Abstract: Methods and systems for a reinforcement learning system. A spatial and temporal representation of an observed state of an environment is encoded. A previous state is estimated from a given state and a size of a reward is adjusted based on a difference between the estimated previous state and the previous state.

Abstract: Balancing an imbalanced dataset, by: Receiving a balancing policy and the imbalanced dataset. Performing initial adjustment of the imbalanced dataset to comply with the balancing policy, by: oversampling one or more underrepresented classes, and, if one or more of the classes are overrepresented, undersampling them. Operating a generative machine learning model to generate samples for the one or more underrepresented classes, based on the initially-adjusted dataset. Operating a machine learning classification model to label the generated samples with class labels corresponding to the one or more underrepresented classes. Selecting some of the generated samples which, according to the labeling, have a relatively high probability of preserving their class labels.

Abstract: An example system includes a processor to receive a query. The processor can retrieve ranked candidates from an index based on the query. The processor can re-rank the ranked candidates using a Bidirectional Encoder Representations from Transformers (BERT) query-question (Q-q) model trained to match queries to questions of a frequently asked question (FAQ) dataset, wherein the BERT Q-q model is fine-tuned using paraphrases generated for the questions in the FAQ dataset. The processor can return the re-ranked candidates in response to the query.

Abstract: During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.

Abstract: Embodiments may determine user intent in conversations with dialogue systems so as to improve the quality of such conversations and to reduce the number of failed conversations. For example, a method may comprise receiving, at a dialogue system, a first text utterance from a user, generating a plurality of second text utterances at the dialogue system in response to the received text utterance, generating a third text utterance based on each generated second text utterance using a trained deep neural network model, generating a score indicating a quality of each conversation, wherein each conversation includes the first text utterance, one of the second text utterances, and the third text utterance based on the one of the second text utterances, and outputting to the user the second text utterance included in the conversation having the highest quality score.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems for classification and identification of cancer genes while correcting for sample bias for tumor-derived genomic features as well as other biased features using machine learning techniques. Embodiments of the present invention can be used to receive a set of genes that include a first gene and a subset of synthetic genes that include similar features to the first gene and receive a set of gene labels associated with physiological characteristics. Embodiments of the present invention can estimate probabilities that genes in the set of genes are associated with gene labels in the set of gene labels using a machine learning classifier and estimate an effective probability range for the first gene and each gene label based, at least in part, on the first gene's estimated probabilities and the estimated probabilities of one or more of the synthetic genes.

Abstract: Embodiments of the present systems and methods may provide techniques for augmenting textual data that may be used for textual classification tasks. Embodiments of such techniques may provide the capability to synthesize labeled data to improve text classification tasks. Embodiments may be specifically useful when only a small amount of data is available, and provide improved performance in such cases. For example, in an embodiment, a method implemented in a computer system may comprise a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise fine-tuning a language model using a training dataset, synthesizing a plurality of samples using the fine-tuned language model, filtering the plurality of synthesized samples, and generating an augmented training dataset comprising the training dataset and the filtered plurality of synthesized sentences.

Abstract: Balancing an imbalanced dataset, by: Receiving a balancing policy and the imbalanced dataset. Performing initial adjustment of the imbalanced dataset to comply with the balancing policy, by: oversampling one or more underrepresented classes, and, if one or more of the classes are overrepresented, undersampling them. Operating a generative machine learning model to generate samples for the one or more underrepresented classes, based on the initially-adjusted dataset. Operating a machine learning classification model to label the generated samples with class labels corresponding to the one or more underrepresented classes. Selecting some of the generated samples which, according to the labeling, have a relatively high probability of preserving their class labels.

Abstract: Embodiments may determine user intent in conversations with dialogue systems so as to improve the quality of such conversations and to reduce the number of failed conversations. For example, a method may comprise receiving, at a dialogue system, a first text utterance from a user, generating a plurality of second text utterances at the dialogue system in response to the received text utterance, generating a third text utterance based on each generated second text utterance using a trained deep neural network model, generating a score indicating a quality of each conversation, wherein each conversation includes the first text utterance, one of the second text utterances, and the third text utterance based on the one of the second text utterances, and outputting to the user the second text utterance included in the conversation having the highest quality score.

Abstract: An example system includes a processor to receive a query. The processor can retrieve ranked candidates from an index based on the query. The processor can re-rank the ranked candidates using a Bidirectional Encoder Representations from Transformers (BERT) query-question (Q-q) model trained to match queries to questions of a frequently asked question (FAQ) dataset, wherein the BERT Q-q model is fine-tuned using paraphrases generated for the questions in the FAQ dataset. The processor can return the re-ranked candidates in response to the query.

