Machine Learning – Page 13

An Implementation on Building Advanced Multi-Endpoint Machine Learning APIs with LitServe: Batching, Streaming, Caching, and Local Inference

In this tutorial, we explore LitServe, a lightweight and powerful serving framework that allows us to deploy machine learning models […]

Google AI Introduces FLAME Approach: A One-Step Active Learning that Selects the Most Informative Samples for Training and Makes a Model Specialization Super Fast

Open vocabulary object detectors answer text queries with boxes. In remote sensing, zero shot performance drops because classes are fine […]

Weak-for-Strong (W4S): A Novel Reinforcement Learning Algorithm that Trains a weak Meta Agent to Design Agentic Workflows with Stronger LLMs

Researchers from Stanford, EPFL, and UNC introduce Weak-for-Strong Harnessing, W4S, a new Reinforcement Learning RL framework that trains a small […]

Microsoft AI Proposes BitNet Distillation (BitDistill): A Lightweight Pipeline that Delivers up to 10x Memory Savings and about 2.65x CPU Speedup

Microsoft Research proposes BitNet Distillation, a pipeline that converts existing full precision LLMs into 1.58 bit BitNet students for specific […]

Baidu’s PaddlePaddle Team Releases PaddleOCR-VL (0.9B): a NaViT-style + ERNIE-4.5-0.3B VLM Targeting End-to-End Multilingual Document Parsing

How do you convert complex, multilingual documents—dense layouts, small scripts, formulas, charts, and handwriting—into faithful structured Markdown/JSON with state-of-the-art accuracy […]

Google AI Releases C2S-Scale 27B Model that Translate Complex Single-Cell Gene Expression Data into ‘cell sentences’ that LLMs can Understand

A team of researchers from Google Research, Google DeepMind, and Yale released C2S-Scale 27B, a 27-billion-parameter foundation model for single-cell […]

Category: Machine Learning

An Implementation on Building Advanced Multi-Endpoint Machine Learning APIs with LitServe: Batching, Streaming, Caching, and Local Inference

Google AI Introduces FLAME Approach: A One-Step Active Learning that Selects the Most Informative Samples for Training and Makes a Model Specialization Super Fast

Weak-for-Strong (W4S): A Novel Reinforcement Learning Algorithm that Trains a weak Meta Agent to Design Agentic Workflows with Stronger LLMs

Microsoft AI Proposes BitNet Distillation (BitDistill): A Lightweight Pipeline that Delivers up to 10x Memory Savings and about 2.65x CPU Speedup

Baidu’s PaddlePaddle Team Releases PaddleOCR-VL (0.9B): a NaViT-style + ERNIE-4.5-0.3B VLM Targeting End-to-End Multilingual Document Parsing

Google AI Releases C2S-Scale 27B Model that Translate Complex Single-Cell Gene Expression Data into ‘cell sentences’ that LLMs can Understand

Ars Live recap: Is the AI bubble about to pop? Ed Zitron weighs in.

QeRL: NVFP4-Quantized Reinforcement Learning (RL) Brings 32B LLM Training to a Single H100—While Improving Exploration

Anthropic’s Claude Haiku 4.5 matches May’s frontier model at fraction of cost

ChatGPT erotica coming soon with age verification, CEO says