ICLR 2019 Events with Videos
Invited Talks
- Highlights of Recent Developments in Algorithmic Fairness
- Learning Representations Using Causal Invariance
- Can Machine Learning Help to Conduct a Planetary Healthcheck?
- Adversarial Machine Learning
- Developmental Autonomous Learning: AI, Cognitive Sciences and Educational Technology
- Learning Natural Language Interfaces with Neural Models
- Learning (from) language in context
Orals
- Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
- BA-Net: Dense Bundle Adjustment Networks
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training
- How Powerful are Graph Neural Networks?
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
- Learning Protein Structure with a Differentiable Simulator
- Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
- A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
- Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
- Learning to Remember More with Less Memorization
- Learning Robust Representations by Projecting Superficial Statistics Out
- ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
- Learning deep representations by mutual information estimation and maximization
- KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
- Deterministic Variational Inference for Robust Bayesian Neural Networks
- FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models
- ICLR Debate
- Pay Less Attention with Lightweight and Dynamic Convolutions
- The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
- Smoothing the Geometry of Probabilistic Box Embeddings
- Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
- Meta-Learning Update Rules for Unsupervised Representation Learning
- Temporal Difference Variational Auto-Encoder
- Transferring Knowledge across Learning Processes
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