Author: Mohammad Asjad

Mohammad Asjad
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Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

Google DeepMind Researchers Unveil Multistep Consistency Models: A Machine Learning Approach that Balances Speed and Quality in AI Sampling

Diffusion models have gained prominence in image, video, and audio generation, but their sampling process is computationally expensive compared to training. Consistency Models offer...

Training Value Functions via Classification for Scalable Deep Reinforcement Learning: Study by Google DeepMind Researchers and Others

Value functions are a core component of deep reinforcement learning (RL). Value functions, implemented with neural networks, undergo training via mean squared error regression...

This AI Paper from China Introduces ShortGPT: A Novel Artificial Intelligence Approach to Pruning Large Language Models (LLMs) based on Layer Redundancy

Recent advancements in Large Language Models (LLMs) have led to models containing billions or even trillions of parameters, achieving remarkable performance across domains. However,...

Unlocking the Best Tokenization Strategies: How Greedy Inference and SaGe Lead the Way in NLP Models

The inference method is crucial for NLP models in subword tokenization. Methods like BPE, WordPiece, and UnigramLM offer distinct mappings, but their performance differences...

Unlocking the ‘Wisdom of the Silicon Crowd’: How LLM Ensembles Are Redefining Forecasting Accuracy to Match Human Expertise

Large language models (LLMs) trained on vast amounts of text data show remarkable abilities in diverse tasks via next-token prediction and fine-tuning. These tasks...

Microsoft AI Researchers Developed a New Improved Framework ResLoRA for Low-Rank Adaptation (LoRA)

Large language models (LLMs) with hundreds of billions of parameters have significantly improved performance on various tasks. Fine-tuning LLMs on specific datasets enhances performance...

Maximizing Efficiency in AI Training: A Deep Dive into Data Selection Practices and Future Directions

The recent success of large language models relies heavily on extensive text datasets for pre-training. However, indiscriminate use of all available data may not...

Researchers from Tsinghua University and Microsoft AI Unveil a Breakthrough in Language Model Training: The Path to Optimal Learning Efficiency

With the rise of language models, there has been an enormous focus on improving the learning of  LMs to accelerate the learning speed and...

This AI Paper from the University of Michigan and Netflix Proposes CLoVe: A Machine Learning Framework to Improve the Compositionality of Pre-Trained Contrastive Vision-Language...

There has been notable progress in Vision-Language tasks, with models like CLIP showing impressive performance in various tasks. While these models excel at recognizing...

Researchers from Mohamed bin Zayed University of AI Developed ‘PALO’: A Polyglot Large Multimodal Model for 5B People

Large Multimodal Models (LMMs), driven by AI advancements, revolutionize vision and language tasks but are mainly centered on English, neglecting non-English languages. This oversight...

Google AI Proposes USER-LLM: A Novel Artificial Intelligence Framework that Leverages User Embeddings to Contextualize LLMs

Large Language Models (LLMs) have transformed natural language processing, offering user modeling and personalization opportunities. However, effectively integrating user interaction data is challenging. Such...

Brown University Researchers Propose LexC-Gen: A New Artificial Intelligence Method that Generates Low-Resource-Language Classification Task Data at Scale

Data scarcity in low-resource languages can be mitigated using word-to-word translations from high-resource languages. However, bilingual lexicons typically need more overlap with task data,...

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