In one respect, there is provided a system for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: at least one processor; and at least one memory including program code which when executed by the at least one processor provides operations comprising: processing an instruction sequence with a trained machine learning model configured to at least detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and to determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. 2. (canceled) 3. (canceled) 4. The system of claim 1, wherein the trained machine learning model comprises a trained neural network. 5. The system of claim 4, wherein the trained neural network comprises a trained long short-term memory neural network. 6. (canceled) 7. The system of claim 5, wherein the trained long short-term memory neural network comprises a first long short-term memory layer, and wherein the first long short-term memory layer comprises a memory cell having an input gate, an output gate, and a forget gate. 8. The system of claim 7, wherein the first long short-term memory layer is configured to receive a first token from the plurality of tokens included in the instruction sequence. 9. The system of claim 8, wherein a current hidden state of the first long short-term memory layer is determined based at least on the first token and a previous hidden state of the first long-short term memory layer, wherein the previous hidden state of the first long short-term memory layer corresponds to one or more tokens already processed by the long short-term memory neural network, and wherein an output of the first long-short term memory layer corresponds to the current hidden state of the first long short-term memory layer. 10. (canceled) 11. The system of claim 4, wherein the trained neural network comprises a trained recursive neural tensor network, and wherein processing the instruction sequence includes processing an abstract syntax tree representation of the instruction sequence. 12. The system of claim 11, further comprising: generating, based at least on a structure of the plurality of tokens in the instruction sequence, the abstract syntax tree representation of the instruction sequence, wherein the abstract syntax tree representation of the instruction sequence includes a parent node corresponding to a first token from the plurality of tokens in the instruction sequence, a first leaf node corresponding to a second token from the plurality of tokens in the instruction sequence, and a second leaf node corresponding to a third token from the plurality of tokens in the instruction sequence, and wherein at least one of the first leaf node and the second leaf node comprises a child node of the parent node. 13. The system of claim 12, wherein the parent node is associated with a weight that is determined based at least on a first weight and a first tensor associated with the first leaf node, and a second weight and a second tensor associated with the second leaf node. 14. The system of claim 12, wherein the parent node comprises a root node of the abstract syntax tree, and wherein the classification of the instruction sequence is determined based at least on the weight associated with the parent node. 15. A method, comprising: processing an instruction sequence with a trained machine learning model configured to at least detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and to determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. 16. The method of claim 15, wherein the trained machine learning model comprises a trained neural network. 17. The method of claim 16, wherein the trained neural network comprises a trained long short-term memory neural network. 18. (canceled) 19. The method of claim 17, further comprising: receiving, at the long short-term memory neural network, a first token from the plurality of tokens included in the instruction sequence; and outputting, by the long short-term memory neural network, a current hidden state of the long short-term memory neural network, wherein the hidden state of the long short-term memory neural network is determined based at least on the first token and a previous hidden state of the long short-term memory neural network, and wherein the previous hidden state of the long short-term memory network corresponds to one or more tokens already processed by the long short-term memory neural network. 20. (canceled) 21. (canceled) 22. The method of claim 17, wherein the trained neural network comprises a trained recursive neural tensor network, and wherein processing the instruction sequence includes processing an abstract syntax tree representation of the instruction sequence with the trained recursive neural tensor network. 23. The method of claim 22, further comprising: generating, based at least on a structure of the plurality of tokens in the instruction sequence, the abstract syntax tree representation of the instruction sequence, wherein the abstract syntax tree representation of the instruction sequence includes a parent node corresponding to a first token from the plurality of tokens in the instruction sequence, a first leaf node corresponding to a second token from the plurality of tokens in the instruction sequence, and a second leaf node corresponding to a third token from the plurality of tokens in the instruction sequence, and wherein at least one of the first leaf node and the second leaf node comprises a child node of the parent node. 24. The method of claim 23, wherein the parent node is associated with a weight that is determined based at least on a first weight and a first tensor associated with the first leaf node, and a second weight and a second tensor associated with the second leaf node. 25. The method of claim 24, wherein the parent node comprises a root node of the abstract syntax tree, and wherein the classification of the instruction sequence is determined based at least on the weight associated with the parent node. 26. A non-transitory computer-readable storage medium including program code which when executed by at least one processor causes operations comprising: processing an instruction sequence with a trained machine learning model configured to at least detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and to determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. 27. An apparatus, comprising: means for processing an instruction sequence with a trained machine learning model configured to at least detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and to determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and means for providing, as an output, the classification of the instruction sequence.