🎉 Thrilled to announce that our paper "MIND over Body: Adaptive Thinking using Dynamic Computation" has been selected for an *oral presentation* (top 1.8%) at ICLR 2025! 🚀 🔍 What’s the paper about? We introduce the Model INtrospection for a Dynamically adaptive model (MIND) , a groundbreaking adaptive approach that emulates the human brain's efficiency by dynamically adjusting computation based on input complexity. This self-introspective network achieves remarkable results across tasks, including:  1. 96.62% accuracy on ImageNet with a lightweight three-layer model, outperforming larger architectures like ResNet-50 and EfficientNet.  2. 95.8% / 88.7% F1 scores on SQuAD v1.1/v2.0 datasets with minimal parameter costs.  3. Introducing MIND-Transformer. Our work paves the way for intelligent systems that optimize resource allocation based on task complexity, contributing to advancements in both language modeling and computer vision domains. 📅 Presentation Details: Catch us at ICLR 2025 to dive deeper into how MIND is reshaping dynamic computation in deep learning! A huge thank you to my co-authors, including my brilliant advisor Dr. @PlisSergey , whose expertise and insights has been instrumental in shaping this work!! 💡 Explore the paper here: lnkd.in/g4cbebeK Looking forward to engaging discussions with the community at ICLR 2025 in Singapore! 🇸🇬 #ICLR2025 #DeepLearning #AI #DynamicComputation #Neuroscience #Interpretability #MachineLearning

Do You Know About PyTorch? PyTorch, an open-source deep learning framework, has taken the AI world by storm, and here's why you should be excited about it: - Dynamic Computational Graph: PyTorch uses a dynamic computation graph, which means it can change during runtime. This flexibility is a game-changer for researchers and developers, making it easier to experiment and debug. - Tensor Support: PyTorch's tensor library is versatile and powerful, making it ideal for various machine learning and deep learning tasks. From image recognition to natural language processing, PyTorch has you covered. - Modular and Extensible: PyTorch's modular design allows you to build your neural networks with ease. You can create custom layers, loss functions, and optimization algorithms to suit your specific needs. - Pythonic Interface: If you're a Python lover, PyTorch is a natural choice. Its Pythonic API makes it accessible and user-friendly for Python developers, reducing the learning curve. - Rich Ecosystem: With a growing library of pre-trained models, a supportive community, and numerous extensions like fastai, PyTorch offers a rich ecosystem that accelerates your AI projects. - Strong Adoption: PyTorch has gained popularity across academia and industry. It's the preferred framework for many cutting-edge research projects and applications. - Research to Production: PyTorch seamlessly supports the transition from research to production, thanks to its flexibility and integration with deployment tools. - Open Source: Being open source, PyTorch encourages collaboration and innovation. You can find tutorials, courses, and libraries developed by the PyTorch community. - Continuous Evolution: PyTorch is continually evolving, with regular updates and improvements, ensuring it stays at the forefront of AI and deep learning. So, whether you're a researcher pushing the boundaries of AI or a developer building real-world applications, PyTorch offers the tools and support you need to turn your ideas into reality. Dive into the world of deep learning with PyTorch, and let your imagination run wild! #PyTorch #DeepLearning #AIInnovation #OpenSource #MachineLearning #DeepLearning #NeuralNetworks #PythonCoding #DataScience #NLP #ComputerVision #ONNX #Research #AICommunity #Innovation #TensorFlowComparison #PyTorchTutorials #AIResearch #DynamicComputation

AdaTape: Google AI's Breakthrough in Dynamic Computation for Neural Networks #Adaptability #adaptivetapereading #AdaTape #AI #AItechnology #artificialintelligence #balancebetweenqualityandcost #dynamiccomputation #feedforwardnetworks #GoogleAI multiplatform.ai/adatape-goo…

🤯 Unlocking the power of #SelfSupervisedLearning & #DynamicComputation? Tarun Krishna et al. have it figured out! Check out their new paper: deepai.org/publication/unify… #Epoch