Paper List

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1. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
2. Improving Style-Content Disentanglement in Image-to-Image Translation
3. AI Assisted Apparel Design
4. One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
5. RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
6. Towards Robust and Reproducible Active Learning Using Neural Networks
7. Simple and Effective VAE Training with Calibrated Decoders
8. On Mixup Regularization
9. Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
10. Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
11. Do Adversarially Robust ImageNet Models Transfer Better?
12. TSIT: A Simple and Versatile Framework for Image-to-Image Translation
13. Rewriting a Deep Generative Model
14. Supermasks in Superposition
15. Data Valuation using Reinforcement Learning
16. Consistency Regularization for Variational Auto-Encoders
17. Inference Suboptimality in Variational Autoencoders
18. Aligning AI With Shared Human Values
19. The Surprising Effectiveness of Linear Unsupervised Image-to-Image Translation
20. SpeedySpeech: Efficient Neural Speech Synthesis
21. Big Bird: Transformers for Longer Sequences
22. Superposition of many models into one
23. Avoiding Latent Variable Collapse with Generative Skip Models
24. Generative Modeling by Estimating Gradients of the Data Distribution
25. Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
26. Transformer based Multilingual document Embedding model
27. Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries
28. Learning Trailer Moments in Full-Length Movies with Co-Contrastive Attention
29. WaveGrad: Estimating Gradients for Waveform Generation
30. Implicit Kernel Attention
31. CausalGAN Learning Causal Implicit Generative Models with Adversarial Training
32. Approximate Inference for Spectral Mixture Kernel
33. SYNTHESIZER Rethinking Self-Attention in Transformer Models
34. ExGAN: Adversarial Generation of Extreme Samples
35. Understanding the role of individual units in a deep neural network
36. Bayesian Perceptron: Towards fully Bayesian Neural Networks