The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
SAM is a new segmentation model that can segment objects in images using natural language prompts. It was trained on over 1,100 datasets totaling over 10,000 images using a model-in-the-loop approach. SAM uses a transformer-based architecture with encoders for images, text, bounding boxes and masks. It achieves state-of-the-art zero-shot segmentation performance without any fine-tuning on target datasets.
This document summarizes a microservices meetup hosted by @mosa_siru. Key points include:
1. @mosa_siru is an engineer at DeNA and CTO of Gunosy.
2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway.
3. Challenges discussed were managing 30 microservices, ensuring API latency below 50ms across availability zones, and handling 10 requests per second with nginx load balancing across 20 servers.
This document summarizes a microservices meetup hosted by @mosa_siru. Key points include:
1. @mosa_siru is an engineer at DeNA and CTO of Gunosy.
2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway.
3. Challenges discussed were managing 30 microservices, ensuring API latency below 50ms across availability zones, and handling 10 requests per second with nginx load balancing across 20 servers.
The document discusses BrainPad Inc.'s O2O services for business success, including 10 points about offline services, online services, WiFi, and ensuring copyright with each item attributed to BrainPad Inc. in 2012.
This is the presentation which I explain at the Japanese Java Users Conference Annual Technology Conference is called JJUG CCC 2025 Spring.
In this presentation, I introduced the development productivity using GitHub Copilot. And How to implement Enterprise AI Java application by using LangChain4j. Finally I introduced easy of use and install the SLM(Small Language Model) on your edge device by using Azure AI Foundry Local.