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Recurrent Neural Network, Long Short-Term Memory, and Transformer have made great progress in predicting the trajectories of moving objects. Although the trajectory element with the surrounding scene features has been merged to improve…

Computer Vision and Pattern Recognition · Computer Science 2023-04-18 Wendong Zhang , Qingjie Chai , Quanqi Zhang , Chengwei Wu

Hyperdimensional Computing affords simple, yet powerful operations to create long Hyperdimensional Vectors (hypervectors) that can efficiently encode information, be used for learning, and are dynamic enough to be modified on the fly. In…

Symbolic Computation · Computer Science 2022-06-01 Peter Sutor , Dehao Yuan , Douglas Summers-Stay , Cornelia Fermuller , Yiannis Aloimonos

We present a static framework for analysing quantum routing protocols that we call the \textit{cost-vector formalism}. Here, quantum networks are recast as multi-graphs where edges represent two-qubit entanglement resources that…

Quantum Physics · Physics 2025-11-04 Hudson Leone , Nathaniel R. Miller , Deepesh Singh , Nathan K. Langford , Peter P. Rohde

In the first place, a novel, yet straightforward in-place integer value-sorting algorithm is presented. It sorts in linear time using constant amount of additional memory for storing counters and indices beside the input array. The…

Data Structures and Algorithms · Computer Science 2013-07-11 A. Emre Cetin

In this thesis we present a new algorithm for the Vehicle Routing Problem called the Enhanced Bees Algorithm. It is adapted from a fairly recent algorithm, the Bees Algorithm, which was developed for continuous optimisation problems. We…

Neural and Evolutionary Computing · Computer Science 2016-05-19 Aish Fenton

A central problem in data streams is to characterize which functions of an underlying frequency vector can be approximated efficiently. Recently there has been considerable effort in extending this problem to that of estimating functions of…

Data Structures and Algorithms · Computer Science 2018-10-25 Vladimir Braverman , Stephen R. Chestnut , Robert Krauthgamer , Yi Li , David P. Woodruff , Lin F. Yang

We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded…

Computation and Language · Computer Science 2015-06-23 Ryan Kiros , Yukun Zhu , Ruslan Salakhutdinov , Richard S. Zemel , Antonio Torralba , Raquel Urtasun , Sanja Fidler

The Relevance Vector Machine (RVM) is a recently developed machine learning framework capable of building simple models from large sets of candidate features. Here, we describe a protocol for using the RVM to explore very large numbers of…

Genomics · Quantitative Biology 2007-05-23 Thomas A. Down , Tim J. P. Hubbard

We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a…

Computation and Language · Computer Science 2017-07-28 Zhe Gan , Yunchen Pu , Ricardo Henao , Chunyuan Li , Xiaodong He , Lawrence Carin

In this paper, we study representation learning for multi-task decision-making in non-stationary environments. We consider the framework of sequential linear bandits, where the agent performs a series of tasks drawn from distinct sets…

Machine Learning · Computer Science 2022-04-19 Yuzhen Qin , Tommaso Menara , Samet Oymak , ShiNung Ching , Fabio Pasqualetti

Graphs are a natural representation of data from various contexts, such as social connections, the web, road networks, and many more. In the last decades, many of these networks have become enormous, requiring efficient algorithms to cut…

Data Structures and Algorithms · Computer Science 2021-08-11 Alexander Noe

Sequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture for combining…

Computation and Language · Computer Science 2016-11-15 Marek Rei , Gamal K. O. Crichton , Sampo Pyysalo

The super point, a host which communicates with lots of others, is a kind of special hosts gotten great focus. Mining super point at the edge of a network is the foundation of many network research fields. In this paper, we proposed the…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-01-29 Jie Xu , Wei Ding , Xiaoyan Hu

Neural networks mapping sequences to sequences (seq2seq) lead to significant progress in machine translation and speech recognition. Their traditional architecture includes two recurrent networks (RNs) followed by a linear predictor. In…

Machine Learning · Computer Science 2021-06-29 Boris Rubinstein

We propose a new regression algorithm that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the output variable exhibits a heterogeneous behavior across…

Machine Learning · Computer Science 2026-02-17 Ş. İlker Birbil , Sinan Yıldırım , Samet Çopur , M. Hakan Akyüz

This document describes an algorithm to scale a complex vector by the reciprocal of a complex value. The algorithm computes the reciprocal of the complex value and then scales the vector by the reciprocal. Some scaling may be necessary due…

Numerical Analysis · Mathematics 2023-11-13 Weslley da Silva Pereira

Spurred by the growth of transportation network companies and increasing data capabilities, vehicle routing and ride-matching algorithms can improve the efficiency of private transportation services. However, existing routing solutions do…

Systems and Control · Computer Science 2018-10-25 Ian Schneider , Jun Jie Joseph Kuan , Mardavij Roozbehani , Munther Dahleh

Curriculum learning has been growing in the domain of reinforcement learning as a method of improving training efficiency for various tasks. It involves modifying the difficulty (lessons) of the environment as the agent learns, in order to…

Machine Learning · Computer Science 2024-05-28 Avidan Shah , Danny Tran , Yuhan Tang

Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, because it requires approximate inference. We propose an…

Artificial Intelligence · Computer Science 2012-06-18 Daniel Lowd , Pedro Domingos

We suggest a method for routing when the source does not posses full information about the shortest path to the destination. The method is particularly useful for scale-free networks, and exploits its unique characteristics. By assigning…

Networking and Internet Architecture · Computer Science 2007-05-23 Shai Carmi , Reuven Cohen , Danny Dolev
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