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Theory

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Papers

Implicit Regularization for Optimal Sparse Recovery NIPS 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? NIPS 2019
Limitations of the empirical Fisher approximation for natural gradient descent NIPS 2019
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets NIPS 2019
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off NIPS 2019
Gradient Dynamics of Shallow Univariate ReLU Networks NIPS 2019
Trivializations for Gradient-Based Optimization on Manifolds NIPS 2019
Robust Attribution Regularization NIPS 2019
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias NIPS 2019
Beyond the Single Neuron Convex Barrier for Neural Network Certification NIPS 2019
The Geometry of Deep Networks: Power Diagram Subdivision NIPS 2019
Deep Equilibrium Models NIPS 2019
On NMT Search Errors and Model Errors: Cat Got Your Tongue? EMNLP 2019
Robustness Verification of Classification Deep Neural Networks via Linear Programming CVPR 2019
A Sufficient Condition for Convergences of Adam and RMSProp CVPR 2019
Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on N-Spheres CVPR 2019
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression CVPR 2019
Inverse Path Tracing for Joint Material and Lighting Estimation CVPR 2019
ODE-Inspired Network Design for Single Image Super-Resolution CVPR 2019
Deep Dominance - How to Properly Compare Deep Neural Models ACL 2019
Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling ACL 2019
Sensitivity Analysis of Deep Neural Networks AAAI 2019
Learning Adaptive Random Features AAAI 2019
The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes AAAI 2019
Inverse Abstraction of Neural Networks Using Symbolic Interpolation AAAI 2019