2, 2024

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This AI Paper from Princeton and Stanford Introduces CRISPR-GPT For Innovative Gene-Editing Enhancements

ML applications

4 Min Read

This AI Paper from Princeton and Stanford Introduces CRISPR-GPT For Innovative Gene-Editing Enhancements

By Editorial Team

May 2, 2024

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Meta AI Introduces CyberSecEval 2: A Novel Machine Learning Benchmark to Quantify LLM Security Risks and Capabilities

ML applications

4 Min Read

Meta AI Introduces CyberSecEval 2: A Novel Machine Learning Benchmark to Quantify LLM Security Risks and Capabilities

By Editorial Team

May 1, 2024

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Researchers at UC Berkeley Unveil a Novel Interpretation of the U-Net Architecture Through the Lens of Generative Hierarchical Models

ML applications

4 Min Read

Researchers at UC Berkeley Unveil a Novel Interpretation of the U-Net Architecture Through the Lens of Generative Hierarchical Models

By Editorial Team

May 1, 2024

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Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models

ML applications

4 Min Read

Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models

By Editorial Team

May 1, 2024

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REBEL: A Reinforcement Learning RL Algorithm that Reduces the Problem of RL to Solving a Sequence of Relative Reward Regression Problems on Iteratively Collected Datasets

ML applications

4 Min Read

REBEL: A Reinforcement Learning RL Algorithm that Reduces the Problem of RL to Solving a Sequence of Relative Reward Regression Problems on Iteratively Collected Datasets

By Editorial Team

April 30, 2024

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Hippocrates: An Open-Source Machine Learning Framework for Advancing Large Language Models in Healthcare

ML applications

4 Min Read

Hippocrates: An Open-Source Machine Learning Framework for Advancing Large Language Models in Healthcare

By Editorial Team

April 30, 2024

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Researchers at UC San Diego Propose DrS: A Novel Machine Learning Approach for Learning Reusable Dense Rewards for Multi-Stage Tasks in a Data-Driven Manner

ML applications

4 Min Read

Researchers at UC San Diego Propose DrS: A Novel Machine Learning Approach for Learning Reusable Dense Rewards for Multi-Stage Tasks in a Data-Driven Manner

By Editorial Team

April 30, 2024

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ML applications

Researchers from Stanford and Amazon Developed STARK: A Large-Scale Semi-Structure Retrieval AI Benchmark on Textual and Relational Knowledge Bases

Kolmogorov-Arnold Networks (KANs): A New Era of Interpretability and Accuracy in Deep Learning

Bridging the Binary Gap: Challenges in Training Neural Networks to Decode and Summarize Code

This AI Paper from Princeton and Stanford Introduces CRISPR-GPT For Innovative Gene-Editing Enhancements

Meta AI Introduces CyberSecEval 2: A Novel Machine Learning Benchmark to Quantify LLM Security Risks and Capabilities

Researchers at UC Berkeley Unveil a Novel Interpretation of the U-Net Architecture Through the Lens of Generative Hierarchical Models

Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models

REBEL: A Reinforcement Learning RL Algorithm that Reduces the Problem of RL to Solving a Sequence of Relative Reward Regression Problems on Iteratively Collected Datasets

Hippocrates: An Open-Source Machine Learning Framework for Advancing Large Language Models in Healthcare

Researchers at UC San Diego Propose DrS: A Novel Machine Learning Approach for Learning Reusable Dense Rewards for Multi-Stage Tasks in a Data-Driven Manner

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