Open-vocabulary object detection (OVD) aims to detect arbitrary objects with user-provided text labels. Although recent progress has enhanced zero-shot detection […]
Category: Applications
Vintix: Scaling In-Context Reinforcement Learning for Generalist AI Agents
Developing AI systems that learn from their surroundings during execution involves creating models that adapt dynamically based on new information. […]
Zyphra Introduces the Beta Release of Zonos: A Highly Expressive TTS Model with High Fidelity Voice Cloning
Text-to-speech (TTS) technology has made significant strides in recent years, but challenges remain in creating natural, expressive, and high-fidelity speech […]
Efficient Alignment of Large Language Models Using Token-Level Reward Guidance with GenARM
Large language models (LLMs) must align with human preferences like helpfulness and harmlessness, but traditional alignment methods require costly retraining […]
Tutorial to Fine-Tuning Mistral 7B with QLoRA Using Axolotl for Efficient LLM Training
In this tutorial, we demonstrate the workflow for fine-tuning Mistral 7B using QLoRA with Axolotl, showing how to manage limited […]
Adaptive Inference Budget Management in Large Language Models through Constrained Policy Optimization
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly in mathematical problem-solving and coding applications. Research […]
This AI Paper Introduces MaAS (Multi-agent Architecture Search): A New Machine Learning Framework that Optimizes Multi-Agent Systems
Large language models (LLMs) are the foundation for multi-agent systems, allowing multiple AI agents to collaborate, communicate, and solve problems. […]
Meta AI Introduces Brain2Qwerty: A New Deep Learning Model for Decoding Sentences from Brain Activity with EEG or MEG while Participants Typed Briefly Memorized Sentences on a QWERTY Keyboard
Brain-computer interfaces (BCIs) have seen significant progress in recent years, offering communication solutions for individuals with speech or motor impairments. […]
BARE: A Synthetic Data Generation AI Method that Combines the Diversity of Base Models with the Quality of Instruct-Tuned Models
As the need for high-quality training data grows, synthetic data generation has become essential for improving LLM performance. Instruction-tuned models […]
Microsoft AI Researchers Release LLaVA-Rad: A Lightweight Open-Source Foundation Model for Advanced Clinical Radiology Report Generation
Large foundation models have demonstrated remarkable potential in biomedical applications, offering promising results on various benchmarks and enabling rapid adaptation […]