Efficient
Artificial Intelligence
The Rise of Mixture-of-Experts for Efficient Large Language Models
On this planet of natural language processing (NLP), the pursuit of constructing larger and more capable language models has been a driving force behind many recent advancements. Nonetheless, as these models grow in size,...
Artificial Intelligence
Navigating Cost-Complexity: Mixture of Thought LLM Cascades Illuminate a Path to Efficient Large Language Model Deployment
What if I told you that you can save 60% or more off of the associated fee of your LLM API spending without compromising on accuracy? Surprisingly, now you may.Large Language Models (LLMs) are...
Artificial Intelligence
LoRa, QLoRA and QA-LoRA: Efficient Adaptability in Large Language Models Through Low-Rank Matrix Factorization
Large Language Models (LLMs) have carved a singular area of interest, offering unparalleled capabilities in understanding and generating human-like text. The facility of LLMs might be traced back to their enormous size, often having...
Artificial Intelligence
Machine-learning system based on light could yield more powerful, efficient large language models
ChatGPT has made headlines world wide with its ability to write down...
Artificial Intelligence
Implementing math in deep learning papers into efficient PyTorch code: SimCLR Contrastive Loss
IntroductionOne of the perfect ways to deepen your understanding of the mathematics behind deep learning models and loss functions, and likewise an incredible strategy to improve your PyTorch skills is to get used to...
Artificial Intelligence
Empowering Efficient BO Transfer with Neural Acquisition Process (NAP) General Objectives & Results: From Bayesian Optimisation to Meta-Bayesian Optimisation: Neural Acquisition Processes (NAP): Cool Properties:
Our primary objective is to reinforce the effectiveness of Bayesian Optimisation (BO) by leveraging meta-learning to transfer knowledge across different problem domains, thereby significantly improving sample efficiency.In pursuit of this goal, we introduce the...
Artificial Intelligence
Empowering Efficient BO Transfer with Neural Acquisition Process (NAP) General Objectives & Results: From Bayesian Optimisation to Meta-Bayesian Optimisation: Neural Acquisition Processes (NAP): Cool Properties:
Our primary objective is to boost the effectiveness of Bayesian Optimisation (BO) by leveraging meta-learning to transfer knowledge across different problem domains, thereby significantly improving sample efficiency.In pursuit of this goal, we introduce the...
Artificial Intelligence
XGBoost: How Deep Learning Can Replace Gradient Boosting and Decision Trees — Part 1 XGBoost is extremely efficient, but… Constructing Decision Trees just isn’t a differentiable...
If you've gotten read my previous articles on Gradient Boosting and Decision Trees, you're aware that Gradient Boosting, combined with Ensembles of Decision Trees, has achieved excellent performance in classification or regression tasks involving...
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