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FlexPooling with Simple Auxiliary Classifiers in Deep Networks
Arxiv
0+阅读 · 6月12日
Shift-and-Sum Quantization for Visual Autoregressive Models
Arxiv
0+阅读 · 6月15日
SA4Depth: Consistent Pose-Depth Scale Alignment for Self-Supervised Monocular Depth Estimation
Arxiv
0+阅读 · 6月11日
Optimal Initialization in Depth: Lyapunov Initialization and Limit Theorems for Deep Leaky ReLU Networks
Arxiv
0+阅读 · 6月2日
Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
Arxiv
0+阅读 · 4月30日
Geometric Layer-wise Approximation Rates for Deep Networks
Arxiv
0+阅读 · 4月22日
Non-identifiability of Explanations from Model Behavior in Deep Networks of Image Authenticity Judgments
Arxiv
0+阅读 · 4月8日
Using the Path of Least Resistance to Explain Deep Networks
Arxiv
0+阅读 · 2月24日
Covering Numbers for Deep ReLU Networks with Applications to Function Approximation and Nonparametric Regression
Arxiv
0+阅读 · 3月2日
In-Memory ADC-Based Nonlinear Activation Quantization for Efficient In-Memory Computing
Arxiv
0+阅读 · 3月11日
Modelling and analysis of the 8 filters from the "master key filters hypothesis" for depthwise-separable deep networks in relation to idealized receptive fields based on scale-space theory
Arxiv
0+阅读 · 2月23日
VITAL: More Understandable Feature Visualization through Distribution Alignment and Relevant Information Flow
Arxiv
0+阅读 · 2月17日
Deep networks learn to parse uniform-depth context-free languages from local statistics
Arxiv
0+阅读 · 2月9日
Deep Network Trainability via Persistent Subspace Orthogonality
Arxiv
0+阅读 · 2月11日
Optimal Initialization in Depth: Lyapunov Initialization and Limit Theorems for Deep Leaky ReLU Networks
Arxiv
0+阅读 · 2月11日
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