Abstract: One or more systems, devices, computer program products and/or computer implemented methods of use provided herein relate to a process of mitigating biased training instances associated with a machine learning model without additional refitting of the machine learning model. A system can comprise a memory that stores computer executable components, and a processor that executed the computer executable components stored in the memory, wherein the computer executable components can comprise a training data influence estimation component and an influence mitigation component. The training data influence estimation component can receive a pre-trained machine learning model and calculate a fairness influence score of training instances on group fairness metrics associated with the pre-trained machine learning model.

Abstract: Obtain access to a pretrained encoder-decoder language model. Using a dataset including a plurality of text-graph pairs, carry out first fine-tuning training on the pre-trained language model by minimizing cross-entropy loss. A text portion of each text-graph pair includes a list of text tokens and a graph portion of each text-graph pair includes a list of graph tokens. The first fine-tuning training results in an intermediate model. Carry out second fine-tuning training on the intermediate model, by reinforcement learning, to obtain a final model. Make the final model available for deployment.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate a dual learning bridge between text and a knowledge graph are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a model component that employs a model to learn associations between text data and a knowledge graph. The computer executable components further comprise a translation component that uses the model to bidirectionally translate second text data and one or more knowledge graph paths based on the associations.

Abstract: A machine learning system that implements Sobolev Independence Criterion (SIC) for feature selection is provided. The system receives a dataset including pairings of stimuli and responses. Each stimulus includes multiple features. The system generates a correctly paired sample of stimuli and responses from the dataset by pairing stimuli and responses according to the pairings of stimuli and responses in the dataset. The system generates an alternatively paired sample of stimuli and responses from the dataset by pairing stimuli and responses differently than the pairings of stimuli and responses in the dataset. The system determines a witness function and a feature importance distribution across the features that optimizes a cost function that is evaluated based on the correctly paired and alternatively paired samples of the dataset. The system selects one or more features based on the computed feature importance distribution.

Abstract: A service receives a persuasion-based input comprising a text and one or more marketing objectives to persuade a desired response. The service evaluates persuasion values of text segments of the text and persuasion transition values consecutively between respective persuasion values of the persuasion values across the text segments. The service generates a desired curve of persuasion factors across the text segments according to the one or more marketing objectives. The service recommends one or more replacement words to replace one or more selected words in text to move a deviation between the persuasion values and transition values in comparison to the desired curve of persuasion factors.

Abstract: A computer-implemented method according to one aspect includes training a latent variable model (LVM), utilizing labeled data and unlabeled data within a data set; training a classifier, utilizing the labeled data and associated labels within the data set; and generating new data having a predetermined set of labels, utilizing the trained LVM and the trained classifier.

Abstract: Generating a drug molecule design by training an attribute predictor model using an embedding of a first molecular data base, training a first machine learning model using the attribute predictor, yielding a second embedding of the first molecular data base, training a binding affinity model using a second molecular database and the second embedding of the first molecular database, and generating a molecule design according to the second embedding and the binding affinity model.

Abstract: Generating a molecule design by training a binding affinity model using a first molecular database and an embedding of a second molecular database and generating a molecule design according to the embedding and the binding affinity model.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate a dual learning bridge between text and a knowledge graph are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a model component that employs a model to learn associations between text data and a knowledge graph. The computer executable components further comprise a translation component that uses the model to bidirectionally translate second text data and one or more knowledge graph paths based on the associations.

Abstract: A machine learning system receives a witness function that is determined based on an initial sample of a dataset comprising multiple pairs of stimuli and responses. Each stimulus includes multiple features. The system receives a holdout sample of the dataset comprising one or more pairs of stimuli and responses that are not used to determine the witness function. The system generates a simulated sample based on the holdout sample. Values of a particular feature of the stimuli of the simulated sample are predicted based on values of features other than the particular feature of the stimuli of the simulated sample. The system applies the holdout sample to the witness function to obtain a first result. The system applies the simulated sample to the witness function to obtain a second result. The system determines whether to select the particular feature based on a comparison between the first result and the second result.

Abstract: De novo, artificial intelligence (AI) designed antimicrobial peptides (AMPs), antibacterial products comprising the AMPs and methods for treating bacterial infections using the products are provided. In one or more embodiments, the AMPs were designed using conditional latent attribute space sampling (CLaSS). The AMPs comprise up to twenty natural amino acids in length, including one with twelve and another with thirteen natural amino acids in length. The AMPs demonstrate low-toxicity and show high antimicrobial potency against diverse pathogens including multi-medication-resistant Gram negative Klebsiella pneumoniae.

