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Found 11 papers, 2 papers with code
Neural-guided, Bidirectional Program Search for Abstraction and Reasoning
More specifically, we extend existing execution-guided program synthesis approaches with deductive reasoning based on function inverse semantics to enable a neural-guided bidirectional search algorithm.
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN
The FTT module was added to a VGG-11 CNN architecture and ten random initializations were trained on 20-class subsets of the Places and EcoSet datasets for scene and object classification respectively.
Distribution of Classification Margins: Are All Data Equal?
Recent theoretical results show that gradient descent on deep neural networks under exponential loss functions locally maximizes classification margin, which is equivalent to minimizing the norm of the weight matrices under margin constraints.
Dreaming with ARC
We argue that this can be achieved by a modular system –– one that can adapt to solving different problems by changing only the modules chosen and the order in which those modules are applied to the problem.
Biologically Inspired Mechanisms for Adversarial Robustness
A convolutional neural network strongly robust to adversarial perturbations at reasonable computational and performance cost has not yet been demonstrated.
Hierarchically Compositional Tasks and Deep Convolutional Networks
For object recognition we find, as expected, that scrambling does not affect the performance of shallow or deep fully connected networks contrary to the out-performance of convolutional networks.
Double descent in the condition number
In solving a system of $n$ linear equations in $d$ variables $Ax=b$, the condition number of the $n, d$ matrix $A$ measures how much errors in the data $b$ affect the solution $x$.
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization
In approximation theory both shallow and deep networks have been shown to approximate any continuous functions on a bounded domain at the expense of an exponential number of parameters (exponential in the dimensionality of the function).
Theory III: Dynamics and Generalization in Deep Networks
In particular, gradient descent induces a dynamics of the normalized weights which converge for $t \to \infty$ to an equilibrium which corresponds to a minimum norm (or maximum margin) solution.
A Surprising Linear Relationship Predicts Test Performance in Deep Networks
Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors?
Theory IIIb: Generalization in Deep Networks
Here we prove a similar result for nonlinear multilayer DNNs near zero minima of the empirical loss.
