Lambert Schomaker

Lambert Schomaker
University of Groningen | RUG · Institute of Artificial Intelligence and Cognitive Engineering (Alice)

PhD, full professor

About

311
Publications
122,569
Reads
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7,424
Citations
Citations since 2016
123 Research Items
3527 Citations
20162017201820192020202120220200400600
20162017201820192020202120220200400600
20162017201820192020202120220200400600
20162017201820192020202120220200400600
Additional affiliations
January 2001 - present
Rijksuniversiteit Groningen
Position
  • Professor (Full)
January 1984 - December 2000
Radboud University
Position
  • Professor (Assistant)
Description
  • PhD then postdoc then assistant prof.
January 1983 - January 1984
Tilburg Universiteit
Description
  • Junior Researcher EMG modeling

Publications

Publications (311)
Conference Paper
Full-text available
This study introduced a framework for smart HVAC controllers that can be used at scale. The proposed controllers derive their control policy solely from data. First a simulator of the process is learned, which we call the Neural Twin. The results showed that the Neural Twin framework is able to simulate several distinct processes with an average ab...
Article
Full-text available
In this paper, we concentrate on the text-to-image synthesis task that aims at automatically producing perceptually realistic pictures from text descriptions. Recently, several single-stage methods have been proposed to deal with the problems of a more complicated multi-stage modular architecture. However, they often suffer from the lack-of-diversi...
Preprint
Full-text available
Recognition of Off-line Chinese characters is still a challenging problem, especially in historical documents, not only in the number of classes extremely large in comparison to contemporary image retrieval methods, but also new unseen classes can be expected under open learning conditions (even for CNN). Chinese character recognition with zero or...
Article
Full-text available
This paper focuses on the classification of seven locomotion modes (sitting, standing, level ground walking, ramp ascent and descent, stair ascent and descent), the transitions among these modes, and the gait phases within each mode, by only using data in the frequency domain from one or two inertial measurement units. Different deep neural network...
Article
Full-text available
Electronic conduction along individual domain walls (DWs) is reported in BiFeO3 (BFO) and other nominally insulating ferroelectrics. DWs in these materials separate regions of differently oriented electrical polarization (domains) and are just a few atoms wide, providing self‐assembled nanometric conduction paths. Herein, it is shown that electroni...
Conference Paper
The task of text-to-image generation has achieved remarkable progress due to the advances in conditional generative adversarial networks (GANs). However, existing conditional text-to-image GANs approaches mostly concentrate on improving both image quality and semantic relevance but ignore the explainability of the model which plays a vital role in...
Preprint
Electronic conduction along individual domain walls (DWs) has been reported in BiFeO$_3$ (BFO) and other nominally insulating ferroelectrics. DWs in these materials separate regions of differently oriented electrical polarization (domains) and are just a few atoms wide, providing self-assembled nanometric conduction paths. In this work, it is shown...
Preprint
Computational approaches for sub-organelle protein localisation and identification are often neglected while general methods, not suitable for specific use cases, are promoted instead. In particular, organelle-specific research lacks user-friendly and easily accessible computational tools that allow researchers to perform computational analysis bef...
Preprint
Full-text available
Although the performance of current automatic recognition algorithms of on-line handwriting has much improved in recent years, there are still many problems with the actual application of these systems. It appears that the step from academical experiments to real-life use of such algorithms, in, e.g., portable pen computers, is still difficult. Wha...
Technical Report
Full-text available
The necessity for cataloguing Western handwriting styles becomes more and more apparent as online handwriting recognition algorithms currently reach an asymptote in their performance, and a limited generalization from laboratory training set to real life conditions is observed. Although the algorithms as such still need to be refined, and an optima...
Poster
Background: A DPP6 gene risk haplotype, putatively enhancing Purkinje fiber transient outward current I to , associates with familial idiopathic ventricular fibrillation (IVF). Outside this genetic disposition, no clinical risk factors nor ECG features for IVF have so far been identified to recognize those at risk for IVF. Objective: We aimed to de...
Preprint
Full-text available
The task of text-to-image generation has achieved remarkable progress due to the advances in the conditional generative adversarial networks (GANs). However, existing conditional text-to-image GANs approaches mostly concentrate on improving both image quality and semantic relevance but ignore the explainability of the model which plays a vital role...
Preprint
Full-text available
Researchers continually perform corroborative tests to classify ancient historical documents based on the physical materials of their writing surfaces. However, these tests, often performed on-site, requires actual access to the manuscript objects. The procedures involve a considerable amount of time and cost, and can damage the manuscripts. Develo...
