In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: Visual representation is crucial for a visual tracking method’s performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. These descriptors were developed generically without considering tracking-specific information. In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders. The deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker. The proposed observational model retains old training samples to alleviate drift, and collect negative samples which are coherent with target’s motion pattern for better discriminative tracking. With the learned representation and online training samples, a logistic regression classifier is adopted to distinguish target from background, and retrained online to adapt to appearance changes. Subsequently, the observational model is integrated into a particle filter framework to peform visual tracking. Experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favourably against several state-of-the-art trackers.
[A]: Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle


[Q]: In this paper we present the greedy step averaging(GSA) method, a parameter-free stochastic optimization algorithm for a variety of machine learning problems. As a gradient-based optimization method, GSA makes use of the information from the minimizer of a single sample’s loss function, and takes average strategy to calculate reasonable learning rate sequence. While most existing gradient-based algorithms introduce an increasing number of hyper parameters or try to make a trade-off between computational cost and convergence rate, GSA avoids the manual tuning of learning rate and brings in no more hyper parameters or extra cost. We perform exhaustive numerical experiments for logistic and softmax regression to compare our method with the other state of the art ones on 16 datasets. Results show that GSA is robust on various scenarios.
[A]: Greedy Step Averaging: A parameter-free stochastic optimization method


[Q]: We consider an Erdős-Rényi graph with n nodes and edge probability q that is embedded with a random subgraph of size K with edge probabilities p such that p > q. We address the problem of detecting the subgraph nodes when only the graph edges are observed, along with some extra knowledge of a small fraction of subgraph nodes, called cued vertices or cues. We employ a local and distributed algorithm called belief propagation (BP). Recent works on subgraph detection without cues have shown that global maximum likelihood (ML) detection strictly outperforms BP in terms of asymptotic error rate, namely, there is a threshold condition that the subgraph parameters should satisfy below which BP fails in achieving asymptotically zero error, but ML succeeds. In contrast, we show that when the fraction of cues is strictly bounded away from zero, i.e., when there exists non-trivial side-information, BP achieves zero asymptotic error even below this threshold, thus approaching the performance of ML detection. Key-words: Belief Propagation, Subgraph Detection, Semisupervised Learning, Random Graphs ∗ Corresponding author, arun.kadavankandy@inria.fr La Detection de Sousgraphes en presence des indices grâce au Belief Propagation Résumé : Nous considérons un graphe Erdős-Rényi qui a n sommets dont q est la probabilité d’arrêtes. La dessus il y un sousgraphe placé sur leurs m sommets selectionnés aléatoirement et leur probabilité d’arrêtes est p, en sorte que p > q. Nous proposons un algorithme distribué aux calculs locales à chaque sommet, tiré du “Belief Propagation” (BP), qui détecte les sommets du sousgraphe, quand on connait une fraction de sommets du sousgraphe en tant qu’indices. Des recherches récentes ont prouvé que la prestation du BP dans l’absence des indices est strictement inférior par rapport à la detection globale du maximum de vraisemblance (DMV). A l’opposé, ici on prouve qu’en presence des indices, la prestation du BP est à l’hauteur de celle de DMV, dans la sens où le premier reussie à detecter la sousgraphe avec une erreur qui tend a zéro, à chaque fois le dernier peut le faire, dans la limite où le nombre de sommets du graph tend l’infinité. Mots-clés : Belief Propagation, Detection de Sousgraphes, Semisupervised Learning, Graphes Aléatoires Subgraph Detection with cues using Belief Propagation 3
[A]:
Subgraph Detection with cues using Belief Propagation