Reinforcement learning (RL) has become a pivotal component of large language model (LLM) post-training, and agentic RL extends this paradigm to operate as agents through multi-turn interaction and tool use. Scaling such systems exposes two practical...
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images. (⭐ 1349)
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code. (⭐ 2184)
Entity Linking (EL) has traditionally relied on large annotated datasets and extensive model fine-tuning. While recent few-shot methods leverage large language models (LLMs) through prompting to reduce training requirements, they often suffer from in...
Feb 22, 2026 · PEORIA, Ariz. -- A wry smile settled on Shohei Ohtani's face as the two-way superstar considered the following scenario. Bottom of the ninth inning. Championship game …
This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective...
3,000 had been delivered and 24 million flight hours have been logged. Caravans have been used for flight training, commuter airlines, VIP transport, air
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We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists with optimal training performance, according to the regularized empirical...
Full day training hands on workshop creating an Azure based Modern Data Warehouse authored by Paul Andrew, Simon Whiteley and Terry McCann (⭐ 19)
As large language models (LLMs) expand multilingual capabilities, questions remain about the equity of their performance across languages. While many communities stand to benefit from AI systems, the dominance of English in training data risks disadv...
Feb 18, 2026 · Employee safety is a top priority at Newell Brands. Over the past year, we maintained the programs that have allowed us to achieve a world-class safety record while deepening our training …
During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or...
open source training courses about distributed database and distributed systems (⭐ 10844)
Creating InSpec commands based on https://github.com/chef-training/workshops/tree/master/InSpec (⭐ 7)
Feb 18, 2026 · Employee safety is a top priority at Newell Brands. Over the past year, we maintained the programs that have allowed us to achieve a world-class safety record while deepening our training …
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MAT...
We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model...
Calibration can reduce overconfident predictions of deep neural networks, but can calibration also accelerate training? In this paper, we show that it can when used to prioritize some examples for performing subset selection. We study the effect of p...
Precise spatial fidelity in Image-to-3D multi-instance generation is critical for downstream real-world applications. Recent work attempts to address this by fine-tuning pre-trained Image-to-3D (I23D) models on multi-instance datasets, which incurs s...