Preprint
Full-text available
Text-to-image generation intends to automatically produce a photo-realistic image, conditioned on a textual description. It can be potentially employed in the field of art creation, data augmentation, photo-editing, etc. Although many efforts have been dedicated to this task, it remains particularly challenging to generate believable, natural scene...
Chapter
Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on...
Chapter
Full-text available
In recent years, an enormous amount of fluorescence microscopy images were collected in high-throughput lab settings. Analyzing and extracting relevant information from all images in a short time is almost impossible. Detecting tiny individual cell compartments is one of many challenges faced by biologists. This paper aims at solving this problem b...
Conference Paper
Full-text available
Word embeddings are used as building blocks for a wide range of natural language processing and information retrieval tasks. These embeddings are usually represented as continuous vectors, requiring significant memory capacity and computationally expensive similarity measures. In this study, we introduce a novel method for semantic hashing continuo...
Preprint
Full-text available
In this paper, we present an efficient and effective single-stage framework (DiverGAN) to generate diverse, plausible and semantically consistent images according to a natural-language description. DiverGAN adopts two novel word-level attention modules, i.e., a channel-attention module (CAM) and a pixel-attention module (PAM), which model the impor...
Preprint
Full-text available
Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on...
Conference Paper
Full-text available
Reward shaping is an efficient way to incorporate domain knowledge into a reinforcement learning agent. Nevertheless , it is unpractical and inconvenient to require prior knowledge for designing shaping rewards. Therefore, learning the shaping reward function by the agent during training could be more effective. In this paper, based on the potentia...
Article
Full-text available
The strength of long short-term memory neural networks (LSTMs) that have been applied is more located in handling sequences of variable length than in handling geometric variability of the image patterns. In this paper, an end-to-end convolutional LSTM neural network is used to handle both geometric variation and sequence variability. The best resu...
Conference Paper
Full-text available
Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration. However, in long-horizon motion planning tasks, a challenging problem in deploying IL and RL methods is how to generate and collect massive, broadly distributed data suc...
Article
Document binarization is a key step in most document analysis tasks. However, historical-document images usually suffer from various degradations, making this a very challenging processing stage. The performance of document image binarization has improved dramatically in recent years by the use of Convolutional Neural Networks (CNNs). In this paper...
Article
Full-text available
The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. This study takes an innovative approach to palaeography-the study of ancient handwriting-as a new entry point to access this scribal culture. One of the problems of palaeography is to determine writer identity or difference when the writing style is near uniform. Thi...
Preprint
Full-text available
This paper presents an end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of local fragment-based features. The global-context information is extracted from the tail of the neural network by a global average pooling step. The sequence of local and...
Preprint
In recent years, an enormous amount of fluorescence microscopy images were collected in high-throughput lab settings. Analyzing and extracting relevant information from all images in a short time is almost impossible. Detecting tiny individual cell compartments is one of many challenges faced by biologists. This paper aims at solving this problem b...
Article
This paper presents an end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of local fragment-based features. The global-context information is extracted from the tail of the neural network by a global average pooling step. The sequence of local and...
Preprint
Full-text available
Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration. However, in long-horizon motion planning tasks, a challenging problem in deploying IL and RL methods is how to generate and collect massive, broadly distributed data suc...
Conference Paper
Full-text available
In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation bias, which may lead to poor performance or unstable learning. In this paper, we present a novel analysis of this problem using various control tasks. For solving these tasks, Q-learning is combined with a multilayer perceptron (MLP), experience rep...
Article
In this paper, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. We train a deep neural network for estimating the robot’s position in the environment using ground truth information provided by a classical localization an...
Preprint
Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of being cheaply available for large volumes of scholarly documents. Previous work has dealt with number of...
Presentation
Presentation given at the CogniGron Center in the CogniGron@Work series 18/12/2020 How to bridge the gap between current deep learning and current neuromorphic computing? Lambert Schomaker Abstract ›It has become clear that the researchers in neuromorphic computing focus on training methods for multi-layer perceptrons pointing to a large numbe...
Preprint
