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Papers

Decentralized Riemannian Gradient Descent on the Stiefel Manifold ICML 2021 Deciding What to Learn: A Rate-Distortion Approach ICML 2021 Decomposition Strategies to Count Integer Solutions over Linear Constraints IJCAI 2021 Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices ICML 2021 Deep Bucket Elimination IJCAI 2021 Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws AAAI 2021 Deep Contextual Video Compression NeurIPS 2021 Deep Gradient Projection Networks for Pan-sharpening CVPR 2021 Deep Graph Matching Under Quadratic Constraint CVPR 2021 Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures AAAI 2021 Deep Learning meets Projective Clustering ICLR 2021 DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain Decomposition CVPR 2021 Deep Optimized Priors for 3D Shape Modeling and Reconstruction CVPR 2021 DeepPanoContext: Panoramic 3D Scene Understanding With Holistic Scene Context Graph and Relation-Based Optimization ICCV 2021 Deep Spectral Ranking AISTATS 2021 DEEPSPLIT: An Efficient Splitting Method for Neural Network Verification via Indirect Effect Analysis IJCAI 2021 Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients ICLR 2021 DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization IJCAI 2021 Demystifying and Generalizing BinaryConnect NeurIPS 2021 Denoising Word Embeddings by Averaging in a Shared Space ACL 2021 Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset ICML 2021 Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers ICML 2021 Descent-to-Delete: Gradient-Based Methods for Machine Unlearning ALT 2021 Determinantal Beam Search ACL 2021 Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD NeurIPS 2021