Karan UppalPaper Summary: NPMs: Neural Parametric Models for 3D Deformable ShapesPalafox, Pablo, et al. “Npms: Neural parametric models for 3d deformable shapes.” Proceedings of the IEEE/CVF International Conference on…8 min read·Sep 14, 2023----
Karan UppalPaper Summary: DeepSDF: Learning Continuous Signed Distance Functions for Shape RepresentationPark, Jeong Joon, et al. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.8 min read·Jul 27, 2023----
Karan UppalPaper Summary: DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionDonahue, Jeff, et al. International conference on machine learning. PMLR, 2014.7 min read·Jun 26, 2023----
Karan UppalPaper Summary: Dropout: A Simple Way to Prevent Neural Networks from OverfittingSrivastava, Nitish, et al. The Journal of Machine Learning Research (2014)7 min read·May 30, 2023----
Karan UppalPaper Summary: Maxout NetworksGoodfellow, Ian, et al. “Maxout networks.” International conference on machine learning. PMLR, 2013.6 min read·Jan 12, 2023--1--1
Karan UppalPaper Summary: Adam: A Method for Stochastic OptimizationKingma, Diederik P., and Jimmy Ba. arXiv preprint arXiv:1412.6980 (2014)7 min read·Aug 4, 2022----
Karan UppalPaper Summary: On the importance of initialization and momentum in deep learningSutskever, Ilya, et al. International conference on machine learning. PMLR, 20137 min read·Jul 28, 2022----
Karan UppalPaper Summary: Understanding the difficulty of training deep feedforward neural networksGlorot, Xavier, and Yoshua Bengio. JMLR Workshop and Conference Proceedings, 20106 min read·Jul 19, 2022----
Karan UppalPaper Summary: ImageNet Classification with Deep Convolutional Neural NetworksAlex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. NIPS 2012.6 min read·May 21, 2021--1--1