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.

Alibaba Researchers Introduce Mobile-Agent: An Autonomous Multi-Modal Mobile Device Agent

AI Shorts
Mobile device agents utilizing Multimodal Large Language Models (MLLM) have gained popularity due to the rapid advancements in MLLMs, showcasing notable visual comprehension capabilities....

Researchers from the Chinese University of Hong Kong and Tencent AI Lab Propose a Multimodal Pathway to Improve Transformers with Irrelevant Data from Other...

AI Shorts
Transformers have found widespread application in diverse tasks spanning text classification, map construction, object detection, point cloud analysis, and audio spectrogram recognition. Their versatility...

This AI Paper from China Introduces DREditor: A Time-Efficient AI Approach for Building a Domain-Specific Dense Retrieval Model

AI Shorts
Deploying dense retrieval models is crucial in industries like enterprise search (ES), where a single service supports multiple enterprises. In ES, such as the...

This AI Paper from ETH Zurich, Google, and Max Plank Proposes an Effective AI Strategy to Boost the Performance of Reward Models for RLHF...

AI Shorts
In language model alignment, the effectiveness of reinforcement learning from human feedback (RLHF) hinges on the excellence of the underlying reward model. A pivotal...

Google AI Presents Lumiere: A Space-Time Diffusion Model for Video Generation

AI Shorts
Recent advancements in generative models for text-to-image (T2I) tasks have led to impressive results in producing high-resolution, realistic images from textual prompts. However, extending...

Researchers from ByteDance and Sun Yat-Sen University Introduce DiffusionGPT: LLM-Driven Text-to-Image Generation System

AI Shorts
In image generation, diffusion models have significantly advanced, leading to the widespread availability of top-tier models on open-source platforms. Despite these strides, challenges in...

This AI Paper from Meta and NYU Introduces Self-Rewarding Language Models that are Capable of Self-Alignment via Judging and Training on their Own Generations

AI Shorts
Future models must receive superior feedback for effective training signals to advance the development of superhuman agents. Current methods often derive reward models from...

Researchers from the University of Washington and Allen Institute for AI Present Proxy-Tuning: An Efficient Alternative to Finetuning Large Language Models

AI Shorts
The inherent capabilities of pretrained large language models are notable, yet achieving desired behaviors often requires additional adaptation. When dealing with models whose weights...

This AI Paper Introduces XAI-AGE: A Groundbreaking Deep Neural Network for Biological Age Prediction and Insight into Epigenetic Mechanisms

AI Shorts
Aging involves the gradual accumulation of damage and is an important risk factor for chronic diseases. Epigenetic mechanisms, particularly DNA methylation, play a role...

Stanford Researchers Introduce Clover: Closed-Loop Verifiable Code Generation that Checks Consistencies Among Code, Doc Strings and Annotations and Enforces Correctness in AI-Generated Code

AI Shorts
The trend of employing large language models (LLMs) for code generation is rapidly gaining momentum in software development. However, the lack of robust mechanisms...

This AI Paper from China Unveils ‘Activation Beacon’: A Groundbreaking AI Technique to Expand Context Understanding in Large Language Models

AI Shorts
Large language models (LLMs) face a hurdle in handling long contexts due to their constrained window length. Although the context window length can be...

AWS Researchers Propose Panda: A New Machine Learning Framework to Provide Context Grounding to Pre-Trained LLMs

Applications
Debugging performance issues in databases is challenging, and there is a need for a tool that can provide useful and in-context troubleshooting recommendations. Large...
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