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Found 10 papers, 4 papers with code
A Theory of Natural Intelligence
Introduction: In contrast to current AI technology, natural intelligence -- the kind of autonomous intelligence that is realized in the brains of animals and humans to attain in their natural environment goals defined by a repertoire of innate behavioral schemata -- is far superior in terms of learning speed, generalization capabilities, autonomy and creativity.
ImageNet as a Representative Basis for Deriving Generally Effective CNN Architectures
We investigate and improve the representativeness of ImageNet as a basis for deriving generally effective convolutional neural network (CNN) architectures that perform well on a diverse set of datasets and application domains.
Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling
We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy.
Automated Machine Learning in Practice: State of the Art and Recent Results
A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions.
DeepScores and Deep Watershed Detection: current state and open issues
This paper gives an overview of our current Optical Music Recognition (OMR) research.
Deep Learning in the Wild
Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks.
Learning Neural Models for End-to-End Clustering
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass.
Deep Watershed Detector for Music Object Recognition
Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline.
Speaker Clustering Using Dominant Sets
We report on a comprehensive set of experiments on the TIMIT dataset against standard clustering techniques and specific speaker clustering methods.
Sound Audio and Speech Processing
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.
