This shows you the differences between two versions of the page.
cs704r_w2019 [2019/01/07 19:01] wingated created |
cs704r_w2019 [2021/06/30 23:42] |
||
---|---|---|---|
Line 1: | Line 1: | ||
- | **Applications** | ||
- | BERT - https://arxiv.org/abs/1810.04805 | ||
- | Machine Theory of Mind - http://arxiv.org/pdf/1802.07740v2.pdf | ||
- | Video-to-Video Synthesis | ||
- | Video Prediction via Selective Sampling | ||
- | Learning to decompose & disadvantage representations for video prediction | ||
- | |||
- | **GANs / unsupervised** | ||
- | |||
- | IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis | ||
- | Wasserstein GAN | ||
- | Text adaptive GAN: Manipulating images with natural language | ||
- | |||
- | **Network design** | ||
- | |||
- | Attention is all you need | ||
- | Neural Ordinary Differential Equations - https://arxiv.org/pdf/1806.07366.pdf | ||
- | Reversible neural networks - https://arxiv.org/abs/1807.03039 - https://arxiv.org/abs/1605.08803 | ||
- | | ||
- | **Foundations / Philosophy** | ||
- | |||
- | Troubling trends in ML scholarship - https://arxiv.org/pdf/1807.03341 | ||
- | A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation - https://arxiv.org/pdf/1506.06472 | ||
- | Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review - https://arxiv.org/pdf/1611.00740 | ||
- | | ||
- | **RL** | ||
- | |||
- | Curiosity-driven exploration by self-supervised prediction | ||
- | Diversity is all you need: Learning skills without a reward function - http://arxiv.org/pdf/1802.06070v6.pdf | ||
- | World Models - https://arxiv.org/pdf/1803.10122v4.pdf | ||
- | |||
- | **Graph networks** | ||
- | |||
- | Graph Neural Networks: A Review of Methods and Applications - https://arxiv.org/abs/1812.08434 | ||
- | Relational inductive biases, deep learning, and graph networks - https://arxiv.org/pdf/1806.01261 | ||
- | |||
- | **Optimization / training** | ||
- | |||
- | Averaging weights leads to wider optima and better generalization - http://arxiv.org/pdf/1803.05407v2.pdf | ||
- | The loss surface of multilayer networks - https://arxiv.org/pdf/1412.0233 | ||
- | Visualizing The Loss Landscape of Neural Nets - https://arxiv.org/pdf/1712.09913v3.pdf | ||
- | The Matrix Calculus You Need For Deep Learning - https://arxiv.org/pdf/1802.01528v3.pdf | ||
- | Group Norm - https://arxiv.org/pdf/1803.08494v3.pdf | ||
- | Kalman Normalization: Normalizing internal representations across network layers | ||
- | MetaReg: towards Domain Generalization using meta-regularization | ||
- | AutoAugment - https://arxiv.org/abs/1805.09501 | ||
- | A Disciplined Approach To Neural Network Hyper-Parameters: part 1 - http://arxiv.org/pdf/1803.09820v2.pdf | ||
- | (Direct) Feedback alignment | ||
- | |||
- | ** Geometric deep learning ** | ||
- | |||
- | Geometric deep learning: going beyond Euclidean data- https://arxiv.org/pdf/1611.08097.pdf | ||
- | Convolutional Neural Networks on Surfaces via Seamless Toric Covers | ||
- | SchNet: A continuous-filter convolutional neural network for modeling quantum interactions | ||
- | Deriving Neural Architectures from Sequence and Graph Kernels | ||
- | CayleyNets: Graph convolutional neural networks with complex rational spectral filters | ||
- | Deep Functional Maps: Structured Prediction for Dense Shape Correspondence | ||
- | Geometric matrix completion with recurrent multi-graph neural networks | ||
- | Neural Message Passing for Quantum Chemistry | ||
- | Deep Learning on Lie Groups for Skeleton-based Action Recognition | ||
- | | ||
- | **Other** | ||
- | |||
- | Bayesian neural networks? |