Deep Learning
f9b8853ea81731f9bfc11820b064de96-Paper-Conference.pdf
Next, we derive generalization bounds for the primal gap in nonconvex-concave settings.
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation Peihao Chen 1,6 Dongyu Ji1 Kunyang Lin
An important ability for such a robot is to understand human instructions ( e.g., " stop
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Following, a population of such neural network models (referred to as model zoo) would form structures in weight space.
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond Shiji Zhou
Severe gradient conflicts will lead to significantly degraded model performance.
Model Preserving Compression for Neural Networks
Consequently, our method achieves good performance even without fine tuning and admits theoretical analysis.
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Pruning is one of the predominant approaches for compressing deep neural networks (DNNs).
