Category: Uncategorized

Scaling state-of-the-art models for real-world deployment often requires training different model sizes to adapt to various computing environments. However, training multiple versions independently is computationally expensive and leads to inefficiencies in deployment when intermediate-sized models are optimal. Current solutions like model compression and distillation have limitations, often requiring additional data and retraining, which may degrade… Read more

Large Language Models (LLMs) have gained significant attention for their versatility in various tasks, from natural language processing to complex reasoning. A promising application of these models is the development of autonomous multi-agent systems (MAS), which aim to utilize the collective intelligence of multiple LLM-based agents for collaborative problem-solving. However, LLM-based MAS faces two critical… Read more

Retrieval-augmented generation (RAG) is a method that integrates external knowledge sources into large language models (LLMs) to provide accurate and contextually relevant responses. These systems enhance the ability of LLMs to offer detailed and specific answers to user queries by utilizing up-to-date information from various domains. The field is particularly important in applications such as… Read more

Ego-centric searches are essential in many applications, from financial fraud detection to social network research, because they concentrate on a single vertex and its immediate neighbors. These queries offer insights into direct connections by analyzing interconnections around a key node. Enabling such searches without jeopardizing privacy becomes a major difficulty when graphs are dispersed over… Read more

To bring the vision of robot manipulators assisting with everyday activities in cluttered environments like living rooms, offices, and kitchens closer to reality, it’s essential to create robot policies that can generalize to new tasks in unfamiliar settings. In a new paper Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation, a research… Read more

In the ever-evolving world of artificial intelligence (AI), large language models have proven instrumental in addressing a wide array of challenges, from automating complex tasks to enhancing decision-making processes. However, scaling these models has also introduced considerable complexities, such as high computational costs, reduced accessibility, and the environmental impact of extensive resource requirements. The enormous… Read more

Parameter-efficient fine-tuning (PEFT) methods, like low-rank adaptation (LoRA), allow large pre-trained foundation models to be adapted to downstream tasks using a small percentage (0.1%-10%) of the original trainable weights. A less explored area of PEFT is extending the pre-training phase without supervised labels—specifically, adapting foundation models to new domains using efficient self-supervised pre-training. While traditional… Read more

A simple letter counting experiment exposes a fundamental limitation of LLMs ChatGPT and Claude, proving they can’t yet “think” like humans.Read More Read more

Machine Learning (ML) models have shown promising results in various coding tasks, but there remains a gap in effectively benchmarking AI agents’ capabilities in ML engineering. Existing coding benchmarks primarily evaluate isolated coding skills without holistically measuring the ability to perform complex ML tasks, such as data preparation, model training, and debugging. OpenAI Researchers Introduce… Read more

Feature selection and engineering are critical steps that can significantly impact your model’s performance. Read more