The Model Selection Showdown: 6 Considerations for Choosing the Best Model
Selecting the right model is one of the most critical decisions in any machine learning project.
Compute-Optimal Quantization-Aware Training
Quantization-aware training (QAT) is a leading technique for improving the accuracy of quantized neural networks. Previ- ous work has shown that decomposing training into a full-precision (FP) phase followed by a QAT phase yields superior accuracy compared to QAT alone. However, the optimal allocation of compute between the FP and QAT phases remains unclear. We …
Modernize fraud prevention: GraphStorm v0.5 for real-time inference
Fraud continues to cause significant financial damage globally, with U.S. consumers alone losing $12.5 billion in 2024—a 25% increase from the previous year according to the Federal Trade Commission. This surge stems not from more frequent attacks, but from fraudsters’ increasing sophistication. As fraudulent activities become more complex and interconnected, conventional machine learning approaches fall short …
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Forecasts and data insights come to BigQuery’s MCP and Agent Development Kit tools
For AI agents to be really useful, they need to be able to securely interact with enterprise data. In July, we introduced a toolset to help AI agents interact with and analyze business data in BigQuery through natural language, and with just a few lines of code. Today, we’re taking the next step, with “Ask …
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40 Best Early Amazon Prime Day Deals on WIRED-Tested Gear (2025)
Amazon Prime Day is back on October 7, but we’ve already found good deals on WIRED-approved gear.
3D printing becomes stronger and more economical with light and AI
Photocurable 3D printing, widely used for everything from dental treatments to complex prototype manufacturing, is fast and precise but has the limitation of being fragile and easily broken by impact. A KAIST research team has developed a new technology to overcome this weakness, paving the way for the more robust and economical production of everything …
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Dark Touch (hidream + wan2.2 + USDU + gimm vfi)
My workflows: https://civitai.com/models/1389968/my-personal-basic-and-simple-wan21wan22-i2v-workflows-based-on-comfyui-native-one Process: 1. HiDream initial txt2img 2. Wan2.2 img2img to fix “realism” 3. Wan2.2 img2vid 4. Wan2.2 upscale (540p -> 1080p) 5. GIMM VFI 6. MMAudio for the sound effect 🙂 Music by Marshall Watson. submitted by /u/alisitskii [link] [comments]
7 Python Decorator Tricks to Write Cleaner Code
Usually shrouded in mystery at first glance, Python decorators are, at their core, functions wrapped around other functions to provide extra functionality without altering the key logic in the function being “decorated”.
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping
While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to the challenges in training large transformer models. Specifically, large models further exacerbate issues in FL as they are particularly susceptible to gradient heterogeneity across layers, unlike the relatively uniform gradient behavior …
100X Faster: How We Supercharged Netflix Maestro’s Workflow Engine
By Jun He, Yingyi Zhang, Ely Spears TL;DR We recently upgraded the Maestro engine to go beyond scalability and improved its performance by 100X! The overall overhead is reduced from seconds to milliseconds. We have updated the Maestro open source project with this improvement! Please visit the Maestro GitHub repository to get started. If you find …
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