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Found 8 papers, 2 papers with code
Nearest Neighbor Representations of Neural Circuits
Neural networks successfully capture the computational power of the human brain for many tasks.
Nearest Neighbor Representations of Neurons
It is known that two anchors (the points to which NN is computed) are sufficient for a NN representation of a threshold function, however, the resolution (the maximum number of bits required for the entries of an anchor) is $O(n\log{n})$.
Online Distribution Learning with Local Private Constraints
We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy.
Oracle-Efficient Hybrid Online Learning with Unknown Distribution
We study the problem of oracle-efficient hybrid online learning when the features are generated by an unknown i. i. d.
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls
Third, we compute the complexity of the convex hulls in hyperbolic spaces to assess the extent of data leakage; at the same time, in order to limit communication cost for the hulls, we propose a new quantization method for the Poincar\'e disc coupled with Reed-Solomon-like encoding.
On the Information Capacity of Nearest Neighbor Representations
Specifically, in this paper, we study the representation of Boolean functions in the associative computation model, where the inputs are binary vectors and the corresponding outputs are the labels ($0$ or $1$) of the nearest neighbor anchors.
Machine Unlearning of Federated Clusters
We introduce, for the first time, the problem of machine unlearning for FC, and propose an efficient unlearning mechanism for a customized secure FC framework.
On Algebraic Constructions of Neural Networks with Small Weights
The expressive power of neural gates (number of distinct functions it can compute) depends on the weight sizes and, in general, large weights (exponential in the number of inputs) are required.
