Quesma Releases OTelBench: Independent Benchmark Reveals Frontier LLMs Struggle with Real-World SRE Tasks

New benchmark shows top LLMs achieve only 29% pass rate on OpenTelemetry instrumentation, exposing the gap between ...
North Penn Now

Machine Learning Using Python: A Complete Learning Path With Practical Projects

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
Dot Physics on MSN

Python simulation of Faraday’s law electrodynamics part 2

Learn how to simulate Faraday’s Law in electrodynamics using Python (Part 2)! In this video, we continue our step-by-step tutorial on modeling electromagnetic induction, showing how changing magnetic ...
Dot Physics on MSN

Python version of Faraday’s law explained electrodynamics part 1

Dive into Faraday’s Law of Electromagnetic Induction with a practical Python implementation in this first part of our Electrodynamics series. Learn how to simulate and visualize changing magnetic ...
IEEE

JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach

Abstract: In 2019, the JPEG Standardization Committee initiated JPEG AI to define the first image coding specifications, taking advantage of an end-to-end learning-based coding approach. The JPEG AI ...
GitHub

verl: Volcano Engine Reinforcement Learning for LLMs

verl is a flexible, efficient and production-ready RL training library for large language models (LLMs). verl is the open-source version of HybridFlow: A Flexible and Efficient RLHF Framework paper.
GitHub

OSU-NLP-Group/cobalt

Recently, there have been significant research interests in training large language models (LLMs) with reinforcement learning (RL) on real-world tasks, such as multi-turn code generation. While online ...
marktechpost

A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...
IEEE

Spiking Variational Policy Gradient for Brain Inspired Reinforcement Learning

Abstract: Recent studies in reinforcement learning have explored brain-inspired function approximators and learning algorithms to simulate brain intelligence and adapt to neuromorphic hardware. Among ...