Abstract: Embodiments of the present systems and methods may provide techniques for augmenting textual data that may be used for textual classification tasks. Embodiments of such techniques may provide the capability to synthesize labeled data to improve text classification tasks. Embodiments may be specifically useful when only a small amount of data is available, and provide improved performance in such cases. For example, in an embodiment, a method implemented in a computer system may comprise a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise fine-tuning a language model using a training dataset, synthesizing a plurality of samples using the fine-tuned language model, filtering the plurality of synthesized samples, and generating an augmented training dataset comprising the training dataset and the filtered plurality of synthesized sentences.

Abstract: In some examples, a system for executing natural language processing techniques can include a processor to detect text comprising a word and a number. The processor can also embed, via a word embedding model, the word into a first vector of a vector space and embed the number by converting the number into a second vector of the vector space. Additionally, the processor can train a deep neural network to execute instructions based on the first embedded vector of the word and the second embedded vector of the number. Furthermore, the processor can process an instruction based on the trained deep neural network.

Abstract: Methods and systems for a reinforcement learning system. A spatial and temporal representation of an observed state of an environment is encoded. A previous state is estimated from a given state and a size of a reward is adjusted based on a difference between the estimated previous state and the previous state.

Abstract: In some examples, a system for executing natural language processing techniques can include a processor to detect text comprising a word and a number. The processor can also embed, via a word embedding model, the word into a first vector of a vector space and embed the number by converting the number into a second vector of the vector space. Additionally, the processor can train a deep neural network to execute instructions based on the first embedded vector of the word and the second embedded vector of the number. Furthermore, the processor can process an instruction based on the trained deep neural network.

Abstract: An example system includes a processor to receive a plurality of object aspects of an object to be evaluated using a process, a structure of the process, a plurality of extracted facts from documents, a tree related to the plurality of object aspects and the structure, and a thesis for each leaf in the tree. The processor is also to relate the extracted facts to the theses in the tree. The processor is to generate a score for each leaf corresponding to a fact in the tree. The processor is to generate a thesis score and a thesis summary for each thesis based on the scores and the summaries of related facts for each thesis. The processor is to further generate a final score for the object based on the thesis scores.

Abstract: Embodiments are directed to computer implemented method of assessing a relevancy of a pathway to a disease of interest, the pathway having a source and a target. The method includes developing an impact of the source on the pathway. The method further includes developing a value of targeting, based at least in part on an alteration of the pathway, the pathway with a drug of interest. The method further includes identifying a relationship between the source and the target within the pathway. The method further includes combining: the impact of the source on the pathway; the value of targeting, based at least in part on the alteration of the pathway, the pathway with a drug of interest; and the relationship between the source and the target within the pathway, wherein the combining results in an assessment that represents the relevancy of the pathway to the disease of interest.

Abstract: Embodiments are directed to computer implemented method of assessing a relevancy of a pathway to a disease of interest, the pathway having a source and a target. The method includes developing an impact of the source on the pathway. The method further includes developing a value of targeting, based at least in part on an alteration of the pathway, the pathway with a drug of interest. The method further includes identifying a relationship between the source and the target within the pathway. The method further includes combining: the impact of the source on the pathway; the value of targeting, based at least in part on the alteration of the pathway, the pathway with a drug of interest; and the relationship between the source and the target within the pathway, wherein the combining results in an assessment that represents the relevancy of the pathway to the disease of interest.

Abstract: During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems for classification and identification of cancer genes while correcting for sample bias for tumor-derived genomic features as well as other biased features using machine learning techniques. Embodiments of the present invention can be used to receive a set of genes that include a first gene and a subset of synthetic genes that include similar features to the first gene and receive a set of gene labels associated with physiological characteristics. Embodiments of the present invention can estimate probabilities that genes in the set of genes are associated with gene labels in the set of gene labels using a machine learning classifier and estimate an effective probability range for the first gene and each gene label based, at least in part, on the first gene's estimated probabilities and the estimated probabilities of one or more of the synthetic genes.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems for classification and identification of cancer genes while correcting for sample bias for tumor-derived genomic features as well as other biased features using machine learning techniques. Embodiments of the present invention can be used to receive a set of genes that include a first gene and a subset of synthetic genes that include similar features to the first gene and receive a set of gene labels associated with physiological characteristics. Embodiments of the present invention can estimate probabilities that genes in the set of genes are associated with gene labels in the set of gene labels using a machine learning classifier and estimate an effective probability range for the first gene and each gene label based, at least in part, on the first gene's estimated probabilities and the estimated probabilities of one or more of the synthetic genes.