ReLU neural networks
PulseAugur coverage of ReLU neural networks — every cluster mentioning ReLU neural networks across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
-
New research quantifies theory-to-practice gap in neural networks and operators
Researchers have analyzed the sampling complexity for learning with ReLU neural networks and neural operators, deriving upper bounds on convergence rates based on the number of samples. This work establishes a unified t…
-
Tropical circuits with scalar multiplication gates analyzed · 2 sources tracked
Researchers have introduced tropical circuits with scalar multiplication gates, which utilize operations like max, addition, and multiplication by a positive constant. The study establishes exponential size lower bounds…
-
New research details analytic function approximation by ReLU networks · 2 sources tracked
A new research paper published on arXiv explores the approximation of analytic functions using ReLU neural networks. The study introduces a characterization that jointly considers network depth and width, moving beyond …
-
New simplex-based symmetry measure improves analysis of convex sets
Researchers have introduced a novel simplex-based measure of symmetry for compact convex sets, which can be defined as an affine-invariant version of the classical Minkowski measure of symmetry. This new measure improve…
-
New Research Explores ReLU Neural Networks for Binary Classification in O-Minimal Structures
A new research paper explores the use of ReLU neural networks to approximate and learn binary classification tasks within o-minimal structures. The study introduces "traceable sets" as a proxy for definable decision reg…
-
New algebraic method constrains ReLU neural network outputs
Researchers have developed a new method to constrain the outputs of ReLU neural networks by associating them with algebraic varieties. This approach analyzes the piecewise linear and multilinear structures of network ou…
-
Neural networks can generate rectifiable measures
Researchers have demonstrated that ReLU neural networks can approximate m-rectifiable measures with arbitrary precision. The study shows that these networks can generate measures that are push-forwards of the one-dimens…
-
Deep ReLU networks efficiently learn smooth functions
Researchers have published a paper detailing how deep ReLU neural networks can efficiently approximate and learn smooth functions. The study extends previous findings to anisotropic and mixed smooth function classes, es…
-
New method explains ReLU neural networks using geometry
Researchers have developed a new method to understand the decision-making processes of ReLU neural networks by analyzing their geometric properties. This approach views neural networks as dividing input spaces into dist…
-
Researchers Compare In-Context and Agentic Learning Under Constraints
Researchers explored the differences between in-context learning and agentic learning, focusing on how adaptive queries impact performance under realizability constraints. They found that adaptivity generally does not h…