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.

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...

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

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...

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

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...

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

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...