Author: Mohammad Arshad

Mohammad Arshad
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Arshad is an intern at MarktechPost. He is currently pursuing his Int. MSc Physics from the Indian Institute of Technology Kharagpur. Understanding things to the fundamental level leads to new discoveries which lead to advancement in technology. He is passionate about understanding the nature fundamentally with the help of tools like mathematical models, ML models and AI.

This AI Paper Introduces the Diffusion World Model (DWM): A General Framework for Leveraging Diffusion Models as World Models in the Context of Offline...

Reinforcement learning (RL) comprises a wide range of algorithms, typically divided into two main groups: model-based (MB) and model-free (MF) methods. MB algorithms rely...

Revolutionizing Language Model Safety: How Reverse Language Models Combat Toxic Outputs

Language models (LMs) exhibit problematic behaviors under certain conditions: chat models can produce toxic responses when presented with adversarial examples, LMs prompted to challenge...

Meet Hawkeye: A Unified Deep Learning-based Fine-Grained Image Recognition Toolbox Built on PyTorch

In recent years, notable advancements in the design and training of deep learning models have led to significant improvements in image recognition performance, particularly...

Researchers from ETH Zurich and Microsoft Introduce EgoGen: A New Synthetic Data Generator that can Produce Accurate and Rich Ground-Truth Training Data for EgoCentric...

Understanding the world from a first-person perspective is essential in Augmented Reality (AR), as it introduces unique challenges and significant visual transformations compared to...

This AI Paper from China Introduces SegMamba: A Novel 3D Medical Image Segmentation Mamba Model Designed to Effectively Capture Long-Range Dependencies within Whole Volume...

Enhancing the receptive field of models is crucial for effective 3D medical image segmentation. Traditional convolutional neural networks (CNNs) often struggle to capture global...

This AI Paper from China Unveils ‘Vary-toy’: A Groundbreaking Compact Large Vision Language Model for Standard GPUs with Advanced Vision Vocabulary

In the past year, large vision language models (LVLMs) have become a prominent focus in artificial intelligence research. When prompted differently, these models show...

Researchers from KAIST and the University of Washington have introduced ‘LANGBRIDGE’: A Zero-Shot AI Approach to Adapt Language Models for Multilingual Reasoning Tasks without...

Language models (LMs) often struggle with reasoning tasks like math or coding, particularly in low-resource languages. This challenge arises because LMs are primarily trained...

Meet MMToM-QA: A Multimodal Theory of Mind Question Answering Benchmark

Understanding the Theory of Mind (ToM), the ability to grasp the thoughts and intentions of others, is crucial for developing machines with human-like social...

UCLA Researchers Introduce Group Preference Optimization (GPO): A Machine Learning-based Alignment Framework that Steers Language Models to Preferences of Individual Groups in a Few-Shot...

Large Language Models (LLMs) are increasingly employed for various domains, with use cases including creative writing, chatbots, and semantic search. Many of these applications...

Enhancing Graph Data Embeddings with Machine Learning: The Deep Manifold Graph Auto-Encoder (DMVGAE/DMGAE) Approach

Manifold learning, rooted in the manifold assumption, reveals low-dimensional structures within input data, positing that the data exists on a low-dimensional manifold within a...

Researchers from the University of Wisconsin-Madison Challenge the Efficacy of Score-based Generative Models: A Surprising Revelation of Gaussian Mimicry in High-Quality Data Generation

Score-based Generative Models (SGMs) are a prominent approach in generative modeling, celebrated for their capacity to produce high-quality samples from intricate, high-dimensional data distributions....

This AI Paper Introduces the Open-Vocabulary SAM: A SAM-Inspired Model Designed for Simultaneous Interactive Segmentation and Recognition

Combining CLIP and the Segment Anything Model (SAM) is a groundbreaking Vision Foundation Models (VFMs) approach. SAM performs superior segmentation tasks across diverse domains,...

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