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.

Neural Network Diffusion: Generating High-Performing Neural Network Parameters

Despite the great success of diffusion models in visual generation, their potential in other domains still needs to be explored. Existing research methodologies have...

Microsoft Present AI Controller Interface: Generative AI with a Lightweight, LLM-Integrated Virtual Machine (VM)

The rise of Large Language Models (LLMs) has transformed text creation and computing interactions. These models' lack of ensuring content accuracy and adherence to...

Can We Drastically Reduce AI Training Costs? This AI Paper from MIT, Princeton, and Together AI Unveils How BitDelta Achieves Groundbreaking Efficiency in Machine...

Training Large Language Models (LLMs) involves two main phases: pre-training on extensive datasets and fine-tuning for specific tasks. While pre-training requires significant computational resources,...

Can Machine Learning Teach Robots to Understand Us Better? This Microsoft Research Introduces Language Feedback Models for Advanced Imitation Learning

The challenges in developing instruction-following agents in grounded environments include sample efficiency and generalizability. These agents must learn effectively from a few demonstrations while...

This Machine Learning Research from Yale and Google AI Introduce SubGen: An Efficient Key-Value Cache Compression Algorithm via Stream Clustering

Large language models (LLMs) face challenges in generating long-context tokens due to high memory requirements for storing all previous tokens in the attention module....

Apple Researchers Introduce Keyframer: An LLM-Powered Animation Prototyping Tool that can Generate Animations from Static Images (SVGs)

Large language models (LLMs) promise to revolutionize various creative fields, including animation, but face challenges in effectively interpreting natural language descriptions of motion. Recent...

Meet BiLLM: A Novel Post-Training Binary Quantization Method Specifically Tailored for Compressing Pre-Trained LLMs

Pretrained large language models (LLMs) boast remarkable language processing abilities but require substantial computational resources. Binarization, which reduces model weights to a single bit,...

This AI Paper Proposes an Interactive Agent Foundation Model that Uses a Novel Multi-Task Agent Training Paradigm for Training AI Agents Across a Wide...

AI development is shifting from static, task-centric models to dynamic, adaptable agent-based systems suitable for various applications. AI systems aim to gather sensory data...

Meet EscherNet: A Multi-View Conditioned Diffusion Model for View Synthesis

View synthesis, integral to computer vision and graphics, enables scene re-rendering from diverse perspectives akin to human vision. It aids in tasks like object...

Can Large Language Models be Trusted for Evaluation? Meet SCALEEVAL: An Agent-Debate-Assisted Meta-Evaluation Framework that Leverages the Capabilities of Multiple Communicative LLM Agents

Despite the utility of large language models (LLMs) across various tasks and scenarios, researchers need help to evaluate LLMs properly in different situations. They...

Stanford Researchers Introduce RAPTOR: A Novel Tree-based Retrieval System that Augments the Parametric Knowledge of LLMs with Contextual Information

Retrieval-augmented language models often retrieve only short chunks from a corpus, limiting overall document context. This decreases their ability to adapt to changes in...

Researchers from McGill University Present the Pythia 70M Model for Distilling Transformers into Long Convolution Models

The emergence of Large Language Models (LLMs) has transformed the landscape of natural language processing (NLP). The introduction of the transformer architecture marked a...