New model learns content and transformation separately from data.
problem Learning disentangled representations from data without explicit labels.
method Group-based variational autoencoders, assuming content and transformation groups.
result Model learns generalizable content representations from unseen data.
New PCGML approach generates novel game content across multiple platformer domains.
problem Generating novel game content in new domains.
method Using a new affordance and path vocabulary, variational autoencoders trained on data from six platformer games produce new content with varying proportions of different domains.
result Captures latent level space spanning multiple domains and generates new content with varying proportions of different domains.
DEMUD-VIS detects novel image content and explains it visually.
problem Detecting and explaining novel image content in large datasets.
method Uses CNN for feature extraction, reconstruction error for novelty detection, and up-convolutional networks for image reconstruction.
result Demonstrates visual explanations of novel image content on diverse datasets.
Builds a novel educational recommender for lifelong learners.
problem Challenges in creating scalable and transparent models for lifelong learning.
method Integrative approach combining content novelty, background knowledge, and learner engagement.
result TrueLearn achieves promising performance while retaining a human interpretable learner model.
Method recombines image content and style from different images.
problem Recombining image content and style from different images.
method Constructs content embedding, uses VAE with leakage filtering to ensure separation of style and content.
result Synthesizes novel images with state-of-the-art performance on few-shot learning tasks.
This paper improves image retrieval accuracy through novel relevance feedback methods.
problem Improving image retrieval accuracy in Content-Based Image Retrieval (CBIR).
method Novel addition to feature re-weighting and classification techniques, focusing on 0-th iteration improvement.
result Significantly improved retrieval accuracy from relevance feedback.
Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visu…
Proposes a VAE variant for ordinal content factors.
problem Isolating ordinal-valued content factors in deep latent variable models.
method Introduces a partially ordered set (poset) structure and a conditional Gaussian spacing prior model.
result Significant improvements in content-style separation over previous non-ordinal approaches.
DCMIX learns channel importance for high content imaging.
problem Lack of channel importance information in deep learning-based image analysis.
method Image blending concepts with alpha compositing for arbitrary channels.
result DCMIX learns biologically relevant channel importance without sacrificing prediction performance.
A new method for image translation using disentangled style and content preservation.
problem Difficulty in maintaining original content during reverse diffusion in diffusion-based image translation.
method Disentangled style and content representation using intermediate keys from ViT model, CLIP loss, semantic divergence loss, and resampling strategy.
result Outperforms state-of-the-art models in text-guided and image-guided translation tasks.
Kernel Mean Matching enhances GANs for content-addressable generation.
problem Creating models that can generate images consistent with specified examples.
method Kernel Mean Matching applied to GANs.
result The method generates images consistent with specified input sets while maintaining original model quality.
RB-Modulation trains free diffusion models without external adapters.
problem Training-free personalization of diffusion models with style and content control.
method Stochastic optimal control with a style descriptor and cross-attention aggregation.
result Precise content and style extraction and control without external adapters.
FasterVoiceGrad speeds up VC by 6-7x with novel distillation.
problem Slow iterative sampling in diffusion-based VC models.
method Adversarial diffusion conversion distillation (ADCD) to create a faster one-step model.
result 6.6-6.9 and 1.8x faster on GPU and CPU, respectively.
A new method transcribes complex structured images like musical scores.
problem Transcribing content from images with complex internal structure.
method Hierarchical Spotlight Transcribing Network (STN) framework with two-stage approach.
result Demonstrated effectiveness through experiments on various structural image datasets.
RevGAN generates personalized reviews with given sentiment and style.
problem Creating reviews that are both personalized and of high quality.
method RevGAN combines self-attentive recursive autoencoders, conditional discriminators, and personalized decoders.
result RevGAN outperforms state-of-the-art models in sentence quality, coherence, and personalization.
A graph traversal algorithm for cold-start news recommendation using named entities.
problem Cold-start news recommendation for articles without user-specific information.
method Graph traversal algorithm and novel weighting scheme for named entities over a knowledge graph.
result Our method produces stronger Pearson correlation to human similarity scores than other cold-start methods.
Paper introduces new regression methods for consistent estimation of biophysical parameters.
problem Estimating biophysical parameters while respecting auxiliary variables.
method Linear and nonlinear kernel-based regression models with consistency constraints.
result Models provide closed-form solutions and successfully estimate chlorophyll content.
Paper ranks influential Tor Darknet onion domains using content features.
problem Measuring influence of criminal onion domains in Tor Darknet.
method Content-based features from multiple sources, Learning-to-Rank approach.
result Listwise approach outperforms other methods with NDCG of 0.95 for top-10.
LOLA uses LLMs to optimize content delivery, outperforming traditional methods.
problem Identifying the most engaging headlines for user engagement.
method LOLA integrates LLMs with adaptive experimentation to optimize content delivery.
result LOLA outperforms traditional methods in optimizing user engagement.
A new system recommends educational content based on student and peer interests.
problem Recommending suitable educational content for students.
method Neural Educational Recommendation Engine (NERE) using a recurrent neural network.
result NERE achieves higher recall and R^2 scores than traditional methods.
The paper explores how to find relevant vertices in one graph using another graph's attributes and structure.
problem Finding relevant vertices in one graph using another graph's attributes and structure.
method Theoretical and practical exploration of vertex nomination schemes that leverage both content (edge and vertex attributes) and context (network topology).
result Necessary and sufficient conditions for schemes that use both content and context to outperform those using only one.
Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, w…
A framework for flagging content with limited data.
problem Content flagging with scarce target-language data.
method Nearest-neighbor architecture using Transformer representations.
result Significant performance improvements over prior work.
