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Transfer Learning for Structured Pruning under Limited Task Data Apple Machine Learning Research

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​[[{“value”:”This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP-III) Workshop at NeurIPS. Large, pre-trained models are problematic to use in resource constrained applications. Fortunately, task-aware structured pruning methods offer a solution. These approaches reduce model size by dropping structural units like… Read More »Transfer Learning for Structured Pruning under Limited Task Data Apple Machine Learning Research

Accurate Knowledge Distillation via N-best Reranking Apple Machine Learning Research

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​We propose utilizing n-best reranking to enhance Sequence-Level Knowledge Distillation (Kim and Rush, 2016) where we extract pseudo-labels for student model’s training data from top n-best hypotheses and leverage a diverse set of models with different inductive biases, objective functions or architectures, including some publicly-available… Read More »Accurate Knowledge Distillation via N-best Reranking Apple Machine Learning Research

Achieve up to ~2x higher throughput while reducing costs by up to ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 2 James Wu AWS Machine Learning Blog

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​[[{“value”:” As generative artificial intelligence (AI) inference becomes increasingly critical for businesses, customers are seeking ways to scale their generative AI operations or integrate generative AI models into existing workflows. Model optimization has emerged as a crucial step, allowing organizations to balance cost-effectiveness and responsiveness,… Read More »Achieve up to ~2x higher throughput while reducing costs by up to ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 2 James Wu AWS Machine Learning Blog

Achieve up to ~2x higher throughput while reducing costs by ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 1 Raghu Ramesha AWS Machine Learning Blog

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​[[{“value”:” Today, Amazon SageMaker announced a new inference optimization toolkit that helps you reduce the time it takes to optimize generative artificial intelligence (AI) models from months to hours, to achieve best-in-class performance for your use case. With this new capability, you can choose from… Read More »Achieve up to ~2x higher throughput while reducing costs by ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 1 Raghu Ramesha AWS Machine Learning Blog

Anthropic Claude 3.5 Sonnet ranks number 1 for business and finance in S&P AI Benchmarks by Kensho Qingwei Li AWS Machine Learning Blog

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​[[{“value”:” Anthropic Claude 3.5 Sonnet currently ranks at the top of S&P AI Benchmarks by Kensho, which assesses large language models (LLMs) for finance and business. Kensho is the AI Innovation Hub for S&P Global. Using Amazon Bedrock, Kensho was able to quickly run Anthropic… Read More »Anthropic Claude 3.5 Sonnet ranks number 1 for business and finance in S&P AI Benchmarks by Kensho Qingwei Li AWS Machine Learning Blog

NVIDIA Introduces RankRAG: A Novel RAG Framework that Instruction-Tunes a Single LLM for the Dual Purposes of Top-k Context Ranking and Answer Generation in RAG Mohammad Asjad Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Retrieval-augmented generation (RAG) has emerged as a crucial technique for enhancing large language models (LLMs) to handle specialized knowledge, provide current information, and adapt to specific domains without altering model weights. However, the current RAG pipeline faces significant challenges. LLMs struggle with processing numerous… Read More »NVIDIA Introduces RankRAG: A Novel RAG Framework that Instruction-Tunes a Single LLM for the Dual Purposes of Top-k Context Ranking and Answer Generation in RAG Mohammad Asjad Artificial Intelligence Category – MarkTechPost

5 Tips for Getting Started with Deep Learning Cornellius Yudha Wijaya MachineLearningMastery.com

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​[[{“value”:” Deep learning is a subset of machine learning that has become a cornerstone in many technological breakthroughs. At the core of deep learning, it’s a model inspired by the human brain, which we call a neural network. Contrary to the traditional machine learning model,… Read More »5 Tips for Getting Started with Deep Learning Cornellius Yudha Wijaya MachineLearningMastery.com

A Survey of Controllable Learning: Methods, Applications, and Challenges in Information Retrieval Aswin Ak Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Controllable Learning (CL) is emerging as a crucial component of trustworthy machine learning. It emphasizes ensuring that learning models meet predefined targets and adapt to changing requirements without retraining. Let’s delve into the methods and applications of CL, particularly focusing on its implementation within… Read More »A Survey of Controllable Learning: Methods, Applications, and Challenges in Information Retrieval Aswin Ak Artificial Intelligence Category – MarkTechPost

MALT (Mesoscopic Almost Linearity Targeting): A Novel Adversarial Targeting Method based on Medium-Scale Almost Linearity Assumptions Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Adversarial attacks are attempts to trick a machine learning model into making a wrong prediction. They work by creating slightly modified versions of real-world data (like images) that a human wouldn’t notice as different but that cause the model to misclassify them. Neural networks… Read More »MALT (Mesoscopic Almost Linearity Targeting): A Novel Adversarial Targeting Method based on Medium-Scale Almost Linearity Assumptions Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost