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New insights into training dynamics of deep classifiers

MIT Center for Brains, Minds and Machines researchers provide one of the first theoretical analyses covering optimization, generalization, and approximation in deep networks and offers new insights into the properties that emerge during training. ....

Tomaso Poggio , Akshay Rangamani , Department Of Brain , Mcgovern Institute , Cognitive Sciences , Brown University , Deep Classifiers , Square Loss , Slow Rank , Neural Collapse , Generalization Bounds , Govern Institute , Eugene Mcdermott Professor , Mit Center For Brains Minds And Machines , Tomaso Poggio , Omer Galanti , Akshay Rangamani , Onvolutional Neural Networks Cnns , Eep Neural Networks Dnns , Ully Connected Deep Networks , Deep Classifiers , Stochastic Gradient Descent , Weight Decay Regularization , Eight Normalization , Ow Rank Bias , Ocalized Kernels ,

"Quantification of visual blockage at culverts using deep learning base" by Umair Iqbal, Muhammad Zain Bin Riaz et al.

Blockage of culverts by debris material is reported as main cause of urban flash floods. Extraction of blockage information using intelligent video analytic (IVA) algorithms can prove helpful in making timely maintenance-related decisions toward avoiding flash floods. Having known the percentage of visual blockage at culverts can help better prioritise the maintenance of highly blocked culvert sites. This article proposes a deep learning-based segmentation-classification pipeline where visible culvert openings are segmented at the first stage and classified into one of four percentage visual blockage classes at the second stage. Images of Culverts and Blockage (ICOB) and Visual Hydraulics-Lab Blockage Dataset (VHD) dataset were used to train the deep learning models. From the results, Mask R-CNN (ResNet50 backbone) achieved the best segmentation performance (i.e. mAP@75 of 77.2%), while NASNet achieved the best classification performance (i.e. 81.2% test accuracy). To demonstrate the i ....

Lab Blockage Dataset , Visual Hydraulics Lab Blockage Dataset , Computer Vision , Onvolutional Neural Networks Cnns , Deep Learning Pipeline , Ntelligent Video Analytic Iva , Isual Blockage ,

Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices

An MIT team evaluated memory bottleneck to deploying machine learning on small "internet of things" devices and created a new method and device that outperforms other tinyML methods on image detection and classification tasks. ....

Shan Cai , Wei Ming Chen , Ji Lin , Watson Ai Lab , National Science Foundation , Department Of Electrical Engineering , Woodside Energy , Watson Ai Lab Research Scientist Chuang Gan , Song Han , Electrical Engineering , Computer Science , Neural Information Processing Systems , National Science , Mit Ibm Watson Ai Lab , Nternet Of Things , Edge Devices , Computer Vision , Tiny Machine Learning , Icrocontroller Unit Mcu , Dong Han , Onvolutional Neural Networks Cnns , Ji Lin , Wei Ming Chen , Ryan Cai , Chuang Gan ,