Research & Publications

Exploring the frontiers of Deep Learning and Computer Vision

Tiny Neurons Research Group

I'm proud to be part of this innovative research team focused on advancing AI capabilities

About Our Research

Tiny Neurons Research Group is at the forefront of artificial intelligence research, specializing in Deep Learning, Computer Vision, and Agentic AI systems. Our work focuses on developing novel architectures and algorithms that push the boundaries of what's possible in machine perception and reasoning.

Primary Research Areas:

  • Deep Neural Network Architectures
  • Computer Vision & Image Processing
  • Agentic AI & Autonomous Systems
  • Explainable AI (XAI)
  • Multi-modal Learning
15+
Research Papers
5
Active Projects
3
Research Grants
10+
Team Members

Published Papers

Advancements in Neural Architecture Search for Computer Vision

2023
Your Name, Co-author One, Co-author Two
International Conference on Computer Vision (ICCV)

This paper introduces a novel neural architecture search framework that significantly reduces computational requirements while maintaining state-of-the-art performance on benchmark datasets. Our method achieves 15% faster convergence with comparable accuracy to existing approaches.

PDF Code Presentation
Computer Vision NAS Deep Learning

Multi-Agent Reinforcement Learning for Autonomous Systems

2022
Co-author One, Your Name, Co-author Three
Journal of Artificial Intelligence Research (JAIR)

We present a novel multi-agent reinforcement learning framework that enables autonomous systems to collaborate effectively in dynamic environments. Our approach demonstrates improved coordination and task completion rates in simulated environments, paving the way for real-world applications.

PDF Code Results
Reinforcement Learning Multi-Agent Systems Autonomous AI

Current Research Projects

Agentic AI Systems

Developing autonomous AI agents capable of complex reasoning, planning, and decision-making in dynamic environments. Our research focuses on creating systems that can understand context, set goals, and execute multi-step tasks with minimal human intervention.

Key Focus Areas:

  • Autonomous goal-setting and planning
  • Multi-modal perception and reasoning
  • Human-AI collaboration frameworks
  • Ethical decision-making in autonomous systems

Advanced Computer Vision

Pushing the boundaries of visual understanding through novel deep learning architectures and training methodologies. Our work spans object detection, semantic segmentation, and generative models for various applications.

Current Investigations:

  • Few-shot learning for visual recognition
  • 3D scene understanding from 2D images
  • Adversarial robustness in vision systems
  • Cross-modal visual-language models

Deep Learning Architectures

Exploring novel neural network architectures and training paradigms to improve efficiency, interpretability, and performance across various domains. Our research addresses fundamental challenges in deep learning theory and practice.

Research Directions:

  • Efficient model compression techniques
  • Explainable AI for deep networks
  • Meta-learning and few-shot adaptation
  • Neuromorphic computing approaches

Research Journey

2023

Paper Published - ICCV

Published groundbreaking research on Neural Architecture Search at premier computer vision conference

2022

Journal Publication - JAIR

Contributed to multi-agent reinforcement learning research published in top AI journal

2021

Joined Tiny Neurons

Became research member at Tiny Neurons Research Group, focusing on DL and CV

2020

Research Beginnings

Started undergraduate research in machine learning and computer vision