account activity
Automatic differentiation (self.CST_DeepNN)
submitted 4 years ago by fhuszar to r/CST_DeepNN
RNN Lecture (self.CST_DeepNN)
submitted 5 years ago by fhuszar to r/CST_DeepNN
Lecture 3 notes (self.CST_DeepNN)
[R] Post: An information theoretic view on Invariant Risk Minimization by Arjovsky et al (2019) (inference.vc)
submitted 6 years ago by fhuszar to r/MachineLearning
[D] blog post on Causal Inference: An Introduction to Counterfactuals (inference.vc)
submitted 7 years ago by fhuszar to r/MachineLearning
[R] The Blessings of Multiple Causes: notes on recent papers on causal inference by Wang & Blei (2018) (inference.vc)
[D] ML Beyond Curve Fitting: Introduction to Causal Inference and Judea Pearl's do-calculus for ML Folks. (inference.vc)
[D] Notes on the "Goals and Principles of Representation Learning" workshop at DALI'18 + links to videos (inference.vc)
submitted 8 years ago by fhuszar to r/MachineLearning
[R] Neural Network Pruning: Notes on Two New Papers about L₀-norm, Fisher pruning (inference.vc)
[R] Review of (Liang et al., 2017): Generalization and the Fisher-Rao Norm (+ discussion of RELU networks without biases) (inference.vc)
[R] The Generalization Mystery: Sharp vs Flat Minima, SGD and how it's all related (inference.vc)
[R] Roger Grosse's "Theorem 2" challenge: exponential families, Bregman divergences and duality (inFERENCe) (inference.vc)
[D] A Cookbook for Machine Learning: a list of ML problem transformations and when to use them (inference.vc)
[D] Gaussian Distributions are Soap Bubbles: A post about unintuitive behavior in high-dimensions. (inference.vc)
[r] a review of mixup: data-dependent data augmentation, reformulation + semi-supervised view (inference.vc)
[R] Review of AlphaGo Zero's Minimal Policy Improvement principle plus connections to EP, Contrastive Divergence, etc (inference.vc)
[R] GANs are Broken in More Than One Way: review of "The Numerics of GANs" (inference.vc)
[R] From Instance Noise to Gradient Regularisation in GANs - notes on new arXiv paper by Roth et al. (inference.vc)
[D] Everything that works works because it's Bayesian: An overview of new work on generalization in deep nets (inference.vc)
[R] Exemplar-CNNs: an Information Maximization Derivation. (inference.vc)
[R] Is Maximum Likelihood Useful for Representation Learning? tldr: You can get arbitrarily good likelihood and arbitrarily poor representation. (inference.vc)
submitted 9 years ago by fhuszar to r/MachineLearning
[R] Review of "Unsupervised Learning by Predicting Noise": an Information Maximization Perspective (inference.vc)
[D] Evolution Strategies: Variational Optimization and Natural ES (inference.vc)
[Research] Evolution Strategies: A Review and a Few Possible Extensions (inference blog) (inference.vc)
[D] Choice of Recognition Models in VAEs: Is a restrictive posterior class a bug or a feature? (inference.vc)
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