Time Series Analysis with Transformers: Fixed vs Learned Time Representation

Why Do Multimodal LLMs (MLLM) Struggle with Spatial Understanding? Research shows that MLLMs’ spatial difficulties isn’t from data scarcity, but from architecture. Spatial ability relies on the vision encoder’s positional cues, so a redesign like prompt targeting is much needed. https://lnkd.in/g4mjCeQi

🔬 Excited to share the publication "FADEL: Ensemble Learning Enhanced by Feature Augmentation and Discretization". 🥼 Authored by Chuan-Sheng Hung, Chun-Hung Richard Lin, Shi-Huang Chen, You-Cheng Zheng, Cheng-Han Yu, Cheng-Wei Hung, Ting-Hsin Huang and Jui-Hsiu Tsai. 🚀 This study proposes a novel architecture, FADEL. FADEL introduces a unique feature augmentation ensemble framework that preserves the original data distribution by concurrently processing continuous and discretized features. Welcome to access the full article freely here 👉 https://lnkd.in/guvNXab3 And welcome to follow our LinkedIn account @Bioengineering MDPI. 👏 #imbalance_class_classification #data_augmentation  #feature_augmentation #feature_discretization #ensemble_learning

Really helpful visualization explaining the core of the transformer architecture. Anyone learning/experimenting/building with AI will find this very helpful.

We are using text-generative models like GPT, Llama and Gemini in almost all our day-2-day activities. But did we stop thinking what happens under the hood and how it works? These models are based on Transformer architecture, which was first introduced at 2017. The brake down of this architecture can be a bit hard to explain, but there is a great visualization (credit to: https://lnkd.in/dnyivbv5) that helps understanding the different phases and calculation along the way and thought to share with you: https://lnkd.in/dcggCpz9 I hope that can help make some order :) If not, feel free to reach out to discuss.

In his latest SemiWiki article, Dr. Thang Tran introduces Simplex Micro’s Unified Deterministic Architecture — a design that unites scalar, vector, and matrix compute in one deterministic pipeline. At its core is Predictive Execution: static, cycle-precise scheduling that replaces speculation with clarity and control. Backed by six newly issued patents, this architecture offers a clean break from decades of patchwork built on the Von Neumann and Harvard models. It delivers deterministic, predictable performance across both general-purpose and AI workloads — while staying fully compatible with existing software flows. Read here: https://lnkd.in/gBhVWcfi #RISC_V  #ComputerArchitecture #DeterministicComputing  #PredictiveExecution  #AIChips  #Semiconductors  #HighPerformanceComputing  #VectorProcessing  #MatrixCompute #HPC

In his latest SemiWiki article, Dr. Thang Tran introduces Simplex Micro’s Unified Deterministic Architecture — a design that unites scalar, vector, and matrix compute in one deterministic pipeline. At its core is Predictive Execution: static, cycle-precise scheduling that replaces speculation with clarity and control. Backed by six newly issued patents, this architecture offers a clean break from decades of patchwork built on the Von Neumann and Harvard models. It delivers deterministic, predictable performance across both general-purpose and AI workloads — while staying fully compatible with existing software flows. Read here: https://lnkd.in/giYnD-6Z #RISC_V  #ComputerArchitecture  #DeterministicComputing  #PredictiveExecution  #AIChips  #Semiconductors  #HighPerformanceComputing  #VectorProcessing  #MatrixCompute #HPC

The most comprehensive, LLM architecture analysis one can read. Covers every flagship model: 1. DeepSeek V3/R1 2. OLMo 2 3. Gemma 3 4. Mistral Small 3.1 5. Llama 4 6. Qwen3 7. SmolLM3 8. Kimi 2 9. GPT-OSS Made by Sebastian Raschka, PhD

#Highlycitedpaper 📖 U-Net-Based CNN Architecture for Road Crack Segmentation ✍ By Alessandro Di Benedetto, Margherita Fiani and Lucas Gujski 🏘️ From University of Salerno 👉 https://lnkd.in/gvnrexP8 ✨ The aim of this study is to optimize the crack segmentation process through the implementation of a modified U-Net model based algorithm. For this, the Crack500 Dataset proposed by Yang et al. in 2019 was used, and then the results were compared with those obtained from the algorithm that is currently found to be the most accurate and performant in the literature, U-Net by Lau et al. The results are promising and accurate, and the shape and width of the segmented cracks are very close to reality.  #infrastructures #road #roadcrack #convolutionalneuralnetworks

I wrote a short article (link in replies) about LFM-2's (by Liquid AI) hybrid architecture w/ illustration + simple pytorch impl.

Antisqueezing noise boosts image generation quality by up to 15%. 💡 Improves generative model performance without changing architecture, offering a competitive edge in AI-driven image applications. 🇺🇸 Arxiv paper: 📄 https://lnkd.in/ecuxTPGt Author: Jyotirmai Singh, Samar Khanna, James Burgess Stanford University #ComputerScience #MachineLearning #arXiv