Abstract: An embodiment may include a processor that identifies a plurality of weights from the propositional logical neural network. The embodiment may convert the plurality of weights into a sparse matrix. The embodiment may convert a training set into a plurality of bound vectors. The embodiment may update the sparse matrix using a graphical processing unit (GPU). The embodiment may compute a loss parameter and based on determining the loss function is below threshold, update the plurality of weights of the propositional neural network.

Abstract: Methods, systems, and computer program products for accelerating inference of transformer-based models are provided herein. A computer-implemented method includes obtaining a machine learning model comprising a plurality of transformer blocks, a task, and a natural language dataset; generating a compressed version of the machine learning model based on the task and the natural language dataset, wherein the generating comprises: obtaining at least one set of tokens, wherein each token in the set corresponds to one of the items in the natural language dataset, identifying and removing one or more redundant output activations of different ones of the plurality of transformer blocks for the at least one set of tokens, and adding one or more input activations corresponding to the one or more removed output activations into the machine learning model at subsequent ones of the plurality of the transformer blocks; and outputting the compressed version of the machine learning model to at least one user.

Abstract: A system, program product, and method for performing multi-objective automated machine learning. The method includes selecting two or more objectives from a plurality of objectives to be optimized and injecting data and the objectives into a first machine learning (ML) pipeline. The first ML pipeline includes one or more data transformation stages in communication with a modeling stage. The method also includes executing, subject to the injecting, optimization of the two or objectives. Such executing includes selecting a respective algorithm for each of the data transformation stages and the modeling stage. Each respective algorithm is associated with a first set of respective hyperparameters. The executing also includes generating a plurality of second ML pipelines.

Abstract: Techniques for utilizing model and hyperparameter optimization for multi-objective machine learning are disclosed. In one example, a method comprises the following steps. One of a plurality of hyperparameter optimization operations and a plurality of model parameter optimization operations are performed to generate a first solution set. The other of the plurality of hyperparameter optimization operations and the plurality of model parameter optimization operations are performed to generate a second solution set. At least a portion of the first solution set and at least a portion of the second solution set are combined to generate a third solution set.

Abstract: A computer-implemented machine learning model training method and resulting machine learning model. One embodiment of the method may comprise receiving at a computer memory training data; and training on a computer processor a machine learning model on the received training data using a plurality of batch sizes to produce a trained processor. The training may include calculating a plurality of activations during a forward pass of the training and discarding at least some of the calculated plurality of activations after the forward pass of the training.

Abstract: Methods, systems, and computer program products for accelerating inference of transformer-based models are provided herein. A computer-implemented method includes obtaining a machine learning model comprising a plurality of transformer blocks, a task, and a natural language dataset; generating a compressed version of the machine learning model based on the task and the natural language dataset, wherein the generating comprises: obtaining at least one set of tokens, wherein each token in the set corresponds to one of the items in the natural language dataset, identifying and removing one or more redundant output activations of different ones of the plurality of transformer blocks for the at least one set of tokens, and adding one or more input activations corresponding to the one or more removed output activations into the machine learning model at subsequent ones of the plurality of the transformer blocks; and outputting the compressed version of the machine learning model to at least one user.

Abstract: Methods, systems, and computer program products for accelerating inference of neural network models via dynamic early exits are provided herein. A computer-implemented method includes determining a plurality of candidate exit points of a neural network model; obtaining a plurality of outputs of the neural network model for data samples in a target dataset, wherein the plurality of outputs comprises early outputs of the neural network model from the plurality of candidate exit points and regular outputs of the neural network model; and a set of one or more exit points from the plurality of candidate exits points that are dependent on the target dataset based at least in part on the plurality of outputs.

Abstract: One or more computer processors decompose a weight matrix associated with a neural network utilizing a permutation dependent decomposition. The one or more computer processors regenerate a recovered matrix utilizing the decomposed weight matrix. The one or more computer processors reduce an error between the decomposed weight matrix and regenerated recovered matrix.

Abstract: A computer-implemented machine learning model training method and resulting machine learning model. One embodiment of the method may comprise receiving at a computer memory training data; and training on a computer processor a machine learning model on the received training data using a plurality of batch sizes to produce a trained processor. The training may include calculating a plurality of activations during a forward pass of the training and discarding at least some of the calculated plurality of activations after the forward pass of the training.

Abstract: Methods, systems, and computer program products for dataset dependent low rank decomposition of neural networks are provided herein. A computer-implemented method includes obtaining a target dataset and a trained model of a neural network; providing at least a portion of the target dataset to the trained model; determining relevance of each of one or more of filters of the neural network and channels of the neural network to the target dataset based on the provided portion, wherein the one or more of the filters and the channels correspond to at least one layer of the neural network; and compressing the trained model of the neural network based at least in part on the determined relevancies.

Abstract: Methods, systems, and computer program products for dynamic batch sizing for inferencing of deep neural networks in resource-constrained environments are provided herein. A computer-implemented method includes obtaining, as input for inferencing of one or more deep neural networks, (i) an inferencing model and (ii) one or more resource constraints; computing, based at least in part on the obtained input, a set of statistics pertaining to resource utilization for each of multiple layers in the one or more deep neural networks; determining, based at least in part on (i) the obtained input and (ii) the computed set of statistics, multiple batch sizes to be used for inferencing the multiple layers of the one or more deep neural networks; and outputting, to at least one user, the determined batch sizes to be used for inferencing the multiple layers of the one or more deep neural networks.

Abstract: One embodiment provides a method for predicting maintenance of a redox flow battery, the method including: receiving, from a plurality of sensors, data regarding characteristics of the redox flow battery; weighting, using a processor, each of the characteristics to form an estimated state parameter for the redox flow battery; and determining, using the processor, a maintenance action for the redox flow battery using the estimated state parameter. Other aspects are described and claimed.

Abstract: One embodiment provides a method for predicting maintenance of a redox flow battery, the method including: receiving, from a plurality of sensors, data regarding characteristics of the redox flow battery; weighting, using a processor, each of the characteristics to form an estimated state parameter for the redox flow battery; and determining, using the processor, a maintenance action for the redox flow battery using the estimated state parameter. Other aspects are described and claimed.