Unified method for disentangling classes and content improves AI representation learning.
problem Current methods struggle with disentangling class and content variations.
method LORD: Latent Optimization for Representation Disentanglement with asymmetric noise regularization.
result LORD achieves superior disentanglement performance compared to existing methods.
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an…
Proposes a new approach to MSDA by introducing latent covariate shift to handle varying label distributions.
problem Challenges of conventional MSDA approaches in real-world settings where label distributions vary across domains.
method Introduces latent covariate shift (LCS) and a causal generative model with latent noises, latent content variable, and latent style variable.
result Identifies latent content variable up to block identifiability, enabling more nuanced label distribution recovery.
Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a…
Study shows online learning algorithms incentivize low-quality content, proposing new algorithms to improve quality.
problem Online learning algorithms in content recommender systems incentivize producers to create low-quality content.
method Analyzed the game between producers and content quality, designed new learning algorithms to incentivize high effort and quality.
result New algorithms incentivize producers to invest high effort and achieve high user welfare, improving content quality.
Proposes a model to optimize feedback for content creators on social media.
problem Nurturing content creation on social media platforms.
method Modeling feedback distribution to incentivize creators and optimize newsfeed experience.
result Optimized newsfeed for content creators without compromising user experience.
A method for disentangling text representations without supervision.
problem Challenges in learning disentangled representations of natural language.
method Information-theoretic guidance to induce independent style and content embeddings.
result High quality disentangled representations in terms of content and style preservation.
RALM extends exposure for long-tail contents in real-time recommender systems.
problem Lack of timely exposure for long-tail contents in recommender systems.
method Real-time attention-based look-alike model (RALM) with seeds-to-user similarity prediction and user representation learning.
result RALM outperforms traditional look-alike models in effectiveness and real-time performance.
Automated detection of new, interesting, unusual, or anomalous images within large data sets has great value for applications from surveillance (e.g., airport security) to science (observations that don't fit a given theory can lead to new discoveries). Many image data analysis systems are turning to convolutional neur…
This research predicts the popularity of new video contents using a hybrid machine learning approach.
problem Predicting the popularity of new video contents before they are published.
method Hybrid machine learning approach combining XGBoosting and deep neural nets, using metadata and categorical embedding techniques.
result Achieved better performance than standalone methods, validated on a dataset from a top streaming service.
Preserves content while changing style in text.
problem Text style transfer often fails to preserve content.
method Leverages linguistic information in structured supervisions.
result Significant improvement in content preservation and style transfer.
Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-booste…
Spotify improves content mix using contextual bandits.
problem Skewed historical data and varying user preferences across contexts.
method Contextual bandits to dynamically learn optimal content type distribution.
result Improved precision and user engagement with under-represented content types.
A new model considers fatigue in online content recommendation systems.
problem Fatigue in users due to overexposure and boredom from similar recommendations.
method Proposed a fatigue-aware Dependent Click Model (DCM) and two learning algorithms.
result Developed algorithms with regret bounds for learning content relevance and fatigue effects.
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
A new framework converts EEG signals between subjects and tasks.
problem Noise and variability in EEG data hinder generalizable signal extraction.
method Contrastive Split-Latent Permutation Autoencoder (CSLP-AE) framework.
result The CSLP-AE framework enables zero-shot conversion between unseen subjects.
New study shows personalized content recommendations can lead to polarization of user preferences.
problem Personalized content recommendations can alter user preferences, leading to polarization.
method Used a model of preference dynamics to explore how personalized content affects user preferences.
result Standard reward maximization algorithms achieve only constant regret in personalized recommendation environments.
A new approach uses deep learning to manage vehicular content efficiently.
problem Managing content replication and caching in vehicular networks efficiently.
method Data-driven, centralized approach using a Convolutional Neural Network (CNN).
result Effective strategies derived to modulate FC operation in space and adapt to mobility changes.
Study on heat content for submanifolds in sub-Riemannian geometry.
problem Understanding heat content for submanifolds in sub-Riemannian geometry.
method Existence of smooth tubular neighborhood, definition of relative heat content, approximation via smooth neighborhoods, asymptotic expansion analysis.
result Approximation of relative heat content fails to recover the exact expansion.
With rapid development of the Internet, web contents become huge. Most of the websites are publicly available, and anyone can access the contents from anywhere such as workplace, home and even schools. Nevertheless, not all the web contents are appropriate for all users, especially children. An example of these content…
The ability to characterize the color content of natural imagery is an important application of image processing. The pixel by pixel coloring of images may be viewed naturally as points in color space, and the inherent structure and distribution of these points affords a quantization, through clustering, of the color i…
Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previo…
This research investigates reliable local explanations for machine listening models.
problem Generating reliable local explanations for machine listening models.
method Investigates the sensitivity of SoundLIME explanations to input perturbations and proposes a novel method for identifying suitable content types.
result SoundLIME explanations are sensitive to the content in occluded input regions, and the average magnitude of input mel-spectrogram bins is the most suitable content type for temporal explanations.
Enhances content moderation with culturally-aware models.
problem Global content moderation policies miss local cultural nuances.
method Fine-tuning encoder-decoder models on media-diet data.
result Improved accuracy in local violation detection and cultural alignment.
This work improves disentanglement by preventing style variables from encoding content-related features.
problem Disentanglement of content and style in data representations using Variational Autoencoders.
method Adversarial training with mutual information minimization to prevent content information leakage in style representations.
result The method efficiently separates content and style related attributes and generalizes to unseen data.