Abstract: In one embodiment, a method includes receiving a query comprising a query content object and constraints, generating a feature vector representing the query content object, accessing a sparse graph comprising nodes corresponding to candidate content objects represented by compact codes and links connecting the nodes, selecting an entry node, selecting similar content objects iteratively by identifying linked nodes of the entry node, decompressing the compact codes representing candidate content objects to generate feature vectors, selecting zero or more similar content objects based on a comparison between the feature vector representing the query content object and the feature vectors representing the candidate content objects, returning the selected similar content objects if a completion condition is satisfied, else repeating the iterative selection by using a linked node corresponding to a most similar content object as the entry node, and sending instructions for presenting one or more of the selected si

Abstract: Disclosed herein are a system, a method and a device for radioactive data generation. A defined marker can be applied or inserted within data of at least one class of a dataset having a plurality of classes of data. The defined marker data can be used to determine if a neural network model was trained using the respective class of data. A device can determine characteristics of a neural network model. The device can compare the characteristics of the neural network model with characteristics of the defined marker data incorporated into a first class of data. The device can determine, responsive to the comparing, whether the neural network model was trained using a dataset having a plurality of classes of data that includes the first class of data incorporated with the defined marker data.

Abstract: In one embodiment, a method includes receiving a query and determining a query vector. The method includes accessing multiple object vectors representing multiple objects, respectively. The method includes, for a first set of object vectors identified as top object vectors, calculating an inner product with the query vector. The method includes progressively computing an inner product of the query vector and each remaining object vector and sending, to a user, the objects corresponding to the top object vectors. Progressively computing an inner product includes checking whether to calculate a first partial inner product based on a bound on the inner product and the minimum inner product for a top object vector, calculating subsequent partial inner products until the inner product is complete, and substituting the object vector for a top object vector if the complete inner product is greater than the minimum inner product.

Abstract: In one embodiment, a method includes receiving a query comprising a query content object and constraints, generating a feature vector representing the query content object, accessing a sparse graph comprising nodes corresponding to candidate content objects represented by compact codes and links connecting the nodes, selecting an entry node, selecting similar content objects iteratively by identifying linked nodes of the entry node, decompressing the compact codes representing candidate content objects to generate feature vectors, selecting zero or more similar content objects based on a comparison between the feature vector representing the query content object and the feature vectors representing the candidate content objects, returning the selected similar content objects if a completion condition is satisfied, else repeating the iterative selection by using a linked node corresponding to a most similar content object as the entry node, and sending instructions for presenting one or more of the selected si

Abstract: In one embodiment, a method includes receiving a query and determining a query vector. The method includes accessing multiple object vectors representing multiple objects, respectively. The method includes, for a first set of object vectors identified as top object vectors, calculating an inner product with the query vector. The method includes progressively computing an inner product of the query vector and each remaining object vector and sending, to a user, the objects corresponding to the top object vectors. Progressively computing an inner product includes checking whether to calculate a first partial inner product based on a bound on the inner product and the minimum inner product for a top object vector, calculating subsequent partial inner products until the inner product is complete, and substituting the object vector for a top object vector if the complete inner product is greater than the minimum inner product.

Abstract: In one embodiment, a method includes receiving a query, wherein the query is represented by an n-dimensional vector in an n-dimensional vector space; quantizing the vector representing the query using a quantizer, wherein the quantized vector corresponds to a polysemous code, and wherein the quantizer has been trained by machine learning to determine polysemous codes such that the Hamming distance approximates the inter-centroid distance using an objective function; calculating, for each of a plurality of content objects, a Hamming distance between the polysemous code corresponding to the vector representing the query and a polysemous code corresponding to a quantized vector representing the content object; and determining that a content object of the plurality of content objects is an approximate nearest neighbor to the query based on determining that the calculated Hamming distance is less than a threshold amount.