paper – Up As Pro

Unlock transformative advancements in AI with these three cutting-edge techniques. First, learn how to slash your GPU memory usage by up to 50% with a simple PyTorch trick, allowing you to double your batch size by calling backward() on each loss separately. Next, discover SaySelf, a revolutionary framework for Large Language Models (LLMs) that drastically improves confidence estimation by 30%, providing more reliable self-reflective rationales and reducing errors. Finally, dive into the world of neural diffusion models with a technique that edits syntax trees directly, boosting code generation efficiency by 20% and enhancing debugging accuracy. These innovations are poised to redefine AI performance, making your models faster, more efficient, and safer.

Algorithm FEATURED Machine Learning paper Series

October 11, 2024 admin

Deep dive: Llama3 from scratch, LinearBoost, LoRA Learns and Forgets Less

In this post, we’ll explore three groundbreaking advancements that are pushing the boundaries of AI and machine learning. First, dive into the intricacies of LLaMa 3, implemented from scratch in Python, where every aspect, from attention mechanisms to tokenization, is meticulously explained, making it a must-see for anyone interested in model architecture. Next, discover how LinearBoost, a new linear classifier-based algorithm, outperforms traditional GBDTs like CatBoost and XGBoost, showcasing superior accuracy and response time across five benchmark datasets. Lastly, we’ll delve into the debate on Low-Rank Adaptation (LoRA) in fine-tuning large language models, revealing why LoRA might not match full fine-tuning in specialized domains but offers remarkable regularization benefits. These insights are not only educational but also essential for staying at the forefront of AI research and application.

UpasPro, Pedram Agand personal blog. AI and financial advice

Academic Concept Machine Learning paper

September 27, 2024 admin

Are Models Converging Towards the Same Representation of the World?

Are AI models converging in the way they represent the world? This recent paper says yes. The authors analyze several language and vision models, their learned latent spaces and the way they measure distances between data points, and conclude that there is a noticeable and growing alignment between all of them.

Academic Code Concept Machine Learning paper Series

August 30, 2024 admin

Deep Dive: FineTune small GPT for SPAM, ScrapeGraphAI, Parallelizable LSTMs

Sebastian Raschka guides users in fine-tuning a small GPT model to classify SPAM messages with 96% accuracy. ScrapeGraphAI is a Python library that automates data extraction from websites using LLMs. And Sepp Hochreiter’s xLSTM architecture extends traditional LSTMs to compete with state-of-the-art Transformers. These innovations are making AI more accessible and efficient! 🚀🤖📚

Academic Algorithm Code Concept paper Series

August 16, 2024 admin

Deep dive: Transformers by Gemma, Iterative Reasoning PO, inner work of Transformers

Demystifying Transformers with Google’s Gemma, boosting reasoning tasks with Meta’s Iterative Reasoning Preference Optimization, and enhancing understanding of Transformer models with a unified interpretability framework. These are the latest strides in AI, making complex concepts accessible and improving model performance. Stay tuned for more! 🚀🧠🤖

Academic Concept paper Series

June 21, 2024 admin

Top papers: CIFAR-10 94% in 3.29 Sec, Gemini Infinite Context Method, Microsoft’s Vasa-1

Achieve 94% accuracy on CIFAR-10 in just 3.29 seconds using a single NVIDIA A100 GPU, scaling up to 96% in 46.3 seconds with advanced techniques. Integrate strategies like patch-whitening, identity initialization, higher learning rate for biases, Lookahead optimization, multicrop TTA, and alternating flip for augmentation. Utilizing torch.compile for efficient GPU usage, this method significantly speeds up ML experiments and reduces costs, showing a 1.9× speed boost over previous records. Learn how these techniques can generalize across small-scale tasks and contribute to rapid model training.

Academic Code Concept paper Series

June 19, 2024 admin

Deep dive: LLM priority for RAG, AIOS, More Agents Is All You Need

Ever wondered if large language models (LLMs) stick to the information they retrieve or if they rely on their internal knowledge? In this video, we dive into a recent paper from Stanford University that explores this very question. Discover the experiments they conducted, the surprising insights they uncovered, and what it means for the future of AI. We’ll also touch on AIOS, a revolutionary LLM Agent Operating System, and how it’s changing the way we interact with machines. Stay tuned to the end for the most interesting revelations about the performance of LLMs with manipulated data!

Academic Code Concept paper Series

June 7, 2024 admin

Top papers: Voicecraft, T-Rex, Mixture-of-Depths

This article examines three AI innovations: Voicecraft, T-Rex2, and Mixture-of-Depths (MoD). Voicecraft accelerates speech synthesis with a neural codec model, while T-Rex2 enhances zero-shot object detection with combined text and visual prompts. MoD improves processing efficiency by dynamically allocating computation in transformers, potentially cutting computational overhead by 50%. These advancements promise significant impacts across various industries.

Code Concept paper Series

May 31, 2024 admin

Top papers: Deleting 40% Without Accuracy Drop, Turbo Sketch, AnimateDiff

Recent research by Meta, Cisco, and MIT shows that pruning 40-50% of Large Language Model layers can maintain accuracy, offering faster, cost-effective AI. Novel techniques like Img2Img Turbo Sketch and AnimateDiff-Lightning demonstrate efficient image creation and rapid video generation from text. These developments suggest smaller AI models can be equally effective, heralding a potential shift in resource utilization and training methods.

Code Concept paper

May 22, 2024 admin

1-bit LLMs by Microsoft | Implementing GPT in 60 Lines

Microsoft released BitNet b1.58, a new Large Language Model (LLM) using 1.58 bits per parameter, reducing computational demands significantly while