Results 21 to 30 of about 10,035,302 (334)

Geographical Information Retrieval [PDF]

International Journal of Geographical Information Science, 2008
Geographical information is recorded in a wide variety of media and document types. There are innumerable paper‐based books, reports, images and maps, and there are computer databases and digital m...
Jones, Christopher B, Purves, Ross S
openaire   +6 more sources

Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering

Algorithms, 2023
Page stream segmentation (PSS) is the task of retrieving the boundaries that separate source documents given a consecutive stream of documents (for example, sequentially scanned PDF files).
Lukas Busch, Ruben van Heusden, Maarten Marx   +2 more
doaj   +1 more source

UCN-YOLOv5: Traffic Sign Object Detection Algorithm Based on Deep Learning

IEEE Access, 2023
Traffic sign detection plays an important role in traffic safety and traffic management. In view of the complex and changeable environment and detection accuracy of traffic sign detection, this paper proposes UCN-YOLOv5 model based on the framework of ...
Peilin Liu, Zhaoyang Xie, Taijun Li
doaj   +1 more source

Causal Factor Disentanglement for Few-Shot Domain Adaptation in Video Prediction

Entropy, 2023
An important challenge in machine learning is performing with accuracy when few training samples are available from the target distribution. If a large number of training samples from a related distribution are available, transfer learning can be used to
Nathan Cornille, Katrien Laenen, Jingyuan Sun, Marie-Francine Moens   +3 more
doaj   +1 more source

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

Complexity, 2023
Many real-world systems of various origins are capable of self-organization to the edge of a phase transition, characterized by avalanche-like behavior. Therefore, it is important, by observing the behavior of early warning measures for dynamical series ...
Andrey Dmitriev, Andrey Lebedev, Vasily Kornilov, Victor Dmitriev   +3 more
doaj   +1 more source

Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. It aims to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-stage ranking architecture.
Jimmy J. Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, Rodrigo Nogueira, D. Cheriton   +6 more
semanticscholar   +1 more source

Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
Electroencephalography-to-Text generation (EEG-to-Text), which aims to directly generate natural text from EEG signals has drawn increasing attention in recent years due to the enormous potential for Brain-computer interfaces.
Xiachong Feng, Xiaocheng Feng, Bing Qin, Ting Liu   +3 more
doaj   +1 more source

Effective precursors for self-organization of complex systems into a critical state based on dynamic series data

Frontiers in Physics, 2023
Many different precursors are known, but not all of which are effective, i.e., giving enough time to take preventive measures and with a minimum number of false early warning signals.
Andrey Dmitriev, Andrey Dmitriev, Andrey Lebedev, Vasily Kornilov, Victor Dmitriev   +4 more
doaj   +1 more source

Declarative Experimentation in Information Retrieval using PyTerrier [PDF]

International Conference on the Theory of Information Retrieval, 2020
The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representations of deep neural network architectures.
Craig Macdonald, N. Tonellotto
semanticscholar   +1 more source

In-Context Retrieval-Augmented Language Models [PDF]

Transactions of the Association for Computational Linguistics, 2023
Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significantly improve language modeling performance. In addition, they can mitigate
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, A. Shashua, Kevin Leyton-Brown, Y. Shoham   +6 more
semanticscholar   +1 more source