Mike Kwong’s Post

Manifold Learning, Recently, I have been reading about manifold learning, and I find it interesting that nonlinear manifold methods preserve the local relations between points. This is somewhat similar to using CNNs. Finding an appropriate manifold for data can significantly reduce the computational cost! Thanks to César Sánchez for motivating me to learn about it.

🚀 Exciting Update! 🎓 Just completed an advanced learning algorithm course for machine learning on Coursera! 🌐🧠 Grateful for the incredible insights and knowledge gained. Ready to apply these advanced techniques to real-world projects. Let's continue the journey of learning and innovation together! 💻🔍 I am also thankful to Dr. Jigar Sarda for your guidance and support and to Department of Computer Science and Engineering CSPIT, CHARUSAT UNIVERSITY for providing all facilities. #MachineLearning #Coursera #ContinuousLearning #TechEducation 🚀

CSAFE Learning March Webinar: Algorithmic Assessment of Striation Similarity between Wire Cuts Tuesday, March 19 • 1-2 PM CDT Presenter: Yuhang (Tom) Lin CSAFE graduate student Yuhang (Tom) Lin will present “Algorithmic Assessment of Striation Similarity between Wire Cuts” on March 19 at 1 p.m. CDT. Lin will propose a new, reproducible, automatic algorithm to analyze the similarity between wires, showing how interpreting the results promises a consistent way of using wires as forensic evidence in the future. Enroll for free at https://lnkd.in/dR3TP7in Attendees will have the chance to ask questions during the live presentation. But for those unable to attend, a recording of the webinar will be available on CSAFE Learning for later viewing. Webinar Description: The comparative evaluation of aluminum wire cuts holds considerable significance within the field of forensic science. Nonetheless, there exists an absence of a systematic algorithmic framework for assessing their degree of similarity. In our recently-introduced algorithm, we address this void by undertaking an examination of surface cuts presented in the x3p format. The outcome of this algorithmic procedure consists of cross-correlation values, which serve as measurements for the similarity. In order to enhance the precision of this process, a sequence of multiple procedural steps has been integrated into the algorithmic pipeline. These steps encompass various methodologies, including imputation, Hough transformation, and maximum likelihood estimation.

How Crucial is Attention in Learning Regardless of the specific learning difficulty, attention plays a crucial role in the learning process. Think of it as the engine that drives your brain’s learning machine. It fuels various stages

Join me July 12-13 to discuss how the use of artificial intelligence can enhance learning for exceptional students.

Summary: New research presents a novel clustering-based framework, CluDe, for depth completion in computer vision. The approach focuses on learning pixel-wise and continuous depth representation to address the limitation of existing classification-based methods. CluDe successfully reduces depth smearing around object boundaries and demonstrates state-of-the-art performance on various datasets. Takeaway: The CluDe framework introduces a promising approach to improving depth completion in computer vision by incorporating pixel-wise and continuous depth representation, leading to enhanced performance and reduced depth smearing around object boundaries. Hashtags: #ComputerVision #DepthCompletion #ClusteringFramework #AIResearch #MachineLearning #ArtificialIntelligence #DataScience #TechInnovation

I’ve heard a lot of people say, “school na scam.” It means school is fraud. But is it? Today, I learned about the five steps of machine learning. They include: 1. Define the problem. 2. Build the dataset. 3. Train the model. 4. Evaluate the model. 5. Use the model. Today’s lesson focused more on defining the problem. I learned that you must identify a specific task first, and then determine the most suitable machine learning task to solve the problem. Then, the lesson introduced two types of machine learning tasks; supervised and unsupervised learning. To define supervised and unsupervised learning, I’ll have to introduce some terms like labeled and unlabeled data, #clustering, etc. I want this post to be as short as possible, so I won’t use so much #technical jargons. Moving on, while I was learning about labeled data, I was introduced to categorical and continuous (regression) label. This took me back to one of Engineer Chris’ digital #electronics class, where we had a little debate on what we thought discrete and continuous #data meant. Good memories😌 Anyway, it was easier for me to understand labeled data because of the #knowledge I had gotten from #school. Then it hit me. School may not really prepare you for the real world, but it makes it easier to understand certain real life situations related to your discipline. In fact, one of these machine learning steps involves #statistics. Things like standard deviation, mean, variance, range, and some of all those other stuff I thought wasn’t important in school. Now I get to use them to solve real life problems. And I’m certain it won’t be difficult for me to learn because it’s not foreign to me. In summary, I think I appreciate school more now. I appreciate my degree a little better. Do I think there’s room for improvements with our educational system? Definitely. My point is that it’s not entirely useless. What do you think? #machinelearning #physics #electronics #school #learning

I'm excited to share that I have successfully completed a Post graduate program in Artificial Intelligence and Machine Learning from The University of Texas at Austin Texas McCombs School of Business and Great Lakes executive learning through Great learning. #MachineLearning #ArtificaialIntelligence #TheUniversityofTexas #GreatLakes #GreatLearning #HappyLearning

Happy to share that our paper "Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning" got accepted to ICML2024! (https://lnkd.in/dgh32Jbb) In Multi-Task Learning it is common to first compute the gradient of each task and then aggregate all the gradients via some heuristic for updating the shared parameters. However, by only relying on the gradient values popular methods miss an important aspect, the sensitivity in each of the gradients' dimensions. Some dimensions may be more lenient for changes while others may be more restrictive. Hence, we propose a Bayesian view for this process. We place a probability distribution over the task-specific parameters, which in turn induce a distribution over the gradients of the tasks. As a result, we keep track of both the mean values and the variance (which reflect the sensitivity) in the gradients, allowing us to combine them more effectively! More details in the paper :-) Work done in collaboration with Idit Diamant, PhD, Arnon Netzer, GAL CHECHIK, Ethan Fetaya

Self-supervised learning from hour-long first-person walking tour videos is as effective as pretraining with ImageNet. Intuitively, objects are naturally enlarged, cropped, and rotated in the video. Hence, all self-supervised learning techniques are naturally present. Even better, all changes follow the laws of physics. #artificialintelligence #machinelearning #deeplearning https://lnkd.in/eEC8XHnr