Optimization & Theory

Scalable Graph Networks for Particle Simulations AAAI 2021 Scalable Partial Explainability in Neural Networks via Flexible Activation Functions (Student Abstract) AAAI 2021 Scalable Verification of Quantized Neural Networks AAAI 2021 Scale-Aware Automatic Augmentation for Object Detection CVPR 2021 Scaled-YOLOv4: Scaling Cross Stage Partial Network CVPR 2021 Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup ACL 2021 Scaling Local Self-Attention for Parameter Efficient Visual Backbones CVPR 2021 Scaling Up Exact Neural Network Compression by ReLU Stability NeurIPS 2021 Scaling-Up Robust Gradient Descent Techniques AAAI 2021 Searching by Generating: Flexible and Efficient One-Shot NAS With Architecture Generator CVPR 2021 Searching for Fast Model Families on Datacenter Accelerators CVPR 2021 Searching Parameterized AP Loss for Object Detection NeurIPS 2021 Second-Order Neural ODE Optimizer NeurIPS 2021 Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification NeurIPS 2021 Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics NeurIPS 2021 Sequence Length is a Domain: Length-based Overfitting in Transformer Models EMNLP 2021 SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs NeurIPS 2021 Shallow-UWnet: Compressed Model for Underwater Image Enhancement (Student Abstract) AAAI 2021 Shape Matters: Understanding the Implicit Bias of the Noise Covariance COLT 2021 Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning EMNLP 2021 Shortformer: Better Language Modeling using Shorter Inputs ACL 2021 Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU ICLR 2021 Simple and Effective Stochastic Neural Networks AAAI 2021 Size and Depth Separation in Approximating Benign Functions with Neural Networks COLT 2021 SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature CVPR 2021