DeepKSPD: Learning to detect unusual motion patterns in videos – In a video game, the players have chosen various actions which could be useful in achieving their goals or in avoiding mistakes. Using a real-world video simulation as an example, we show that the player’s actions in games are not relevant for a certain goal: a robotic walking. We propose a novel method to learn the player’s action from the video frames and generate a novel motion vector representing the player’s state. Our method is flexible enough to model the player’s actions but requires the video to be captured by an entirely different camera, or only a set of clips. The proposed method is a novel method of learning a player’s action in a video game, by analyzing the camera sequences of the game and by leveraging the information obtained from the video frames captured by the player’s actions and from the video frames captured from different video frames.

This paper presents a probabilistic model for online learning with spatio-temporal information. The model proposes a learning algorithm that combines a novel learning algorithm with a temporal learning algorithm and a stochastic coupling between the sparse vector and the neighborhood lattice. This model does not require an extra parameter to obtain the posterior distribution, which makes solving it much easier. Our approach obtains both an efficient and competitive inference algorithm: (1) our algorithm is evaluated on synthetic data and (2) the algorithm is evaluated in real data with a non-parametric covariance matrix.

Nearest Local Average Post-Processing for Online Linear Learning

Axiomatic gradient for gradient-free non-convex models with an application to graph classification

# DeepKSPD: Learning to detect unusual motion patterns in videos

MorphNet: A Python-based Entity Disambiguation Toolkit

An efficient model with a stochastic coupling between the sparse vector and the neighborhood latticeThis paper presents a probabilistic model for online learning with spatio-temporal information. The model proposes a learning algorithm that combines a novel learning algorithm with a temporal learning algorithm and a stochastic coupling between the sparse vector and the neighborhood lattice. This model does not require an extra parameter to obtain the posterior distribution, which makes solving it much easier. Our approach obtains both an efficient and competitive inference algorithm: (1) our algorithm is evaluated on synthetic data and (2) the algorithm is evaluated in real data with a non-parametric covariance matrix.