Search Results for author:
Found 11 papers, 3 papers with code
RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model
RSBuilding is designed to enhance cross-scene generalization and task universality.
The Implicit Bias of Gradient Noise: A Symmetry Perspective
For one class of symmetry, SGD naturally converges to solutions that have a balanced and aligned gradient noise.
Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling
We conduct a systematic study of the approximation properties of Transformer for sequence modeling with long, sparse and complicated memory.
Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling
Inspired by this analysis, we propose a novel algorithm called Progressive Rescaling Gradient Descent (PRGD) and show that PRGD can maximize the margin at an {\em exponential rate}.
A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent
In this paper, we provide a theoretical study of noise geometry for minibatch stochastic gradient descent (SGD), a phenomenon where noise aligns favorably with the geometry of local landscape.
Q-YOLO: Efficient Inference for Real-time Object Detection
Real-time object detection plays a vital role in various computer vision applications.
Understanding Multi-phase Optimization Dynamics and Rich Nonlinear Behaviors of ReLU Networks
The training process of ReLU neural networks often exhibits complicated nonlinear phenomena.
The alignment property of SGD noise and how it helps select flat minima: A stability analysis
In this paper, we provide an explanation of this striking phenomenon by relating the particular noise structure of SGD to its \emph{linear stability} (Wu et al., 2018).
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars.
Model-based Reinforcement Learning
reinforcement-learning
+2
Generalization Error Bounds for Deep Neural Networks Trained by SGD
Generalization error bounds for deep neural networks trained by stochastic gradient descent (SGD) are derived by combining a dynamical control of an appropriate parameter norm and the Rademacher complexity estimate based on parameter norms.
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks
The convergence of GD and SGD when training mildly parameterized neural networks starting from random initialization is studied.
