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Extracting meaningful features from sequences and devising effective similarity measures are vital for sequence data mining tasks, particularly sequence classification. While neural network models are commonly used to automatically learn sequence ...

Insects are really astonishing creatures if their learning and adaptation capabilities are considered. In this paper two specific characteristics of their tiny brain are taken into account: classification and sequence learning. A complex neural ...

Sequence learning and prediction are considered principle computations performed by biological brains. Machine learning algorithms solve this type of task, but they require large amounts of training data and a substantial energy budget. An approach to ...

The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation ...

Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In practice, temporal sets prediction is much more complex than predictive ...

Depression is the silent killer of the new age. Although depression is considered a mental illness it can affect physical health. Most often people ignore it until physical or acute mental symptoms start showing up. One way to early detect symptoms of ...

One of the major challenges in machine learning nowadays is to provide predictions with not only high accuracy but also user-friendly explanations. Although in recent years we have witnessed increasingly popular use of deep neural networks for sequence ...

Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges ...

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional ...

Recent developments in computer vision and the emergence of wearable sensors have opened opportunities for the development of advanced and sophisticated techniques to enable multi-modal user assessment and personalized training which is important in ...

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds ...

As more industries adopt the use of robots to increase productivity, there is an increased need for effective human-robot interaction training, especially in the case of heavy and high precision robots. This implies the need for easy assessment methods ...

We aim to form stable representations of temporal sequences with key focus on semantic learning and streaming data. The state of the art in the Hierarchical Temporal Memory is represented by Numenta’s recently published “ColumnPooler” which ...

We focus on hashing videos into short binary codes for efficient Content-based Video Retrieval (CBVR), which is a fundamental technique that supports access to the ever-growing abundance of videos on the Web. Existing video hash functions are built on ...

Reinforcement learning (RL) is an algorithmic theory for learning by experience optimal action control. Two widely discussed problems within this field are the temporal credit assignment problem and the transfer of experience. The temporal credit ...

In this paper, we introduce a new problem: automatically capturing programming content in online discussions. We expect solving this problem helps enhance visual presentation of programming forum content, qualitative analysis of forum contributions, and ...

Analyzing logical structures of texts is important to understanding natural language, especially in the legal domain, where legal texts have their own specific characteristics. Recognizing logical structures in legal texts does not only help people in ...

As an important aspect in video content analysis, event detection is still an open problem. In particular, the study on detecting interactive events in crowd scenes is still limited. In this paper, we investigate detecting interactive events between ...

Insider threats are veritable needles within the haystack. Their occurrence is rare and when they do occur, are usually masked well within normal operation. The detection of these threats requires identifying these rare anomalous needles in a ...

In this paper we present a methodology based on combining sequence learning and case-based reasoning. This methodology has been applied in the analysis, mining and recognition of sequential data provided by complex systems with the aim of anticipating ...