Abstract: Techniques regarding generating molecular structures with attributes of interest are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a transfer learning component that determines a molecular structure of a compound by employing a transfer learning process that utilizes lessons learned from an unconditional generative machine learning model to train a conditional machine learning model that regards a target attribute profile.

Abstract: An intelligent computer platform to introduce adversarial training to natural language processing (NLP). An initial training set is modified with synthetic training data to create an adversarial training set. The modification includes use of natural language understanding (NLU) to parse the initial training set into components and identify component categories. As input is presented, a classifier evaluates the input and leverages the adversarial training set to identify the intent of the input. An identified classification model generates accurate and reflective response data based on the received input.

Abstract: Techniques for filtering artificial intelligence (AI)-designed molecules for laboratory testing provided. According to an embodiment, computer implemented method can comprise selecting, by a system operatively coupled to a processor, a first subset of AI-designed molecules from a set of AI-designed molecules as candidate pharmaceutical agents based on classification of the AI-designed molecules using one or more classifiers. The method further comprises selecting, by the system, a second subset of the candidate pharmaceutical agents for wet laboratory testing based on evaluation of molecular interactions between the candidate pharmaceutical agents and one or more biological targets using one or more computer simulations.

Abstract: De novo, artificial intelligence (AI) designed antimicrobial peptides (AMPs), antibacterial products comprising the AMPs and methods for treating bacterial infections using the products are provided. In one or more embodiments, the AMPs were designed using conditional latent attribute space sampling (CLaSS). The AMPs comprise up to twenty natural amino acids in length, including one with twelve and another with thirteen natural amino acids in length. The AMPs demonstrate low-toxicity and show high antimicrobial potency against diverse pathogens including multi-medication-resistant Gram negative Klebsiella pneumoniae.

Abstract: De novo, artificial intelligence (AI) designed antimicrobial peptides (AMPs), antibacterial products comprising the AMPs and methods for treating bacterial infections using the products are provided. In one or more embodiments, the AMPs were designed using conditional latent attribute space sampling (CLaSS). The AMPs comprise up to twenty natural amino acids in length, including one with twelve and another with thirteen natural amino acids in length. The AMPs demonstrate low-toxicity and show high antimicrobial potency against diverse pathogens including multi-medication-resistant Gram negative Klebsiella pneumoniae.

Abstract: An intelligent computer platform to introduce adversarial training to natural language processing (NLP). An initial training set is modified with synthetic training data to create an adversarial training set. The modification includes use of natural language understanding (NLU) to parse the initial training set into components and identify component categories. One or more paraphrase terms are identified with respect to the components and component categories, and function as replacement terms. The synthetic training data is effectively a merging of the initial training set with the replacement terms. As input is presented, a classifier leverages the adversarial training set to identify the intent of the input and to output a classification label to generate accurate and reflective response data.

Abstract: A service receives a persuasion-based input comprising a text and one or more marketing objectives to persuade a desired response. The service evaluates persuasion values of text segments of the text and persuasion transition values consecutively between respective persuasion values of the persuasion values across the text segments. The service generates a desired curve of persuasion factors across the text segments according to the one or more marketing objectives. The service recommends one or more replacement words to replace one or more selected words in text to move a deviation between the persuasion values and transition values in comparison to the desired curve of persuasion factors.

Abstract: A computer-implemented method includes obtaining a training data set including text data indicating one or more phrases or sentences. The computer-implemented method includes training a classifier using supervised machine learning based on the training data set and additional text data indicating one or more out-of-domain phrases or sentences. The computer-implemented method includes training an autoencoder using unsupervised machine learning based on the training data. The computer-implemented method further includes combining the classifier and the autoencoder to generate the out-of-domain sentence detector configured to generate an output indicating a classification of whether input text data corresponds to an out-of-domain sentence. The output is based on a combination of a first output of the classifier and a second output of the autoencoder.

Abstract: A machine learning system that implements Sobolev Independence Criterion (SIC) for feature selection is provided. The system receives a dataset including pairings of stimuli and responses. Each stimulus includes multiple features. The system generates a correctly paired sample of stimuli and responses from the dataset by pairing stimuli and responses according to the pairings of stimuli and responses in the dataset. The system generates an alternatively paired sample of stimuli and responses from the dataset by pairing stimuli and responses differently than the pairings of stimuli and responses in the dataset. The system determines a witness function and a feature importance distribution across the features that optimizes a cost function that is evaluated based on the correctly paired and alternatively paired samples of the dataset. The system selects one or more features based on the computed feature importance distribution.