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MarkTechPost
29  mars     05h00
Efficiency Breakthroughs in LLMs: Combining Quantization, LoRA, and Pruning for Scaled-down Inference and Pre-training
Sana Hassan    In recent years, LLMs have transitioned from research tools to practical applications, largely due to their increased scale during training. However, as most of their computational resources are consumed during inference, efficient pretraining and inference are crucial. Post training techniques...
    03h00
FedFixer: A Machine Learning Algorithm with the Dual Model Structure to Mitigate the Impact of Heterogeneous Noisy Label Samples in Federated Learning
Vibhanshu Patidar    In today’s world, where data is distributed across various locations and privacy is paramount, Federated Learning FL has emerged as a game changing solution. It enables multiple parties to train machine learning models collaboratively without sharing their data, ensuring that sensitive...
    01h00
Researchers at the University of Maryland Propose a Unified Machine Learning Framework for Continual Learning (CL)
Asif Razzaq    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. However, CL faces a challenge...
28  mars     23h00
This AI Paper Explores the Impact of Model Compression on Subgroup Robustness in BERT Language Models
Nikhil    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 performance trade offs. This pivot...
    22h00
OpenAI Enhances Language Models with Fill-in-the-Middle Training: A Path to Advanced Infilling Capabilities
Sana Hassan    Transformer based language models, like BERT and T , 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 training data typically includes...
    21h00
AI21 Labs Breaks New Ground with Jamba’: The Pioneering Hybrid SSM-Transformer Large Language Model
Asif Razzaq    In an era where the demand for smarter, faster, and more efficient artificial intelligence AI solutions is continuously on the rise, AI Labs’ unveiling of Jamba marks a significant leap forward. Jamba, a pioneering SSM Transformer model, heralds a new chapter in AI technology by melding the...
    21h00
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
Mohammad Asjad    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. LLMs have also exhibited...
    11h00
Researchers at Rutgers University Propose AIOS: An LLM Agent Operating System that Embeds Large Language Model into Operating Systems (OS) as the Brain of the OS
Adnan Hassan    Artificial intelligence AI has introduced a dynamic shift in various sectors, most notably by deploying autonomous agents capable of independent operation and decision making. These agents, powered by large language models LLMs , have significantly broadened the scope of tasks that can be...
    10h00
Hugging Face Introduces Cosmopedia To Create Large-Scale Synthetic Data For Pre-Training
Dhanshree Shripad Shenwai    Hiring human annotators was a time consuming and expensive technique traditionally used to create datasets for supervised fine tuning and instruction tuning. Due to the high cost, only a select few influential people in the area were able to create such comprehensive datasets. Nevertheless, things...
    09h00
Recall to Imagine (R2I): A New Machine Learning Approach that Enhances Long-Term Memory by Incorporating State Space Models into Model-based Reinforcement Learning (MBRL)
Tanya Malhotra    With the recent advancements in the field of Machine Learning ML , Reinforcement Learning RL , which is one of its branches, has become significantly popular. In RL, an agent picks up skills to interact with its surroundings by acting in a way that maximizes the sum of its rewards. The...