CBIRT, Centre of Bioinformatics Research & Tech’s Post

Georgia Institute of Technology Unveils LARS-VSA: Revolutionizing Abstract Rule Learning with Vector Symbolic Architecture In today’s era of rapid technological advancement, analogical reasoning stands as a pillar of human abstraction and innovative ideation. Understanding the intricate relationships between objects fuels creative thinking and problem-solving, a skill set that sets humans apart from machines. However, bridging this cognitive gap has been a challenge for contemporary connectionist models like deep neural networks (DNNs), which excel in semantic and procedural knowledge acquisition but often struggle with abstract reasoning. https://is.gd/hvl32z #AI #AItechnology #artificialintelligence #GeorgiaTech #LARSVSA #llm #machinelearning

Pipeline Forward-Forward Algorithm (PFF): A Cutting-Edge Approach to Training Distributed Neural Networks using Forward-Forward Algorithm #AI #AItechnology #artificialintelligence #communicationoverhead #computationalunits #Deepneuralnetworks #distributedsystems #Flower #ForwardForwardtechnique #GPipe #gradientcomputation #llm #machinelearning #neuralnetworktraining #parallelization #PFF #PipeDream #PipelineForwardForwardAlgorithm #SabanciUniversity #synchronization #Traditionalbackpropagation

I built a Simple Image classifier using #transferlearning in #CNN 🚀 ✔️Convolutional Neural Networks (CNNs) process and analyze visual imagery by breaking down image data and learning from it, which makes them great for tasks like image recognition, classification, tracking. Link in the comments Credits : iNeuron.ai #AICommunication #NeuralNetworks #CreativeAI #ConvolutionalNeuralNetworks #CNN #ComputerVision #DeepLearning #ImageRecognition #TechExplained #AIForAll

Handwriting synthesis using Recurrent Neural Networks (RNNs) is a common approach to generating realistic and coherent handwritten text. RNNs are well-suited for sequential data generation tasks, making them suitable for modeling the temporal nature of handwriting strokes. To explore the kit, check the link in the bio or copy-paste this link into your browser. https://lnkd.in/d67SWiJg #OpenWeaver #kandi #DevelopApplicationsFaster #ReinventDigitalRealization . . . . . . . . . . . #HandwritingSynthesis #HandwritingGeneration #HandwritingAI #RecurrentNeuralNetworks #RNNs #NeuralNetworks #DeepLearning #NaturalLanguageProcessing #AIResearch #TextGeneration #PatternRecognition #MachineLearning #ArtificialIntelligence #HandwritingModel #SequenceModeling #AIInnovation #AIProgramming #AIAlgorithms #AIApplications #codingcommunity #devcommunity #programmingcommunity

Machine learning models trained on datasets with texture bias tend to have a poor performance on out-of-distribution samples. To overcome this problem, a research team led by Prof. Sang Hyun Park from #DGIST has recently proposed a novel framework that generates additional training images from the source image content and the target image texture by leveraging image translation, in a Neural Networks study. This technology significantly outperforms state-of-the-art methods for training various classification and segmentation models. Read more, here: https://ow.ly/gs2t50Q0rIn #DGIST #ScienceandTechnology #University #debiasing #SelfSimilarity #TextureCoOccurrence #UnsupervisedDomainAdaptation #UnpairedImageTranslation #MachineLearning #classification #segmentation #TrainingDatasets

Our Practical DL for Coders Part 2 Study Group starts in an hour! ⏲️ In today's session, we’ll explore mixed precision training, style transfer using neural networks, neural cellular automata, and more! Don’t miss it! Join us and grab a friend with you! #deeplearning #twimlai #stablediffusion #ai #ml #studygroup #fastai #pytorch #machinelearning #course #neuralnetworks #embeddings #deeplearningai #coders

Generalization of Gradient Descent in Over-Parameterized ReLU Networks: Insights from Minima Stability and Large Learning Rates Gradient descent-trained neural networks operate effectively even in overparameterized settings with random weight initialization, often finding global optimum solutions despite the non-convex nature of the problem. These solutions, achieving zero t... https://lnkd.in/e3uwfQ-h #AI #ML #Automation

📅 Day 2 Update: Today, I revised the basic concepts I already knew, including: 💡Vectorization 💡Classification 💡Logistic Regression 💡Overfitting 💡Regularization 💡Basics of Neural Networks I also implemented code for these concepts and will soon be sharing all the code and notes so everyone can refer to them. #DataScience #30DayChallenge #MachineLearning #MathForDataScience #Consistency #ContinuousLearning

In part 3 of my Series of tutorials on Generative AI development, the main topics will be principles of Recurrent Neurons. Understanding these principles in detail is a prerequisite to understanding the Transformers which are one of the main parts of Generative AI Neural Network Architectures. Stay tuned! The tutorial will be out this week. #ai #generativeai #chatgpt #research #machinelearning #education #tutorial #neuralnetworks

Segment scene in new domain with one image using adversarial refinement with SITTA-SEG Single Image Test-Time Adaptation for Segmentation arXiv paper abstract https://lnkd.in/epqJqiBW arXiv PDF paper https://lnkd.in/edskyjic Project page https://lnkd.in/eYC392jy Test-Time Adaptation (TTA) methods improve the robustness of deep neural networks to domain shift on a variety of tasks such as image classification or segmentation. This work explores adapting segmentation models to a single unlabelled image with no other data available at test-time. In particular, this work focuses on adaptation by optimizing self-supervised losses at test-time. Multiple baselines based on different principles are evaluated under diverse conditions and a novel adversarial training is introduced for adaptation with mask refinement. ... additions to the baselines result in a 3.51 and 3.28 % increase over non-adapted baselines, without these improvements, the increase would be 1.7 and 2.16 % only. Please like and share this post if you enjoyed it using the buttons at the bottom! Stay up to date. Subscribe to my posts https://lnkd.in/emCkRuA Web site with my other posts by category https://lnkd.in/enY7VpM LinkedIn https://lnkd.in/ehrfPYQ6 #ComputerVision #Segmentation #AINewsClips #AI #ML #ArtificialIntelligence #MachineLearning