Abstract: An apparatus for training a reinforcement learning model according to an embodiment includes a starting point determinator configured to determine starting points from an input value of a combinatorial optimization problem, a multi-explorer configured to generate exploration trajectories by performing exploration from each of the starting points using a reinforcement learning model, a trajectory evaluator configured to calculate an evaluation value of each of the exploration trajectories using an evaluation function of the combinatorial optimization problem, a baseline calculator configured to calculate a baseline for the input value from the evaluation value of each exploration trajectory, an advantage calculator configured to calculate an advantage of each of the exploration trajectories using the evaluation value of each exploration trajectory and the baseline, and a parameter updater configured to update parameters of the reinforcement learning model by using the exploration trajectories and the advantage

Abstract: An apparatus includes a modified image generator generating modified images by modifying each unlabeled image, a pre-trainer to generate a feature vector for each modified image by using an artificial neural network-based encoder and train the encoder based on the feature vector for each modified image, a pseudo-label generator to generate a feature vector for each unlabeled training image, cluster the training images based on the feature vector for each training image, and generate a pseudo-label for at least one training image among the training images based on the clustering result, and a further trainer to generate a predicted label by using the trained encoder and a classification model including a classifier to generate a predicted label for an image input to the trained encoder based on a feature vector, and train the classification model based on the pseudo-label and predicted label for the at least one training image.

Abstract: An apparatus for encoding matrix data according to an embodiment includes an initial encoding layer that outputs a plurality of encoded row vectors and a plurality of encoded column vectors for one or more pieces of matrix data representing information on a relationship between items having a plurality of characteristics based on the matrix data, a plurality of initial row vectors corresponding to the number of rows of the matrix data, and a plurality of initial column vectors corresponding to the number of columns of the matrix data, and one or more encoding layers that are disposed after the initial encoding layer and perform additional encoding on the plurality of encoded row vectors and the plurality of encoded column vectors.

Abstract: A method of embedding a vector representing an arrangement state of objects in a 3D space according to an embodiment includes dividing a target space into M×N subspaces extending from a first surface among planes forming the target space having a cuboid shape, calculating, for each of the subspaces, a feature representing an object arrangement state in the subspace, inputting data representing the feature of each of the subspaces into an artificial neural network and obtaining, from the artificial neural network, an embedding vector representing an arrangement state of an object in the target space.

Abstract: An apparatus includes a modified image generator generating modified images by modifying each unlabeled image, a pre-trainer to generate a feature vector for each modified image by using an artificial neural network-based encoder and train the encoder based on the feature vector for each modified image, a pseudo-label generator to generate a feature vector for each unlabeled training image, cluster the training images based on the feature vector for each training image, and generate a pseudo-label for at least one training image among the training images based on the clustering result, and a further trainer to generate a predicted label by using the trained encoder and a classification model including a classifier to generate a predicted label for an image input to the trained encoder based on a feature vector, and train the classification model based on the pseudo-label and predicted label for the at least one training image.

Abstract: An apparatus for training a reinforcement learning model according to an embodiment includes a starting point determinator configured to determine starting points from an input value of a combinatorial optimization problem, a multi-explorer configured to generate exploration trajectories by performing exploration from each of the starting points using a reinforcement learning model, a trajectory evaluator configured to calculate an evaluation value of each of the exploration trajectories using an evaluation function of the combinatorial optimization problem, a baseline calculator configured to calculate a baseline for the input value from the evaluation value of each exploration trajectory, an advantage calculator configured to calculate an advantage of each of the exploration trajectories using the evaluation value of each exploration trajectory and the baseline, and a parameter updater configured to update parameters of the reinforcement learning model by using the exploration trajectories and the advantage

Abstract: A method of embedding a vector representing an arrangement state of objects in a 3D space according to an embodiment includes dividing a target space into M×N subspaces extending from a first surface among planes forming the target space having a cuboid shape, calculating, for each of the subspaces, a feature representing an object arrangement state in the subspace, inputting data representing the feature of each of the subspaces into an artificial neural network and obtaining, from the artificial neural network, an embedding vector representing an arrangement state of an object in the target space.