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Researchers at the University of Maryland Propose a Unified Machine Learning Framework for Continual Learning (CL)
Continual Learning (CL) is a method that focuses on gaining knowledge from dynamically changing data distributions. This technique mimics real-world scenarios and helps improve the performance of a model as it encounters new data while retaining previous information....
This AI Paper Explores the Impact of Model Compression on Subgroup Robustness in BERT Language Models
The significant computational demands of large language models (LLMs) have hindered their adoption across various sectors. This hindrance has shifted attention towards compression techniques designed to reduce the model size and computational needs without major...
OpenAI Enhances Language Models with Fill-in-the-Middle Training: A Path to Advanced Infilling Capabilities
Transformer-based language models, like BERT and T5, are adept at various tasks but struggle with infilling—generating text within a specific location while considering both preceding and succeeding contexts. Though encoder-decoder models can handle suffixes, their...
Do LLM Agents Have Regret? This Machine Learning Research from MIT and the University of Maryland Presents a Case Study on Online Learning and Games
Large Language Models (LLMs) have been increasingly employed for (interactive) decision-making through the model development of LLM-based agents. LLMs have shown remarkable successes in embodied AI, natural science, and social science applications in recent years....
AI21 Labs Breaks New Ground with ‘Jamba’: The Pioneering Hybrid SSM-Transformer Large Language Model
In an era where the demand for smarter, faster, and more efficient artificial intelligence (AI) solutions is continuously on the rise, AI21 Labs’ unveiling of Jamba marks a significant leap forward. Jamba, a pioneering SSM-Transformer model, heralds a new chapter in...
AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks
Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data's patterns (prior probability),...
