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Found 9 papers, 0 papers with code
Quantized Approximately Orthogonal Recurrent Neural Networks
Orthogonal recurrent neural networks (ORNNs) are an appealing option for learning tasks involving time series with long-term dependencies, thanks to their simplicity and computational stability.
Support Exploration Algorithm for Sparse Support Recovery
We introduce a new algorithm promoting sparsity called {\it Support Exploration Algorithm (SEA)} and analyze it in the context of support recovery/model selection problems. The algorithm can be interpreted as an instance of the {\it straight-through estimator (STE)} applied to the resolution of a sparse linear inverse problem.
Local Identifiability of Deep ReLU Neural Networks: the Theory
Is a sample rich enough to determine, at least locally, the parameters of a neural network?
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks
Given two sets $F$, $G$ of real-valued functions, we first prove a general lower bound on how well functions in $F$ can be approximated in $L^p(\mu)$ norm by functions in $G$, for any $p \geq 1$ and any probability measure $\mu$.
Parameter identifiability of a deep feedforward ReLU neural network
The possibility for one to recover the parameters-weights and biases-of a neural network thanks to the knowledge of its function on a subset of the input space can be, depending on the situation, a curse or a blessing.
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
Imposing orthogonality on the layers of neural networks is known to facilitate the learning by limiting the exploding/vanishing of the gradient; decorrelate the features; improve the robustness.
The loss landscape of deep linear neural networks: a second-order analysis
We characterize, among all critical points, which are global minimizers, strict saddle points, and non-strict saddle points.
Overestimation learning with guarantees
Experiments on real data show that the method makes it possible to use the surrogate function in embedded systems for which an underestimation is critical; when computing the reference function requires too many resources.
Multilinear compressive sensing and an application to convolutional linear networks
In this paper, we provide necessary and sufficient conditions on the network topology under which a stability property holds.