Full-text available
Most existing text-to-image generation methods adopt a multi-stage modular architecture which has three significant problems: (1) Training multiple networks can increase the run time and affect the convergence and stability of the generative model; (2) These approaches ignore the quality of early-stage generator images; (3) Many discriminators need...
Article
This paper presents CentroidNetV2, a novel hybrid Convolutional Neural Network (CNN) that has been specifically designed to segment and count many small and connected object instances. This complete redesign of the original CentroidNet uses a CNN backbone to regress a field of centroid-voting vectors and border-voting vectors. The segmentation mask...
Preprint
Full-text available
The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. Palaeography - the study of ancient handwriting - can provide access to this scribal culture. However, one of the problems of traditional palaeography is to determine writer identity when the writing style is near uniform. This is exemplified by the Great Isaiah Scro...
Conference Paper
Full-text available
Counting the number of fruits in an image is important for orchard management, but is complex due to different challenging problems such as overlapping fruits and the difficulty to create large labeled datasets. In this paper, we propose the use of a data-augmentation technique that creates novel images by adding a number of manually cropped fruits...
Chapter
Full-text available
Counting the number of fruits in an image is important for orchard management, but is complex due to different challenging problems such as overlapping fruits and the difficulty to create large labeled datasets. In this paper, we propose the use of a data-augmentation technique that creates novel images by adding a number of manually cropped fruits...
Conference Paper
In recent years, long short-term memory neural networks (LSTMs) followed by a connectionist temporal classification (CTC) have shown strength in solving handwritten text recognition problems. Such networks can handle not only sequence variability but also geometric variation by using a convolutional front end, at the input side. Although different...
Article
Full-text available
For performing multi-class classification, deep neural networks almost always employ a One-vs-All (OvA) classification scheme with as many output units as there are classes in a dataset. The problem of this approach is that each output unit requires a complex decision boundary to separate examples from one class from all other examples. In this pap...
Conference Paper
Full-text available
Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics. However, the required enormous amount of interactive and informative training data provides the major stumbling block for progress. In this study, we focus on accel- erating reinforcement learning (RL) training and...
Conference Paper
Full-text available
This short paper describes how MONK, a machine-learning driven handwriting recognition system, can be used to rapidly index a heterogeneous handwritten collection with the help of volunteers. We discuss the setup and results of an event which saw volunteers come together to enrich a subset of the digitized Prize paper collection, a collection of hi...
Preprint
Full-text available
Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-t...
Article
Writer identification 1 based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNe...
Preprint
Full-text available
Writer identification based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNet...
Preprint
Full-text available
ArXiv:2003.08771 Being aware of other traffic is a prerequisite for self-driving cars to operate in the real world. In this paper, we show how the intrinsic feature maps of an object detection CNN can be used to uniquely identify vehicles from a dash-cam feed. Feature maps of a pretrained `YOLO' network are used to create 700 deep integrated featu...
Preprint
Full-text available
Affordance detection is one of the challenging tasks in robotics because it must predict the grasp configuration for the object of interest in real-time to enable the robot to interact with the environment. In this paper, we present a new deep learning approach to detect object affordances for a given 3D object. The method trains a Convolutional Ne...
Preprint
Full-text available
Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics. However, the required enormous amount of interactive and informative training data provides the major stumbling block for progress. In this study, we focus on accelerating reinforcement learning (RL) training and i...
Article
Full-text available
Paleographers and philologists perform significant research in finding the dates of ancient manuscripts to understand the historical contexts. To estimate these dates, the traditional process of using classical paleography is subjective, tedious, and often time-consuming. An automatic system based on pattern recognition techniques that infers these...
Technical Report
Full-text available
This is a report about the KB use case of the Lorentz Workshop ICT with Industry (January 20-24, 2020), on the improvement of Optical Character Recognition (OCR) techniques for early modern texts which were printed (either fully or partially) in a variety of gothic scripts.
Preprint
Full-text available
This chapter provides an overview of the problems that need to be dealt with when constructing a lifelong-learning retrieval, recognition and indexing engine for large historical document collections in multiple scripts and languages, the Monk system. This application is highly variable over time, since the continuous labeling by end users changes...