Posts
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Deep Residual Networks for Gravitational Wave Detection
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Towards End-to-end Unsupervised Speech Recognition
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An introduction to normalising flows and likelihood-free inference
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Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
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Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
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Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
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Towards a General Purpose CNN for Long Range Dependencies in ND
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GATSBI: Generative Adversarial Training for Simulation-Based Inference
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Denoising Diffusion Probabilistic Models
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Latent distributions in normalisig flows
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Grokking & Deep Double Descent
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Improving Generalization Performance by Switching from Adam to SGD
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Stochastic Normalizing Flows
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A ConvNet for the 2020s
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Attention Is All You Need
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Resampling Base Distributions of Normalizing Flows
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On the Spectral Bias of Neural Networks
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An introduction to Graph Neural Networks
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A introduction to GANs for image generation
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Deep Learning for Video Game Playing
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CKConv: Continuous Kernel Convolution For Sequential Data
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Train longer, generalize better: closing the generalization gap in large batch training of neural networks
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Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
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Normalizing Flows: An Introduction and Review of Current Methods - Part 2
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Normalizing Flows: An Introduction and Review of Current Methods - Part 1
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AlphaFold: Improved protein structure prediction using potentials from deep learning
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A practical introduction to normalising flows
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A practical introduction to variational inference with Variational Autoencoders
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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
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Photometric Classification of Supernovae
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A simple introduction to implementing a neural network in PyTorch
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PyTorch vs Tensorflow, which should I use?
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
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Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
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Wave Physics as an Analog Recurrent Neural Network
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An introduction to neural networks for classification and regression
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Density estimation with Real NVP
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Discovering physical concepts with neural networks (II)
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An introduction to quantum machine learning
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GANSynth: Adversarial Neural Audio Synthesis
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A Docker Tutorial
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Discovering physical concepts with neural networks
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Large Scale Curiosity
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Uncertainty in Neural Networks: Bayesian Ensembling
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Large Scale GAN Training for High Fidelity Natural Image Synthesis (BigGAN)
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A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
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Automatic detection of known advertisements in radio broadcast with data-driven ALISP transcriptions
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A Practical Approach to Sizing Neural Networks
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Estimation and Control Using Sampling-Based Bayesian Reinforcement Learning
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Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
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Analyzing Inverse Problems with Invertible Neural Networks
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Deep Leaning for Sampling from Arbitrary Probability Distributions
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Neural Processes
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Deep Clustering with Convolutional Autoencoders
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CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
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Improving the Resolution of CNN Feature Maps Efficiently with Multisampling
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The Extreme Value Machine
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Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
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Overview of RNN architectures
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Generating Wikipedia by Summarizing Long Sequences
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
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A Flexible Approach to Automated RNN Architecture Generation
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Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
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Single Channel Audio Source Separation using Convolutional Denoising Autoencoders
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Alpha Go Zero: Mastering the game of Go without human knowledge
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WAVENET: a generative model for raw audio
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