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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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2356 · May 201919922001200920182026
48 results for movie synopses

This study compares feature extraction methods using Neural Networks and Latent Dirichlet Allocation for movie synopses.

problem Extracting meaningful features from movie synopses for pattern detection and recommendation.
method Employed Latent Dirichlet Allocation for topic modeling and Neural Networks for distributed paragraph representations.
result Latent Dirichlet Allocation can provide meaningful features for movie synopses, comparable to Neural Networks.

Paper classifies movie genres using multimodal data.

problem Challenging task of multi-label movie genre classification.
method Created dataset from video clips, subtitles, synopses, and posters. Extracted features using various descriptors. Evaluated using different classifiers and late fusion strategy.
result Best F-Score result of 0.628 achieved by combining LSTM on synopses and CNN on movie trailer frames.

We present a grid diagram analogue of Carter, Rieger and Saito's smooth movie theorem. Specifically, we give definitions for grid movies, grid movie isotopies and present a definition of grid planar isotopy as a particular subset of the grid diagram moves: stabilization, destabilization and commutation. We show that gr…

2013-03-07abs ↗pdf ↗

A movie multilayer network model captures narration from script, subtitles, and content.

problem Discovering content and stories in movies using network models.
method Developed a multilayer network model using visual and textual semantic cues.
result Demonstrated the effectiveness of the model on the Star Wars saga.

A shadow diagram is a knot diagram with under-over information omitted; a shadow movie is a sequence of shadow diagrams related by shadow Reidemeister moves. We show that not every shadow movie arises as the shadow of a Reidemeister movie, meaning a sequence of classical knot diagrams related by classical Reidemeister …

2011-06-17abs ↗pdf ↗

Machine learning predicts movie genres from summaries with high accuracy.

problem Predicting movie genres from plot summaries.
method Used Naive Bayes, Word2Vec+XGBoost, Recurrent Neural Networks, and Gated Recurrent Units (GRU) for text classification and multi-label problem.
result GRU neural networks achieve the best result with a Jaccard Index of 50.0%, F-score of 0.56, and hit rate of 80.5%.

Two singular links are cobordant if one can be obtained from the other by singular link isotopy together with a combination of births or deaths of simple unknotted curves, and saddle point transformations. A movie description of a singular link cobordism in 4-space is a sequence of singular link diagrams obtained from …

2015-07-15abs ↗pdf ↗

We present a marked analogue of Carter and Saito's movie theorem. Our definition of marking was chosen to coincide with the markings that arise in link Floer homology. In order to deal with complications arising from certain isotopies, we define three equivalence relations for marked surfaces and work over an equivalen…

2014-07-17abs ↗pdf ↗

Improved video and movie description using multitask learning.

problem Lack of training data and poor generalization in video captioning.
method Multitask learning encoder-decoder framework for video sequences.
result Improved performance on multi-caption and single-caption datasets.

Study analyzes IMDB movie comments and Twitter data using machine learning and vector space techniques.

problem Sentiment analysis of IMDB movie comments and Twitter data.
method Created a vector space in KNIME Analytics platform, used Decision Trees, Naïve Bayes, and SVM algorithms for classification.
result SVM algorithm provided the best classification results for both IMDB movie comments and Twitter data sets.

The paper compares and improves aggregators for relational probabilistic models.

problem Predicting gender from movie ratings is challenging due to varying numbers of movies per user and users per movie.
method The paper compares existing aggregators and proposes new ones, showing their empirical superiority.
result New aggregators and model modifications outperform existing ones.

Solves the film scheduling and staggered showtimes problem for movie theaters.

problem Maximize attendance and revenue by scheduling films with staggered showtimes.
method Binary integer linear optimization to find optimal schedules for each cluster of neighboring locations.
result Optimal scheduling cannot be done for all locations at once, but must be done for each cluster.

Adapting momentum from optimization to reinforcement learning.

problem Improving the convergence and stability of reinforcement learning algorithms.
method Introducing Momentum Value Iteration (MoVI) by incorporating an average of consecutive state-action value functions, inspired by the concept of momentum in optimization.
result MoVI improves the convergence and stability of reinforcement learning algorithms, as demonstrated by experiments on Atari games.

The paper proposes a new method for user-movie recommendation systems.

problem Improving recommendation accuracy in collaborative filtering.
method Uses Empirical Bayes with Reversible Jump Markov Chain in a Bayesian setup.
result Demonstrates improved hyper-parameter tuning and recommendation accuracy.

Predict missing movie ratings or graph embeddings with low rank matrices.

problem Predicting missing entries in a ratings matrix or graph embeddings with known linear relations.
method Low rank matrix completion approach applied to graph embeddings.
result Effective methods for predicting missing entries in matrices and graph embeddings.

New procedures connect braid charts, triplane diagrams, and braid movies for knotted surfaces.

problem Understanding the braid index and bridge index of knotted surfaces in 4D.
method Introducing rainbow diagrams and new procedures for passing among triplane diagrams, braid movies, and braid charts.
result Inequalities relating braid index and bridge index of 2-knots are obtained.

Improved UCB and Thompson Sampling policies for CMAB with probabilistically triggered arms achieve bounded regret.

problem Combinatorial multi-armed bandit problem with probabilistically triggered arms.
method Upper Confidence Bound (UCB) policies and Combinatorial Thompson Sampling (CTS).
result CUCB-κκ and CTS achieve O(T)O(\sqrt{T}) gap-independent regret.

Study shows pruning datasets can improve machine learning model performance.

problem Improving machine learning model performance through dataset pruning.
method Comparison of different algorithms on unpruned and iteratively pruned datasets.
result Algorithms that perform better on unpruned datasets also perform better on pruned datasets.

The paper extends surface link coloring theory to triplane diagrams and knots.

problem Understanding the topological properties of knots and surfaces in 4-space.
method Translated Niebrzydowski's theory of region colorings to triplane diagrams and movies of knots, providing inequalities and applications.
result Yoshikawa's 2-knots 919_1 and 10210_2 are non-invertible.

MOVI learns optimal vehicle dispatch policies without models, reducing unserviced requests.

problem Optimizing vehicle dispatch to minimize passenger wait times in dynamic fleets.
method Model-free approach using Deep Q-network (DQN) for decentralized learning and centralized receding-horizon control comparison.
result DQN dispatch policy reduces unserviced requests by 76% compared to no dispatch and 20% compared to RHC.

Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distri…

2012-02-14abs ↗pdf ↗

New ROC tools assess predictive abilities for any linearly ordered outcomes.

problem Fundamental restriction in ROC analysis for non-dichotomous outcomes.
method ROC movies and UROC curves for linearly ordered outcomes.
result CPA equals AUC for binary outcomes and relates to Spearman's coefficient for pairwise distinct outcomes.

This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track 2) for context-aware movie recommendation systems. The train dataset comprises 4,536,891 ratings provided by 171,670 users on 23,974$ movies, as well as the household groupings of a subset of the users. The test dataset comprises 5,450 rating…

2012-07-26abs ↗pdf ↗

ANT learns sparse embeddings for large vocabularies efficiently.

problem Lack of scalable methods for embedding large vocabularies in neural networks.
method Anchor & Transform (ANT) algorithm that learns a small set of anchor embeddings and a sparse transformation matrix.
result ANT achieves stronger performance with fewer parameters (up to 40x compression) compared to existing methods.

Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current ap…

2015-09-30abs ↗pdf ↗