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Found 22 papers, 10 papers with code
Learning Actionable World Models for Industrial Process Control
To go from (passive) process monitoring to active process control, an effective AI system must learn about the behavior of the complex system from very limited training data, forming an ad-hoc digital twin with respect to process inputs and outputs that captures the consequences of actions on the process's world.
The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training
Self-attention is essential to Transformer architectures, yet how information is embedded in the self-attention matrices and how different objective functions impact this process remains unclear.
AI Agents for Computer Use: A Review of Instruction-based Computer Control, GUI Automation, and Operator Assistants
Instruction-based computer control agents (CCAs) execute complex action sequences on personal computers or mobile devices to fulfill tasks using the same graphical user interfaces as a human user would, provided instructions in natural language.
The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns
We introduce the Cooperative Network Architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets."
MathNet: A Data-Centric Approach for Printed Mathematical Expression Recognition
Based on this process, we developed an improved version of the benchmark dataset im2latex-100k, featuring 30 fonts instead of one.
Efficient Rotation Invariance in Deep Neural Networks through Artificial Mental Rotation
Humans and animals recognize objects irrespective of the beholder's point of view, which may drastically change their appearances.
Deep Neural Networks for Automatic Speaker Recognition Do Not Learn Supra-Segmental Temporal Features
While deep neural networks have shown impressive results in automatic speaker recognition and related tasks, it is dissatisfactory how little is understood about what exactly is responsible for these results.
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions
However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew.
Video object detection for privacy-preserving patient monitoring in intensive care
In this paper, we propose a new method for exploiting information in the temporal succession of video frames.
FormulaNet: A Benchmark Dataset for Mathematical Formula Detection
At over 45k pages, we believe that FormulaNet is the largest MFD dataset with inline formula labels.
Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps
The existence of adversarial attacks on convolutional neural networks (CNN) questions the fitness of such models for serious applications.
PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis
With the spread of COVID-19 over the world, the need arose for fast and precise automatic triage mechanisms to decelerate the spread of the disease by reducing human efforts e. g. for image-based diagnosis.
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.
Is it enough to optimize CNN architectures on ImageNet?
In this work we challenge the notion that CNN architecture design solely based on ImageNet leads to 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.
